pwc-archive/papers-with-abstracts huggingface dataset:
Enhancing Energy Efficiency for Reconfigurable Intelligent Surfaces with Practical Power Models
Authors: Zhiyi Li, Jida Zhang, Jieao Zhu, Shi Jin, Linglong Dai
Date: 2023-10-24 00:00:00

Reconfigurable intelligent surfaces (RISs) are widely considered a promising technology for future wireless communication systems. As an important indicator of RIS-assisted communication systems in green wireless communications, energy efficiency (EE) has recently received intensive research interest as an optimization target. However, most previous works have ignored the different power consumption between ON and OFF states of the PIN diodes attached to each RIS element. This oversight results in extensive unnecessary power consumption and reduction of actual EE due to the inaccurate power model. To address this issue, in this paper, we first utilize a practical power model for a RIS-assisted multi-user multiple-input single-output (MU-MISO) communication system, which takes into account the difference in power dissipation caused by ON-OFF states of RIS's PIN diodes. Based on this model, we formulate a more accurate EE optimization problem. However, this problem is non-convex and has mixed-integer properties, which poses a challenge for optimization. To solve the problem, an effective alternating optimization (AO) algorithm framework is utilized to optimize the base station and RIS beamforming precoder separately. To obtain the essential RIS beamforming precoder, we develop two effective methods based on maximum gradient search and SDP relaxation respectively. Theoretical analysis shows the exponential complexity of the original problem has been reduced to polynomial complexity. Simulation results demonstrate that the proposed algorithm outperforms the existing ones, leading to a significant increase in EE across a diverse set of scenarios.
Neuromorphic Sampling of Sparse Signals
Authors: Abijith Jagannath Kamath, Chandra Sekhar Seelamantula
Date: 2023-10-24 00:00:00
Tasks: parameter estimation

Neuromorphic sampling is a bioinspired and opportunistic analog-to-digital conversion technique, where the measurements are recorded only when there is a significant change in the signal amplitude. Neuromorphic sampling has paved the way for a new class of vision sensors called event cameras or dynamic vision sensors (DVS), which consume low power, accommodate a high-dynamic range, and provide sparse measurements with high temporal resolution making it convenient for downstream inference tasks. In this paper, we consider neuromorphic sensing of signals with a finite rate of innovation (FRI), including a stream of Dirac impulses, sum of weighted and time-shifted pulses, and piecewise-polynomial functions. We consider a sampling-theoretic approach and leverage the close connection between neuromorphic sensing and time-based sampling, where the measurements are encoded temporally. Using Fourier-domain analysis, we show that perfect signal reconstruction is possible via parameter estimation using high-resolution spectral estimation methods. We develop a kernel-based sampling approach, which allows for perfect reconstruction with a sample complexity equal to the rate of innovation of the signal. We provide sufficient conditions on the parameters of the neuromorphic encoder for perfect reconstruction. Furthermore, we extend the analysis to multichannel neuromorphic sampling of FRI signals, in the single-input multi-output (SIMO) and multi-input multi-output (MIMO) configurations. We show that the signal parameters can be jointly estimated using multichannel measurements. Experimental results are provided to substantiate the theoretical claims.
Exploitation des propri{é}t{é}s de saturation synaptique pour obtenir un neurone {à} fr{é}quence sp{é}cifique
Authors: Guillaume Marthe, Claire Goursaud
Date: 2023-10-24 00:00:00
Tasks: Temporal Sequences

Energy consumption remains the main limiting factors in many promising IoT applications. In particular, micro-controllers consume far too much power. In order to overcome this problem, new circuit designs have been proposed and the use of spiking neurons and analog computing has emerged as it allows a very significant consumption reduction. However, working in the analog domain brings difficulty to handle the sequential processing of incoming signals as is needed in many use cases.In this paper, we propose to use a bio-inspired phenomenon called Interacting Synapses to produce a time filter. We propose a model of synapses that makes the neuron fire for a specific range of delays between two incoming spikes, but not react when this Inter-Spike Timing is not in that range. We study the parameters of the model to understand how to adapt the Inter-Spike Timing. The originality of the paper is to propose a new way, in the analog domain, to deal with temporal sequences.
Knowledge-driven Meta-learning for CSI Feedback
Authors: Han Xiao, Wenqiang Tian, Wendong Liu, Jiajia Guo, Zhi Zhang, Shi Jin, Zhihua Shi, Li Guo, Jia Shen
Date: 2023-10-24 00:00:00
Tasks: Meta-Learning

