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# Algorithm Development - Science topic

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Fragmentation trees are a critical step towards elucidation of compounds from mass spectra by enabling high confidence de novo identification of molecular formulas of unknown compounds (doi:10.1186/s13321-016-0116-8). Unfortunately, those algorithms suffer from long computation times making analysis of large datasets intractable. Recently, however, Fertin et al. (doi:10.1016/j.tcs.2020.11.021) highlighted additional properties of fragmentation graphs which could reduce computational times. Since their work is purely theoretical and lacks an implementation, I'm looking to partner up with someone to investigate and implement faster fragmentation tree algorithms. Could end up being a nice paper. Anyone interested?
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I have a new idea (by a combination of a well-known SDP formulation and a randomized procedure) to introduce an approximation algorithm for the vertex cover problem (VCP) with a performance ratio of $2 - \epsilon$.
You can see the abstract of the idea in attached file and the last version of the paper in https://vixra.org/abs/2107.0045
I am grateful if anyone can give me informative suggestions.
Dear Majid Zohrehbandian,
I suggest you see the links on the topic.
Best regards
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A tunable clock source will consist of a PLL circuit like the Si5319, configured by a microcontroller. The input frequency is fixed, e.g. 100 MHz. The user selects an output frequency with a resolution of, say, 1 Hz. The output frequency will always be lower than the input frequency.
The problem: The two registers of the PLL circuit which determine the ratio "output frequency/input frequency" are only 23 bit wide, i.e. the upper limit of both numerator and denominator is 8,388,607. As a consequence, when the user sets the frequency to x, the rational number x/108 has to be reduced or approximated.
If the greatest common divider (GCD) of x and 108 >= 12 then the solution is obvious. If not, the task is to find the element in the Farey sequence F8388607 that is closest to x/108. This can be done by descending from the root along the left half of the Stern-Brocot tree. However, this tree, with all elements beyond F8388607 pruned away, is far from balanced, resulting in a maximum number of descending steps in excess of 4 million; no problem on a desktop computer but a bit slow on an ordinary microcontroller.
F8388607 has about 21*1012 elements, so a balanced binary tree with these elements as leaves would have a depth of about 45. But since such a tree cannot be stored in the memory of a microcontroller, numerator and denominator of the searched Farey element have to be calculated somehow during the descent. This task is basically simple in the Stern-Brocot tree but I don't know of any solution in any other tree.
Do you know of a fast algorithm for this problem, maybe working along entirely different lines?
Many thanks in advance for any suggestions!
Now I tested the idea of jumping downwards in the Stern-Brocot tree. This idea is based on the observation that long paths from the root to a Farey element always seem to contain long sequences of steps directed exclusively to the left or exclusively to the right. As long as the direction is constant, the values of numerator and denominator which are added to the current node are also constant, obviously. Therefore, the products of numerator and a certain jump width resp. denominator and a this jump width can be added to the node.
In order to determine the largest possible jump width, bitwise successive approximation is used in this first approach. The result is quite satisfactory:
With an input frequency of 100 MHz, and the output frequency in the range 1 Hz to 100 MHz - 1 Hz (at the extrema, the approximation by Farey elements is poor, of course), the sum of the passes through the outer loop (movement through the tree) and through the inner loop (determining the maximum jump width) never exceeds 386. Attached is my C source. Compared to the maximum number of single steps, this is an improvement of 4 orders of magnitude.
While this approach solves my practical problem, I would still be interested in other solutions because sometimes it's amazing how a problem can be tackled in different ways.
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Hello,
I am a research assistant on hydrology and fluid dynamics, with a background in Civil Engineering, Currently, our research team have been working on evaluating Data Assimilation techniques for rainfall-runoff predictions.
I would like to know if you have suggestions of studies or books about data assimilation especially focused on the applications in hydrology and hydraulics. Are there any similar studies to recommend? Does anyone have a solved numerical example of rainfall-runoff predictions by means of data assimilation techniques?
You might look at some of the papers by Dr. Dong-Jun Seo. See:
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I have a 1489 spike protein sequence file. I want to extract codon sequences, of 6 amino acids from this with their respective header. I don't know any sort of programming, so can anyone help me with this?
A big thank you in advance.......
These tutorials might be helpful, have a look:
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AI is the intelligence exhibited by computers and software representing the third era of automation. AI is currently used in robotics, machine learning, analytics, decision-support, and virtual personal assistance.  AI is expected to transform many industries all over the world. The success of companies is increasingly defined by how they manage the integration  between human workforce and automation.   Businesses need to benefit from new AI technologies and systems to enhance productivity and increase human intelligence.  However, AI is not  seen by all as a tool that complement rather than replace manpower.  Many humans fear that smart robots will substitute their human counterparts in the labor force.   What is your opinion on the role of AI in job enhancement and boosting of revenue?
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I have in mind, that Logic is mainly about thinking, abstract thinking, particularly reasoning. Reasoning is a process, structured by steps, when one conclusion usually is based on a previous one, and at the same time it can be the base, the foundation of further conclusions. Despite the mostly intuitive character of the algorithm as a concept (even not taking into account Turing and Markov theories/machines), it has a step by step structure, and they are connected, even one would say that logically connected (when they are correct algorithms). The different is, of course, the formal character of the logical proof.
Teachable neural networks are currently the main direction in the development of artificial intelligence (AI). These systems belong to the category of inductive anthropomorphic, which is associated with their well-known advantages, disadvantages and areas of application. It is generally accepted that anthropomorphism is the exclusive factor in the advantage of AI, however, there are actual areas of AI application, in which the use of deductive systems is more effective, which use less data and computational resources, but are capable of learning. Examples of such systems: knowledge generators, expert systems, active marketing systems, and many others.
