NVIDIA

Antimicrobial peptides emerge as compounds that can alleviate the global health hazard of antimicrobial resistance, prompting a need for novel computational approaches to peptide generation. Here, we propose HydrAMP, a conditional variational autoencoder that learns lower-dimensional, continuous representation of peptides and captures their antimicrobial properties. The model disentangles the learnt representation of a peptide from its antimicrobial conditions and leverages parameter-controlled creativity. HydrAMP is the first model that is directly optimized for diverse tasks, including unconstrained and analogue generation and outperforms other approaches in these tasks. An additional preselection procedure based on ranking of generated peptides and molecular dynamics simulations increases experimental validation rate. Wet-lab experiments on five bacterial strains confirm high activity of nine peptides generated as analogues of clinically relevant prototypes, as well as six analogues of an inactive peptide. HydrAMP enables generation of diverse and potent peptides, making a step towards resolving the antimicrobial resistance crisis.

The BioCreative National Library of Medicine (NLM)-Chem track calls for a community effort to fine-tune automated recognition of chemical names in the biomedical literature. Chemicals are one of the most searched biomedical entities in PubMed, and-as highlighted during the coronavirus disease 2019 pandemic-their identification may significantly advance research in multiple biomedical subfields. While previous community challenges focused on identifying chemical names mentioned in titles and abstracts, the full text contains valuable additional detail. We, therefore, organized the BioCreative NLM-Chem track as a community effort to address automated chemical entity recognition in full-text articles. The track consisted of two tasks: (i) chemical identification and (ii) chemical indexing. The chemical identification task required predicting all chemicals mentioned in recently published full-text articles, both span [i.e. named entity recognition (NER)] and normalization (i.e. entity linking), using Medical Subject Headings (MeSH). The chemical indexing task required identifying which chemicals reflect topics for each article and should therefore appear in the listing of MeSH terms for the document in the MEDLINE article indexing. This manuscript summarizes the BioCreative NLM-Chem track and post-challenge experiments. We received a total of 85 submissions from 17 teams worldwide. The highest performance achieved for the chemical identification task was 0.8672 F-score (0.8759 precision and 0.8587 recall) for strict NER performance and 0.8136 F-score (0.8621 precision and 0.7702 recall) for strict normalization performance. The highest performance achieved for the chemical indexing task was 0.6073 F-score (0.7417 precision and 0.5141 recall). This community challenge demonstrated that (i) the current substantial achievements in deep learning technologies can be utilized to improve automated prediction accuracy further and (ii) the chemical indexing task is substantially more challenging. We look forward to further developing biomedical text-mining methods to respond to the rapid growth of biomedical literature. The NLM-Chem track dataset and other challenge materials are publicly available at https://ftp.ncbi.nlm.nih.gov/pub/lu/BC7-NLM-Chem-track/. Database URL https://ftp.ncbi.nlm.nih.gov/pub/lu/BC7-NLM-Chem-track/.

Machine learning (ML) models, if trained to data sets of high-fidelity quantum simulations, produce accurate and efficient interatomic potentials. Active learning (AL) is a powerful tool to iteratively generate diverse data sets. In this approach, the ML model provides an uncertainty estimate along with its prediction for each new atomic configuration. If the uncertainty estimate passes a certain threshold, then the configuration is included in the data set. Here we develop a strategy to more rapidly discover configurations that meaningfully augment the training data set. The approach, uncertainty-driven dynamics for active learning (UDD-AL), modifies the potential energy surface used in molecular dynamics simulations to favor regions of configuration space for which there is large model uncertainty. The performance of UDD-AL is demonstrated for two AL tasks: sampling the conformational space of glycine and sampling the promotion of proton transfer in acetylacetone. The method is shown to efficiently explore the chemically relevant configuration space, which may be inaccessible using regular dynamical sampling at target temperature conditions.

Task offloading is a powerful tool in Mobile Edge Computing (MEC). However, in many practical scenarios, the number of required processing cycles of a task is unknown beforehand and only known until its completion. This poses a serious challenge in making offloading decisions as the number of processing cycles is a key parameter to determine whether a task’s deadline can be met. To cope with such processing uncertainty, we formulate a Chance-Constrained Program (CCP) that offers probabilistic guarantees to task deadlines. The goal is to minimize energy consumption for the users while meeting the probabilistic task deadlines. We assume that only the means and variances of the random processing cycles are available, without any knowledge of distribution functions. We employ a powerful tool called Exact Conic Reformulation (ECR) that reformulates probabilistic deadline constraints into deterministic ones. Subsequently, we design an online solution called EPD (Energy-minimized solution with Probabilistic Deadline guarantee) for periodic scheduling and schedule updates during run-time. We show that EPD can address the processing uncertainty with probabilistic deadline guarantees while minimizing the users’ energy consumption.

This article analyzes visual data captured from five countries and three U.S. states to evaluate the effectiveness of lockdown policies for reducing the spread of COVID-19. The main challenge is the scale: nearly six million images are analyzed to observe how people respond to the policy changes.