Accurate and effective channel state information (CSI) feedback is a key technology for massive multiple-input and multiple-output systems. Recently, deep learning (DL) has been introduced for CSI feedback enhancement through massive collected training data and lengthy training time, which is quite costly and impractical for realistic deployment. In this article, a knowledge-driven meta-learning approach is proposed, where the DL model initialized by the meta model obtained from meta training phase is able to achieve rapid convergence when facing a new scenario during target retraining phase. Specifically, instead of training with massive data collected from various scenarios, the meta task environment is constructed based on the intrinsic knowledge of spatial-frequency characteristics of CSI for meta training. Moreover, the target task dataset is also augmented by exploiting the knowledge of statistical characteristics of wireless channel, so that the DL model can achieve higher performance with small actually collected dataset and short training time. In addition, we provide analyses of rationale for the improvement yielded by the knowledge in both phases. Simulation results demonstrate the superiority of the proposed approach from the perspective of feedback performance and convergence speed.
LDPC Decoding with Degree-Specific Neural Message Weights and RCQ Decoding
Authors: Linfang Wang, Caleb Terrill, Richard Wesel, Dariush Divsalar
Date: 2023-10-24 00:00:00
Tasks: Decoder, Quantization

Recently, neural networks have improved MinSum message-passing decoders for low-density parity-check (LDPC) codes by multiplying or adding weights to the messages, where the weights are determined by a neural network. The neural network complexity to determine distinct weights for each edge is high, often limiting the application to relatively short LDPC codes. Furthermore, storing separate weights for every edge and every iteration can be a burden for hardware implementations. To reduce neural network complexity and storage requirements, this paper proposes a family of weight-sharing schemes that use the same weight for edges that have the same check node degree and/or variable node degree. Our simulation results show that node-degree-based weight-sharing can deliver the same performance requiring distinct weights for each node. This paper also combines these degree-specific neural weights with a reconstruction-computation-quantization (RCQ) decoder to produce a weighted RCQ (W-RCQ) decoder. The W-RCQ decoder with node-degree-based weight sharing has a reduced hardware requirement compared with the original RCQ decoder. As an additional contribution, this paper identifies and resolves a gradient explosion issue that can arise when training neural LDPC decoders.
Xeno Amino Acids: A look into biochemistry as we don't know it
Authors: Sean M. Brown, Christopher Mayer-Bacon, Stephen Freeland
Date: 2023-10-24 00:00:00

Would another origin of life resemble Earth's biochemical use of amino acids? Here we review current knowledge at three levels: 1) Could other classes of chemical structure serve as building blocks for biopolymer structure and catalysis? Amino acids now seem both readily available to, and a plausible chemical attractor for, life as we don't know it. Amino acids thus remain important and tractable targets for astrobiological research. 2) If amino acids are used, would we expect the same L-alpha-structural subclass used by life? Despite numerous ideas, it is not clear why life favors L-enantiomers. It seems clearer, however, why life on Earth uses the shortest possible (alpha-) amino acid backbone, and why each carries only one side chain. However, assertions that other backbones are physicochemically impossible have relaxed into arguments that they are disadvantageous. 3) Would we expect a similar set of side chains to those within the genetic code? Not only do many plausible alternatives exist and evidence exists for both evolutionary advantage and physicochemical constraint for those encoded by life. Overall, as focus shifts from amino acids as a chemical class to specific side chains used by post-LUCA biology, the probable role of physicochemical constraint diminishes relative to that of biological evolution. Exciting opportunities now present themselves for laboratory work and computing to explore how changing the amino acid alphabet alters the universe of protein folds. Near-term milestones include: a) expanding evidence about amino acids as attractors within chemical evolution; b) extending characterization of other backbones relative to biological proteins; c) merging computing and laboratory explorations of structures and functions unlocked by xeno peptides.
Analysis of proteins in the light of mutations
Authors: Jorge A. Vila
Date: 2023-10-24 00:00:00
Tasks: Protein Folding