The basis of AI deductive imitation systems are extremely general models and algorithms that imitate the objects (objects) under consideration. The generality limit is determined both by the amount of knowledge about the object and by the calculated capabilities. General models (metamodels) are usually formed in the form of systems of equations that link together the variables that describe the object under consideration, as well as parameters that determine the form of partial equations formed when transforming the equations of the general model. The solution of such systems of equations is carried out by methods of global optimization: stochastic (Monte Carlo methods) or heuristic (evolutionary algorithms and others). Simulation AI presupposes the solution of two basic problems: inverse (implementing the system training options) - the formation of partial equations (models) according to the values ​​of certain variable characteristics of objects; direct - definitions of values ​​of variables for particular configurations of models. The use of simulation metamodels in combination with the randomization of variables and parameters provides high flexibility and adaptability of intelligent systems.
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Hi,
I have a graph as in attached figure. I have to extract connected nodes from graph based on edge weights. If edge weight is less than a certain threshold, we have to consider that there will be not be any connectivity between that nodes. I have attached expected subgraph with this mail. Is there any efficient algorithms available to extract these type of nodes? In this attached sub graphs, all nodes are grouped if the edge weights are above 1
I posted a presentation about Eulerian paths. Take a look at it.
(PDF) Topology and Structure of Directed Graphs - DNA Sequencing
Another presentation about "Seven Bridges of Königsberg" is in the making.
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What kind of software or what kind of method could be used to manage the huge amount of paper, so that you could find whatever paper you have read fast.
I suggest Mendeley and Endnote. But Mendeley is user friendly software.
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Hi,
If I have some nodes coordinates and I have to group these nodes together if there is path exist between them. Is there any algorithm available?
The main graph structure looks as in attached figure.
By looking at the main graph,
If I have co-ordinates of nodes 4,5,6,7 and 3 in the graph generated using above code. I have to group 4,5, and 6 together and 3 and 7 as another group. Is there any algorithm available for this kind of grouping?
Manu
Peter Breuer : Sorry for the confusing description. You are right. I was talking about SUBGRAPHS. Your explanation was really helpful
Thanks and Regards,
Manu
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Recently, I have seen in many papers reviewers are asking to provide computation complexities for the proposed algorithms. I was wondering what would be the formal way to do that, especially for the short papers where pages are limited. Please share your expertise regarding the computational complexities of algorithms in short papers.
You have to explain time and space complexity of your algorithm
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I would like to compare my algorithm (the improved LPA) with Louvain, infomap and CNM (fast greedy) algorithms (available in Mat lab toolbox community) that has been implemented on LFR dataset.
I confronted with a problem when I cannot use the outputs of the algorithms for NMI criteria.
I will gratitude everyone could guide me about the matter!
please provide me the complete solution of your problem . i am also stuck solving the same problem.
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I have a module set up in ns3 and an algorithm developed from MATLAB. How can i insert this algorithm in ns3? Could anyone suggest me some hint?
Regards,
Shafagat
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I need a Data Sets about Container Stacking Problem for experimentation with differents algorithms
I have the same problem
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Any new innovations other than First-Come, First-Served (FCFS) / Shortest-Job-First (SJF)/ Round Robin (RR) or mix-development of those?
A Process Scheduler is a program that distributes different tasks to the CPU based on different scheduling algorithms. Process scheduling can be done in six different ways.
There are three more algorithms besides First-Come, First-Served (FCFS) / Shortest-Job-First (SJF) / Round Robin (RR) or mix-development,
• Priority Scheduling
• Shortest Remaining Time
• Multiple-Level Queues Scheduling
Priority Scheduling
Priority scheduling is a non-preemptive scheduling approach that is commonly employed in batch systems. Each process is assigned a priority. The most important procedure will be completed first, followed by the next most important step, and so on. Processes with the same priority are executed on a first-come, first-served basis. Memory requirements, time restrictions, or any other resource constraint can all influence prioritization.
Shortest Remaining Time
The SJN algorithm's preemptive version is the least remaining time (SRT).
The processor is allocated to the task that is closest to completion, but it may be surpassed by a newer ready project with a lower completion time. It is impossible to implement in interactive systems when the required CPU time is uncertain. It's commonly utilized in batch settings when short operations need to be prioritized.
Multiple-Level Queues Scheduling
Multiple-level queues aren't a viable scheduling option on their own. Using existing algorithms, they aggregate and schedule work of comparable quality. Each queue, for example, can have its own scheduling algorithms and be assigned a priority.
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I am interested in global convergence and application of that algorithm to different area.
Any help is highly appreciated
Interior point methods (https://en.wikipedia.org/wiki/Interior-point_method) are generally superior to active-set methods (https://en.wikipedia.org/wiki/Active-set_method) for solving large-scale problems.
You usually end up solving a convexified equality-constrained problem, that is a system of nonlinear equations. That's the most favorable case.
Among active-set methods, SLQP (https://en.wikipedia.org/wiki/Sequential_linear-quadratic_programming) is of particular interest. It also forms an equality-constrained quadratic problem (the favorable case) after it approximated the set of active inequality constraints by solving a linear problem.
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From Simulink subsystem after generating C code using Embedded coder wanted to find the number of lines the code will be executed considering the worst path ( lengthy path it the code can follow. Any algorithm or methods references available which can be used for the purpose ?
for e.g. :
1. If (a>b)
2. x=1;
3. y=2;
4 z=3;
5. else
6. a=4;
in this case possible maximum length is 1-->2-->3--4 .
maximum number of executed lines will be equal to = 4 in this case , considering if condition true .