The current work presents the first experimental demonstration of real-time Ethernet (ETH) trial services and 4K-Ultra High Definition (UHD) video application transmission over a 2λ Wavelength Division Multiplexing (WDM) analog Fiber-Wireless (FiWi) mmWave X-haul network, supporting dynamic flexible capacity allocation through an integrated silicon photonic Si <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> Reconfigurable Optical Add Drop Multiplexer (ROADM) and 60 GHz wireless transmission across a 7 m V-band link distance, while controlled by a Software Defined Network (SDN) controller based on Open Daylight. Analog X-haul transport of the radio signals is based on an Intermediate Frequency over Fiber (IFoF) scheme centered at 1.5 GHz using an Orthogonal Frequency Division Multiplexing (OFDM) radio-waveform with a bandwidth of 204 MHz, generated by a Field Programmable Gated Array (FPGA)-based RF System-on-Chip (SoC) processor that converts on-the-fly the real-time ETH downlink traffic to analog radio and vice-versa for the uplink. Dynamic allocation of the X-haul traffic capacity is handled through the use of the 802.1Q Virtual Local Area Network (VLAN) tag-mechanism, which controls the forwarding operation to the proper DAC and InP EML for optical modulation using Intensity Modulation/Direct Detection (IM/DD) schemes, while the wavelength routing operation is handled by the low-loss four-port Si <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> ROADM featuring only 2.5 dB fiber-to-fiber losses based on a cascaded MZI-interleaver design on the TriPleX platform, routing the real-time traffic to a second mmWave antenna site. Detailed measurements and traffic statistics indicate end-to-end latency of less than 260 μs and a packet loss less than 0.0054% across a dynamic range of at least 6.5 dB in the optical domain, while the high user bandwidth and signal quality are validated by an uninterrupted 4K-UHD video transmission across the FiWi X-haul mmWave transport network. The current work aims to shape a complete technology roadmap for Point-to-Multipoint FiWi transport network architectures with high spectral efficiency X-haul transport for dense areas and 5G/6G hotspots of future mmWave Centralized Radio Access Networks (C-RANs).

Zero-shot learning (ZSL) requires one to associate visual and semantic information observed from data of seen classes, so that test data of unseen classes can be recognized based on the described semantic representation. Aiming at synthesizing visual data from the given semantic inputs, hallucination-based ZSL approaches might suffer from mode collapse and biased problems due to the lack of ability in modeling the desirable visual features for unseen categories. In this paper, we present a generative model of Cross-Modal Consistency GAN (CMC-GAN), which performs semantics-guided intra-category knowledge transfer across image categories, so that data hallucination for unseen classes can be achieved with proper semantics and sufficient visual diversity. In our experiments, we perform standard and generalized ZSL on four benchmark datasets, confirming the effectiveness of our approach over that of state-of-the-art ZSL methods.

This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user's publicly available tweets (the user's 'timeline'). In general, detecting health-related tweets is notoriously challenging for natural language processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health. Thus, finding those tweets in a user's timeline that mention specific health-related concepts such as medications requires addressing extreme imbalance. Task 3 called for detecting tweets in a user's timeline that mentions a medication name and, for each detected mention, extracting its span. The organizers made available a corpus consisting of 182 049 tweets publicly posted by 212 Twitter users with all medication mentions manually annotated. The corpus exhibits the natural distribution of positive tweets, with only 442 tweets (0.2%) mentioning a medication. This task was an opportunity for participants to evaluate methods that are robust to class imbalance beyond the simple lexical match. A total of 65 teams registered, and 16 teams submitted a system run. This study summarizes the corpus created by the organizers and the approaches taken by the participating teams for this challenge. The corpus is freely available at https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-3/. The methods and the results of the competing systems are analyzed with a focus on the approaches taken for learning from class-imbalanced data.

Reliably planning fingertip grasps for multi-fingered hands lies as a key challenge for many tasks including tool use, insertion, and dexterous in-hand manipulation. This task becomes even more difficult when the robot lacks an accurate model of the object to be grasped. Tactile sensing offers a promising approach to account for uncertainties in object shape. However, current robotic hands tend to lack full tactile coverage. As such, a problem arises of how to plan and execute grasps for multi-fingered hands such that contact is made with the area covered by the tactile sensors. To address this issue, we propose an approach to grasp planning that explicitly reasons about where the fingertips should contact the estimated object surface while maximizing the probability of grasp success. Key to our method's success is the use of visual surface estimation for initial planning to encode the contact constraint. The robot then executes this plan using a tactile-feedback controller that enables the robot to adapt to online estimates of the object's surface to correct for errors in the initial plan. Importantly, the robot never explicitly integrates object pose or surface estimates between visual and tactile sensing, instead it uses the two modalities in complementary ways. Vision guides the robots motion prior to contact; touch updates the plan when contact occurs differently than predicted from vision. We show that our method successfully synthesises and executes precision grasps for previously unseen objects using surface estimates from a single camera view. Further, our approach outperforms a state of the art multi-fingered grasp planner, while also beating several baselines we propose.

The Functional Safety Standards Committee (FSSC) turned one year old. This article describes the FSSC results both in terms of sponsorship of IEEE Computer Society safety-related standards (for example, IEEE P2851) and of nurturing new initiatives.