If else condition get executed since number of lines under else condition is one total number of executed lines will be equal to 3
So the algorithm should return maximum number =4
Hello,
Kind regards
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Hello all, is there any MATLAB code for Adaptive Data Rate for LoRaWAN in terms of secure communication?
Adnan Majeed Thank you for your reply, but the problem still remains. I need an algorithm or MATLAB code related to ADR for LoRaWAN. If you have such details then please share that here. Thank you.
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There is an idea to design a new algorithm for the purpose of improving the results of software operations in the fields of communications, computers, biomedical, machine learning, renewable energy, signal and image processing, and others.
So what are the most important ways to test the performance of smart optimization algorithms in general?
I'm not keen on calling anything "smart". Any method will fail under some circumstances, such as for some outlier that no-one have thought of.
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Its going to be a huge shift for marketers, tracking identity is tricky at the best of times with online/offline and multiple channels of engagement - but when the current methods of targeting, measurement and attribution get disrupted, its going to be extremely difficult to get identity right to deliver exceptional customer experiences whilst getting compliance right.
We have put our framework and initial results show promising measurement techniques including Advanced Neo-classical fusion models (borrowed from Financial industry, Biochemical Stochastic & Deterministic frameworks) and applied Bayesian and Space models to run the optimisations. Initial results are looking very good and happy to share our wider thinking thru this work with everyone.
Please suggest how would you be handling this environmental change and suggest methods to measure digital landscape going forward.
#datascience #analytics #machinelearning #artificialintelligence #reinforcementlearning #cookieless #measurementsolutions #digital #digitaltransfromation #algorithms #econometrics #MMM #AI #mediastrategy #marketinganalytics #retargeting #audiencetargeting #cmo
Here are a few ideas about how marketers can do this:
• Encourage site login by better authenticated experiences or other consumer-oriented rewards to increase the number of persistent IDs.
• Create a holistic customer view by combining customer and other owned first-party data (e.g., web data) and establishing a persistent cross-channel customer ID.
• Allow customer segmentation, targeting, and measurement across all organizations and platforms. Measurement and audience control can be supported by integrating martech and ad tech pipes wherever possible.
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I need a dermoscopic image database to test an algorithm for automatic diagnose. In particular I am interested in images with blue-black colors within the lesion.
Can anyone tell me where to find it?
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For Example, I have a South Carolina map comprising of 5833 grid points as shown below in the picture. How do I interpolate to get data for the unsampled points which are not present in 5833 points but within the South Carolina(red region in the picture) region? Which interpolation technique is best for a South Carolina region of 5833 grid points?
Dear Vishnu,
in which format is the data, which you would like to interpolate, available: NetCDF, ASCII-Text, Excel, ... ?
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I have located the MECP using the algorithm developed by Harvey. Since the MECP is not a stationary point in the full 3N-6 dimensions of either of the PESs standard frequency calculation would be erroneous. Can anyone suggest how to perform frequency calculation at MECP?
I have the same problem. Have you known the answer to this question?
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Hi
I have a large time series with two variables ( In reality more dependent variable, but now focusing on only two) in which variable2 is dependent on variable1. Algorithm need to identify whenever variable2 differ from its ideal path in dependent to variable1. Would like to know any efficient machine learning method suited for this application ( Which can be adapted in future for multiple variable ) in Matalb .
Simple plotting and visually inspecting is laborious because of the large data set.
You can apply RNN and LSTM, these deep learning tools are available in Matlab 2018 (or onward) .
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It's no longer a surprise to realize a wide gap between advances in academic research and practicality in Industry. This discussion is about exploring this gap for a particular domain, which is Time-Series Forecast. The topic has had great many research advances in recent years, since researchers have identified promises offered by Deep Learning (DL) architectures for this domain. Thus, as evident in recent research gatherings, researchers are racing to perfect the DL architectures for taking over the time-series forecast problems. Nevertheless, the average industry practitioner remains reliant on traditional statistical methods for understandable reasons. Probably the biggest reason of all is the ease of interpretation (i.e. interpretability) offered by traditional methods, but many other reasons are valid as well, such as: ease of training, deployment, robustness, etc. The question is: If we were to reinvent a machine learning solution solely for industrial applicability, considering the current and future industry needs, then what attributes should this solution possess? Interpretability, Manipulability, Robustness, Self-maintainability, Inferability, online-updatability, something else?
From my personal experience, Deep Learning (DL) offers NO substantial improvement in time series forecasting accuracy vs. traditional methods, such as Holt-Winters, exponential and double exponential smoothing with or without trends, Holt-Winters with seasonality and trends, autoregressive integrated moving average (ARIMA), digital filtering, etc.
Regardless of the concrete technique (including deep learning and recursive neural networks in AI), all time series forecasting methods are based on the use of the historical past data to extrapolate them into the future. The fundamental assumption of any forecasting technique (implicit or explicit) is that time series represents a stable pattern that can be identified and then extended into the future. If a pattern of the past data-points is not statistically stable, then no meaningful future prediction (forecasting) is possible regardless of the sophistication of the forecasting technique (DL or not).
It seems reasonable to assume that too ‘old’ data-points do not practically affect (correlated to) the most recent data-points, let alone the future data. The data-points that are strongly correlated to the newer ones can be used for making the forecast. The data-points that are weakly correlated to the newer ones (or not correlated at all) should not be included for forecasting; otherwise, the forecast will likely be skewed. Thus, the use of a large number of the available past data-points worth of several years of observations is not needed for making a meaningful forecast; in fact, the large number of the past data-points can be detrimental to producing a reasonably accurate forecast regardless of the used technique (deep learning or not). The problem of the optimal number of the past data points (training set size) of a time series should be the primary focus for industrial applicability of the forecasting rather than a forecasting technique (DL or not).
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Suppose we have the following single nucleotide polymorphic variant:
Reference DNA sequence
S0 = G T G A C T G A G C C T
Variant DNA sequences
S1 = A T G A C T G A G C C T
S2 = G A G A C T G A G C C T
S3 = G T A A C T G A G C C T
S4 = G T G T C T G A G C C T
S5 = G T G A A T G A G C C T
S6 = G T G A C A G A G C C T
S7 = G T G A C T A A G C C T
S8 = G T G A C T G T G C C T
S9 = G T G A C T G A A C C T
S10 = G T G A C T G A G A C T
S11 = G T G A C T G A G C A T
S12 = G T G A C T G A G C C A
S13 = A T G A C T G A G C C T
S14 = T T G A C T G A G C C T
S15 = C T G A C T G A G C C T
S16 = G T G A C C G A G C C T
S17 = G T G A C A G A G C C T
S18 = G T G A C G G A G C C T
S19 = G T G A C C G A G C T T
S20 = G T G A C T G A G C A T
S21 = G T G A C T G A G C G T
Is there any computational algorithm suitable for this problems?
I noted that following these algorithms: Needle, Matcher, Stretcher, and Water, the results of pairwise comparison between S0 and any of S1 to S21 is either 91.70% (with global alignment) or 100% (with local alignment) similar or identical.
Hi Kabir,
Your example dataset just shows satellite haplotypes differing by a single mutation from the reference. It's nothing you would tackle with simple pairwise similarity approaches. Biologically, interpreting these data is trivial: S0 is the ancestral genetic variant, S1–S21 are direct derivates of S0, they differ by one mutation from S0 and zero or two mutations from each other (a free-to-use haplotype network software is e.g. NETWORK: https://www.fluxus-engineering.com/sharenet.htm).
Since we usually have no information about how probable a mutation is at a given SNP site, we have no reason to make an a priori difference whether a mutation happened at the first, second, third etc. site when calculating pairwise distances to get "better" similarity measures. What we do when inferring trees is to use the Gamma distribution to model changing rates from site to site.
(Note that NETWORK allows you to reweight each site, in case you have such prior information. Usually reweighting is used to downweigh sites that appear to have high levels of homoplasy, random convergence.)
Regarding the higher mutation probabilty for transitions vs. transversions. All models used for phylogenetic tree inference under Maximum likelihood and Bayesian inference (see e.g. this list: http://www.iqtree.org/doc/Substitution-Models) can also be applied to calculating pairwise distances (implemented in a huge range of tree-inference softwares and R), with the consequence that the pairwise model-based distance between e.g. S0 and S1 (A<>G transition) would be smaller than between S0 and S2 (A<>T transversion), the latter equal to S0 and S4 (A<>T transversion at a different site). Note that RAxML, for instance, the main software to infer maximum likelihood trees these days and analyse extremely large SNP data sets (classic version: https://cme.h-its.org/exelixis/web/software/raxml/index.html; new version: https://github.com/amkozlov/raxml-ng/releases), can export model-based pairwise distances optimised during inferences that take into account not only differences between the substitution categories (A<>C, A<>G, etc.) but, in addition, model per site rate variation using the Gamma function.
So, I'm afraid, there nothing really to research here.
Cheers, Guido.
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A unit matrix will lead to a digraph with all variables on the same level, thus defeating the purpose to find a structural relationship between them.
this is my openion too although no research has mentioned that?
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Hi,
We developed a subpixel image registration algorithm for finding sub-pixel displacement and I want to test that against existing methods. I have compared that with subpixel image registration algorithm by Guizar et al. and also the algorithm developed by Foroosh et al. Does anyone knows any other accurate algorithm for subpixel image registration (preferably with an open-source code)?
Thank you.
Hi Amir,
Hope you have find the solution.
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Fiscal Frequency is simulated from a structure, to detect damage there are many vibration based algorithm for damage detection. However, most of them are written by Matlab code. Is there any explicit reference for algorithm development by Python for frequency response function (FRF) Analysis?
Thanks
i think, this paper very helpful for u
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I would like to test the performance of a modified algorithm developed to solve a real-world problem that has these characteristics : (1)Discrete (2)Multi-Objective (3) Black-box (4)Large-scale.
How we can do this? and if there are no such test problems, is it sufficient to show its performance on the real-world problem only? (where the true Pareto Front is unknown)
Best regards,
I suggest to search in the mathworks homepage, specifically in the Exchange file
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We have some research works related to Algorithm Design and Analysis. Most of the computer science journals focus the current trends such as Machine Learning, AI, Robotics, Block-Chain Technology, etc. Please, suggest me some journals that publish articles related to core algorithmic research.
There are several journal for algorithms. Some of them are:
Algorithmica
The Computer Journal
Journal of Discrete Algorithms
ACM Journal of Experimental Algorithmics
ACM Transactions on Algorithms
SIAM Journal on Computing
ACM Computing Surveys
Algorithms
Close related:
Theoretical Computer Science
Information Systems
Information Sciences
ACM Transactions on Information Systems
Information Retrieval
International Journal on Foundations of Computer Science
Related:
IEEE Transactions on Information Theory
Information and Computation
Information Retrieval
Knowledge and Information Systems
Information Processing Letters
ACM Computing Surveys
Information Processing and Management
best regards,
rapa
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Hi, I have little experience with Genetic algorithm previously.
Currently I am trying to use GA for some scheduling where I have some events and rooms which must be scheduled for these event each event has different time requirements and there are some constraints on availability of rooms.
But I want to know are there any other alternatives for GA since GA is a little random and slow process. So are their any other techniques which can replace GA.
There are tons of algorithms. Here a list:
DE
PSO
ABC
CMA-ES, etc
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The original psudo code of Cuckoo search
It is an algorithm that has the particularity to use levy flghts to perturb soulutions. Such a perturbation some times is small and some times large.
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We know that reflectance is influenced both by solar angle and satellite viewing angle, namely BRDF effects (Bidirectional Reflectance Distribution Function). But few researchers conducted BRDF corrections for water quality remote sensing during model devlelopment and intercomparison between two sensors. Whether or not this issue should be considered? How much accuracy can be improved for water quality estimation if BRDF was corrected?
I've done a lot of BRDF work related to vegetation in the past. I'm just starting a project that looks at water dynamics. The main directional issue I've seen over water is specular reflection, which is usually negligible for vegetation. It was an issue when we worked with POLDER data over northern Canada where a ~7km pixel is often a mixture of toundra and small water bodies. Wind will change the reflectance too, but I'm not sure if there is an obvious directionality of that effect.
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I read some scientific papers and most of them are using data dependency test to analyse their code for parallel optimization purpose. one of the dependency test is that, Banerjee's test. Are there other tests that can provide better result testing data dependency? and is it hard to do a test on control dependency code, and if we can, what are some of the technique that we can use?
Thank you
Hisham Alosaimi sorry forgot to share the link for the slides. Here you go
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Everyone, at some point, has probably thought about the problem artificial intelligence(AI) may represent in the future when the rate of unemployment is rising at an alarming speed thanks to robots that do a much better job than any human ever did but the question is : how, when, and where will the impact of artificial intelligence hit hardest?!
Perhaps another way to answer the question is with another question. Did electronic calculators, and subsequently computers, replace mathematicians, accountants, statisticians, and engineers? No. Just made them that much more productive.
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I am searching for an implementation of an algorithm that constructs three edge independent trees from a 3-edge connected graph. Any response will be appreciated. Thanks in Advance.
Dear Imran,
I suggest you to see links in subject.
-EXPLORING ALGORITHMS FOR EFFECTIVE ... - Semantic Scholar
-Graph-Theoretic Concepts in Computer Science: 24th International ...
-Expanders via Random Spanning Trees
Best regards
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Given a set of m (>0) trucks and a set of k (>=0) parcels. Each parcel has a fixed amount of payment for the trucks (may be same for all or may different for all) . The problem is to pick up the maximum number of parcels such that the profit of each truck is maximized. There may be 0 to k number of parcels in the service region of a particular truck. Likewise, a parcel can located in the service region of 0 to m trucks. There are certain constraints as follows.
1. Each truck can pick up exactly one parcel.
2. A parcel can be loaded to a truck if and only if it is located within the service region of the truck.
The possible cases are as follows
Case 1. m > k
Case 2. m = k
Case 3. m < k
As far as I know, to prove a given problem H as NP-hard, we need to give a polynomial time reduction algorithm to reduce a NP-Hard problem L to H. Therefore, I am in search of a similar NP-hard problem.
Kindly suggest some NP-hard problem which is similar to the stated problem. Thank you in advance.
Let p_{ij} denote the profit gained if parcel j is loaded onto truck i. If the parcel cannot be loaded onto that particular truck, we just set p_{ij} to zero. It looks like we just need to solve the following 0-1 linear program:
maximise
\sum_{i=1}^m \sum_{j=1}^k p_{ij} x_{ij}
subject to
\sum_{i=1}^m x_{ij} \le 1 (for all j)
\sum_{j=1}^k x_{ij} \le 1 (for all i)
x_{ij} \in \{0,1\} (for all i and all j).
If that's right, the problem is very easy. As stated by Przemysław and Helmut, it is equivalent to the linear assignment problem, which in turn is equivalent to maximum-weight bipartite matching.
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I want to find existing algorithms which have developed to find similarities in non-English documents using word mover's distance.
Hi Kumudika,
May be this type of research is of any help for your purpose. Next step how are these type of algoritms inplemented in for example in Python.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence IAAA2016, BuildingEarthMover’sDistanceonBilingual WordEmbeddingsforMachineTranslation
Kind regards
Rob
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Hello everyone,
I am doing some research on Fake News detection for this I would like to develop a measure that tells the difficulty level of the article. Actually I would like to analyze the webpage content.
Could someone give some good ideas to start with? I am looking for a Machine Learning kind of algorithms to develop the measure?
Note: I have gone through the indexes like ARI, SMOG, etc... but they are just based on number of words/sentences in the article and mostly they do not concentrate on number of complex words.
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Dear Sir:
I have an algorithm for hamilton cycle and I worked on it for many years. Now I really think it is done and my paper is complete and clear. It can be understood. But my English is limited, I need your help.
please carefully read it and think. can it be understood? How to revise the expression?
thank you very much!
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Hi,
I have written a DMD code, and I need to validate it. I need simple test problems, without involving to do any simulations, that I can reproduce with my code.
Dear doctor
Numerical simulation related to casting needs several algorithms and modeling techniques as follows:
1) PISO :coupling flow field to pressure in case of unsteady problem
2) SOLA-VOF will be required to solve free surface(this is fixed grid method; there are some alternatives for solving free surface depending on accuracy and time consumption)
3) enthalpy method to solve phase change phenomena
4) numerical models for solving magnetic field coupled with flow field
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I want to use optimization techniques through simulation tools with control algorithm for developing a customised FDM 3D printer
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I will be very thankful to recieve any article, books or researchers names to continue my research. Thanks a lot!
Thank you both for your support.
The idea of Curriculum Sequencing Algorithm is more popular than I expected, I have found, mainly in the medical field. Currently, I am categorising my areas of research in the clearest posible way while at the same time archiving foundation data on the topic. Essentially, I will be researching the educational elements that inspired this research, the content the curriculum could cover and algorithm designs and applications in an educational context. Again thank for your contribution, I will update you next week on the progress made.
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I need data assimilation fortran code for wave data and specially, I want to use Kalman filter scheme.
Here (http://enkf.nersc.no/) I found an ensemble Kalman filter code by its subroutines but I want a standard Kalman filter fortran code.
This code does not have the subroutines!
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I want to know what is the logic programming and how can combine the characteristics of the logic programming with the constraint satisfaction in any combinatorial problem
Hi Mai,
Very briefly, logic programming is a programming language paradigm which typically expresses properties of inputs and outputs using first-order clausal logics often with extra notations to control evaluation. Each clause specifies that if a finite number of conditions are satisfied then a conclusion is satisfied. Please see examples in the Wikipedia article and well-known texts such as W.F. Clocksin and C.S. Mellish, "Programming in Prolog", Third ed., Springer Verlag, 1987.
Generally, the method of evaluating programs is under-specified and these languages have been described as "declarative" for this reason, e.g. a program to sort lists often can just be read as a specification of a sorted list. This should be compared to imperative languages where each step of a sorting algorithm is encoded explicitly.
In these languages,  unifiability of first-order predicates is used during evaluation. In constraint logic programming, constraints are added to clauses and need to be solvable too.
Regards,
David Wolfram
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Deer peers,
I have encountered a difficulty and I am eagerly seeking your instructions and advice.
When resources are sufficient, i.e., resource constraints in master problem can easily be satisfied, the algorithm will convergent to a linear relaxation upper bound, see fig 3;
When resources are lesser, the algorithm will never convergent, see fig 1 and 2;
Can anybody tell me  whether it is the "tail-off" or degeneration?
Thank you!
Dear Zhu,
Several problems solved by column generation are ddiscussed in the papers:
which describe a stable Primal Dual Column Generation Method (PDCGM):
With best regards,
Jacek Gondzio
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In the SIM reconstruction algorithm, it is necessary to separate the measurement results, but separation need to know the exact translation phase of illumination stripes. I wrote a phase calibration procedure according to the method of literature, but the calibration results are not accurate. I would like to know whether there is a large necessary to calibrate the phase? If necessary, what needs to pay attention in the calibration algorithm? How to remove the measurement error superimposed on the results?
Dear Luo,
I suggest for you a links and attached files in topics.
-SIMcheck: a Toolbox for Successful Super-resolution Structured ...
-QSIM: quantitative structured illumination microscopy image ... - NCBI
https://www.ncbi.nlm.nih.gov › ... › PubMed Central (PMC)
-Optimal 2D-SIM reconstruction by two filtering steps with Richardson ...
www.nature.com › Scientific Reports › Articles
-System Calibration — SIMcheck 1.0.0 documentation
-Electronic Microwave Imaging with Planar Multistatic Arrays
Best regards
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Hello,
I tried to screen for the receptors of a ligand. I got a list of 5K proteins, and lots of them are not in the plasma membrane, e.g either in the mitochondria or ER membrane. Just wondering if anyone know if there is a way to sort out the plasma membrane proteins?
Thank you,
Longhou
Thank you so much, Aparajita!
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We assume that all the channels to be Rayleigh fading channels, including the residue interference channel. So the SINR at the relay should be
\gamma=|h_sr|^2*P_s/(|h_rr|^2*P_r+N0)
where h_sr and h_rr are the channel gains of the S->R and R->R links, respectively. and N0 is the variance of the AWGN. residue interference are independent with AWGN.
The confusion is here, in some paper I have read thar the average SINR can be calculated as
\bar{\gamma}=mean(|h_sr|^2*P_s) /mean(|h_rr|^2*P_r+N0)
but in my understanding, the average SINR should  be
mean(|h_sr|^2*P_s/(|h_sr|^2*P_s+N0))=mean(|h_sr|^2*P_s)*mean(1/(|h_sr|^2*P_s+N0))
the result will be different. I am a little confused about that.
Dear Ruoguang Li,
I suggest for you a links and attached files in topics.
- IEEE Xplore: IEEE Transactions on Wireless Communications ...
- Analysis of full-duplex relay networks with ... - IET Digital Library
Best regards
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How we can make the mutation part of an algorithm adaptive? Is there any code for that I can understand the difference?
For example in simulated annealing the probability
depends on a parameter that mimics a temperature.
And this temperature drops in time (steps),
therefore the name annealing.
But you could also make the probability or
impact (size) of a mutation dependent on how far
you are off from a good solution.
Suppose you can measure the distance of the
state of your intermediate systems to the
desired solution as a number z between 0 and max,
then you can define p := z/max, and do the
mutation only if a random number r from [0.0, 1.0]
is greater than 1-p.
Likewise you can make the size of the effect
of a mutation dependent on p.
Regards,
Joachim
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I am looking for a method to compare the dataset represented in blue with the one in red and I need to extract one single value from this comparison. The idea is to compare datasets generated with different combinations of parameters to a 'optimal' dataset and get a 'score' of each one so I can see which combination of parameters is the closest to the optimal model. I came up with a few options like Fréchet distance, Hausdorff distance and MSE but I don't know which one would work best for me.
Does anybody have any suggestion or another method that could work?
Thank you.
"I came up with a few options like Fréchet distance, Hausdorff distance and MSE but I don't know which one would work best for me."
You have all the basics tool needed.  Which and which variant might work best four you depend on the application: classification, recognition, etc.  I think the best way is to create a setup where you can run multiple experiments using these different distances and you improve based on your findings (or stop when it's "good enough")
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I'm developing a recommender system for movies using a matrix containing data of films found in DBpedia. However, my matrix contains a lot of missing information (e.g. some films does not contain the year attribute, others the writer etc). So my question is if is it possible to use an imputation algorithm taking into account the features of DBpedia, which is mainly built from a collaborative scenario and therefore prone to errors, and that my information is mainly textual (only the year attribute is numeric). My attributes are Name, Country, Directors, Producers, Starring, Writers, Year, and Subject.  The missing data is denoted with a question mark (?). I've also attached an example of my matrix with only 100 films (the full matrix contains around 101000 films).
Dear Christian
There are some data mining books which provide a section addressing the imputation of missing data. These range from very simple solutions (such as use the most frequent value or the average value) to other more complex. One example is "Data Mining: Concepts and Techniques", by J. Han, M. Kamber and J. Pei.
As an alternative, you can also examine the IMDb data, since it might be less prone to the problem of missing data. Check the below paper for more information:
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At present, software tools are used placement & routing in chip design. Still any new algorithm development is going on for routing?
Long time ago my project was a Greedy router for custom VLSI. On working at a company they used auto routing software, but it did not take detailed account of VLSI design rules (particularly in 3D). Experts would go over the routing & insert "dog-legs" where design rules would cause expansion of the routing. I used a simplified version of the design rule checker, to modify the routine & automatically insert the dog-legs, giving up to 20% reduction in silicon routing usage. That was year ago - I do not know what the state of the art is now.  Chip user manufacturing is a big cost - how many layers are they going to need on there board?  Rather than the academic algorithms, I would look at volume customer requirements first (unless you are working at semi-custom, low volume).
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I need to feed the data sets for alcohol and smoking intervention, to the algorithm to evaluate the outcome of the recommender system.
Any suggestion to process the data sets will be very helpful to continue my research work.
Thanks
Hello,
See the websites of public health of a country, they should provide such information in a statistical view maybe
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Hello everyone,
I need to invert a symmetric banded hessian matrix H, which has 7 diagonals.
H^(-1) can be considered as banded, and thus, I do not need to compute the complete inverse matrix, but rather its approximation. (It could be assumed to have 11 or 13 diagonals for example.)
I am looking for a method which does not imply parallelization.
Is there any possibility to build such an algorithm with R, in linear time ?
May I ask why you need that then? No serious numerical formulation should need the "inverse" of a matrix.
Anyway, do a search on "SPAI" or "sparse approximate inverse" in Google scholar. There are a lot of papers (and also libraries) are out there that can help you.
Also check if these help (among a lot of other things):
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I am now working on an RLS algorithm which is used to estimate the eigen-frequency of vibration. Most of time, if the noise level is low, the filter works well. But if there are impulsive-like disturbance at filter input, then this causes large estimation offset, which takes long until recovery for the filter.
Does someone know which methods can help to avoid the erroneous disturbance? In the attached pic there is a sample.
Thanks
.The following technical paper discusses removel different types of Noise.The algorithm  given may be helpful to remove Impulse Noise.
.Janardhan&K.KishanRao., “ Modified Denoising Algorithm for Signals with
Different Noise Distributions”., International Journal of Engineering
Sciences and Technology, Vol:1., Issue:5., March-April 2016., PP:53-61.,
ISSN:  2455-2143.
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Does anyone have an implementation of one of the four algorithms Pascal, Close, MaxMiner or Apriori?
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I have an image transmitted to the input (pin type: cVideoPin ) of my ADTF plugin. I have also created a buffer that can hold the input image. I need to make the image available in this buffer be available to Opencv computation preferably through a Mat container.
I need suggestions on how to make the image in ADTF buffer compatible with opencv Mat.
how is the data stored? in a byte array?
if yes look at
byte *data;
cv::Mat imageWithData = cv::Mat(sizeOfData, 1, CV_8U, data).clone();
After you have created this matrix, call the reshape function with the number of rows in the image.
Mat reshapedImage = imageWithData.reshape(0, numberOfRows);
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Hi RGaters,
Does anyone know how to modify the order in a sample to modify Kendall's tau value(s) ? Let me clarify what I am looking for. Consider that we have N realizations of K random variables. Each realizations of the group of K variables is independent from the other ones. Inside this realization, the K variables might be independent or not, we do not really care. They even can follow different distributions. The question is the following : from any sample (size N x K) that we call M, can I exchange the places of M[i_1,1] with M[i_2,1], M[i_3,2] with M[i_4,2] and so on, possibly coming back to the first column with an interative algorithm to finally obtain as a result a new (rearranged) sample M' where tau[1,2] = first wanted value, tau[1,3] = second wanted value, and so on, i.e. can I get rearranged data to get a desired tau-matrix. If yes, any algorithm to suggest ?
I used Cholesky method to do the same thing to reach a desired Pearson-correlation matrix, but I have to admit I'm facing a wall on this issue right now.
Any help is welcome !
Hi René,
Thanks for your answer, however, I work in practice with 100 000 * 10 (it's true that I did not mentioned this above) matrices that I want to rearrange this sample to reach a target kendall's tau matrix (as close as possible). It is not the kendall's tau matrix that I want to rearrange, it is the data which lead to this kendall's tau matrix. That'd be too easy :)
Considering this, there is absolutely no way that I try every possible combinations and see which one to choose. I need some intelligent trick such as Cholesky method to get correlate variables.
So basically, it's not only about programming skills, it's also about applied mathematics.
Best regards !
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Thanks Mr.Lago Augusto
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I have set up a MapReduce code for KMeans algorithm using python. The code is working fine.The resultant cluster file that is generated as part-00000 output file contains the set of data points with full dimension.Now I want to find out the index of the data points.How can I do that?
What do you mean by index of clusters?
KMeans cluster centers are not the points from the dataset.
You can find the index of cluster center which is closest to your test point (may be from train or test set).
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In order to test any event detection algorithm, it is common practice to compute a confusion matrix, in order to get performance parameters (e.g. sensitivity, and specificity).
It is well known that the confision matrix must be built (identifing and) counting the number of negatives/positives true/false detections, according to a pre-annotated dataset (Gold Standard).
Now, here is my question: how can i identify a true/false positive?
Let me show you a little example.
My algorith wants to detect QRS-Complex's peak, in order to get R-R intervals of a ECG recording. My pre-annotated dataset identify QRS complex not on QRS peak, but some samples before.
It is obvious that, testing my algorithm, i cannot identify a true positive detection "when my algorithm identify the same point as the gold standard", since there's always a time-shift between them. At the other side, all of my detections (because of the time-shift) would be identified as false positives (no time-coincidence with Gold Standard).
An easy solution would be to consider a "coincidence-window" around each point in the Gold Standard but, if i fix the problem with true/false positives, the problem with true/false negatives remains still unsolved.
Has anybody an idea? (Any reference would be great for me).
Thanks a lot.
Best regards,
Marco Bilucaglia
Chandola, V, Banerjee, A & Kumar, V 2009, ‘Anomaly detection: A survey’, ACM Comput. Surv., vol. 41, no. 3, pp. 1–58.
Chandola, V, Banerjee, A & Kumar, V 2012, ‘Anomaly Detection for Discrete Sequences: A Survey’, IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 5, pp. 823–839.
Varun Chandola, Deepthi Cheboli & Vipin Kumar 2009, Detecting Anomalies in a Time Series Database, Department of Computer Science and Engineering, University of Minnesota, 4-192 EECS Bulding, 200 Union Street SE, Minneapolis, MN 55455-0159 USA, Minneapolis.
If I remember correctly: If the process is stationary and the distribution of the random walk is parametric, then state-space methods for time series analysis can be appropriate to use.For example, the R package MARSS implements this. If the distribution of the random walk is non-parametric or the process is not stationary, then simulations need to be employed. Perhaps, particle filtering can be employed.
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Hi all,
I am working with 3 others to solve a real life problem for an remote Australian company. The problem is electricians or mechanical fitter travel daily to repair or maintain assets. However they're not aware there could be another near by task they could also do that meats their skill set. So we developed an app to do so. Now i am working on developing a formula for optimization of the daily trip.
I thought Hamiltonian circuit would be best because they have to return to base at the end of their shift. Also another constraint is they work 9 hours including travel time to and from base.
Also some of the tasks can take up to 1 or 2 or 3 or 6 or more hours to complete and the travel time could be 2 hours or more.
I thought they could go out and the jobs that take up the most amount of hours first and on their way back pick up shorter ones.
Could anyone suggest anything i could do better or use?
I have the latitude and longitude could anyone suggest how i could simulate them specially how i could represent 6 hours on a node?
The formula is attached as a picture, feel free to suggest anything.
Thank you
This is a classic problem of the type "vehicle routing with time windows", and its importance has only grown over the years. There is a rather large body of work on this class of problems. I would first study papers discussing mathematical optimization models solved through the use of exact methods, as it is typically those papers that will illustrate what mathematical optimization is capable of, in terms of what you can model, how to model well, and how to efficiently solve these problems.
Look on the web for the paper "The vehicle routing problem: state of the art", by De Jaegere, Defraeye, and Van Nieuwenhuysen from 2014. They appear to have a rather good overview, with plenty of references on what we would call "exact" methods. I think those model-heavy papers are better to start looking at than those that focus on quickly obtaining any solution - I am one of those that like to stare at the mathematical model and try to work out when and why it works, and to try several (equivalent) varieties of the model(s) in order to perhaps find models that describe the same problem but might be solved more efficiently. With todays software and hardware we can solve pretty substantial problems in this class without resorting to heuristics.
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I have results of approximation ratio of returned by different algorithms after running on benchmarks. Now i want to visualize  these results as much better as i can so that outliers  and other statistically important information are visible. In addition that the mechanism must be something which i can add in my article later on if i want to publish.
If your results are space or comma delimited you can try a histogram using Excel as a start.
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I need code in any language (preferably MATLAB, Java, C++, C#) regarding rule based classification using metaheuristic algorithm. Any help will be highly appreciated.
Thanks
Thanks Jonas Mellin and Pete Bettinger. Actually, metaheuristics have been used for rule mining to solve classification problems - just like the one mentioned below. I have already gone through the methodology mentioned in such few papers, but I need some implementation code to strengthen my understanding of the mater. Hope it clarifies my concerns.
For example:
Bae, C., Yeh, W. C., Wahid, N., Chung, Y. Y., & Liu, Y. (2012). A new simplified swarm optimization (SSO) using exchange local search scheme. International Journal of Innovative Computing, Information and Control, 8(6), 4391-4406.
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i working in searchable encryption and i want to develop algorithm for this use symmetric encyrption combine with attribute based encryption which use public key encryption how i can do that?
There are searchable encryption for symmetric and for public key (attribute based). The difference is of course that symmetric key ones are more efficient. Also if your method assumes that anyone can encrypt then you have to use public key whereas symmetric key assumes that encryption and decryption needs the system's secret.
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Hi friends,
I measured the end to end delay in terms of density in leach-c protocol and kmeans algorithm applied on leach-c.
I need the interpretation of my simulation attached.
Is it correct?