Proteins have evolved through mutations, amino acid substitutions, since life appeared on Earth, some 109 years ago. The study of these phenomena has been of particular significance because of their impact on protein stability, function, and structure. Three of the most recent findings in these areas deserve to be highlighted. First, an innovative method has made it feasible to massively determine the impact of mutations on protein stability. Second, a theoretical analysis showed how mutations impact the evolution of protein folding rates. Lastly, it has been shown that native-state structural changes brought on by mutations can be explained in detail by the amide hydrogen exchange protection factors. This study offers a new perspective on how those findings can be used to analyze proteins in the light of mutations. The preliminary results indicate that: (i) mutations can be viewed as sensitive probes to identify "typos" in the amino-acid sequence and also to assess the resistance of naturally occurring proteins to unwanted sequence alterations; (ii) the presence of "typos" in the amino acid sequence, rather than being an evolutionary obstacle, could promote faster evolvability and, in turn, increase the likelihood of higher protein stability; (iii) the mutation site is far more important than the substituted amino acid in terms of the protein's marginal stability changes, and (iv) the protein evolution unpredictability at the molecular level by mutations exists even in the absence of epistasis effects. Finally, the study results support the Darwinian concept of evolution as "descent with modification" by demonstrating that some regions of any protein sequence are susceptible to mutations while others are not.
Habitat fragmentation reshapes genomic footprints of selection in a forest herb
Authors: Frederik Van Daele, Olivier Honnay, Hanne De Kort
Date: 2023-10-24 00:00:00

Understanding the combined effects of climate change and habitat fragmentation on the adaptive potential of plant populations is essential for devising effective conservation strategies. This is particularly important where mating system variation impacts the evolutionary consequences of habitat fragmentation. Here we aimed to reveal how habitat fragmentation and climate adaptation jointly influence the evolutionary trajectories in Primula elatior, a heterostylous self-incompatible and dispersal-constrained forest herb. We quantified the genomic variation and degree of herkogamy, a floral trait reducing self-pollination, across 60 geographically paired populations of Primula elatior across Europe, each pair featuring contrasting levels of habitat fragmentation. Our findings revealed a large and unique set of adaptive outliers in more fragmented landscapes, compared to high-connectivity ones, despite the geographic proximity of the sampling pairs. This suggests elevated selective pressures in fragmented habitats, mirrored by a reduced adaptive potential to cope with climate change. Finally, a minority of genetic variants associated with herkogamy were influenced by current levels of habitat fragmentation and population size, potentially signalling early indicators of evolutionary mating system changes in response to pollinator limitation. Because evolutionary trajectories and adaptive potential are expected to be increasingly affected by habitat fragmentation, our findings underscore the importance of considering both habitat fragmentation and climate adaptation in conservation research and planning.
GOLEM: distribution of Gene regulatOry eLEMents within the plant promoters
Authors: Lukáš Nevosád, Božena Klodová, David Honys, Radka Svobodová, Tomáš Raček, Petra Procházková Schrumpfová
Date: 2023-10-23 00:00:00

Motivation: The regulation of gene expression during tissue development is extremely complex. One of the key regulatory mechanisms of gene expression involves the recognition of regulatory motifs by various proteins in the promoter regions of many genes. Localisation of these motifs in proximity to the transcription start site (TSS) or translation start site (ATG) is critical for regulating the initiation and rate of transcription. The levels of transcription of individual genes, regulated by these motifs, can vary signifi cantly in diff erent tissues and developmental stages, especially during tightly regulated processes such as sexual reproduction. However, the precise localisation and visualisation of the regulatory motifs within gene promoters with respect to gene transcription in specifi c tissues, can be challenging. Results: Here, we introduce a program called GOLEM (Gene regulatOry eLEMents) which enables users to precisely locate any motif of interest with respect to TSS or ATG within the relevant plant genomes across the plant Tree of Life (Marchantia, Physcomitrium, Amborella, Oryza, Zea, Solanum and Arabidopsis). The visualisation of the motifs is performed with respect to the transcript levels of particular genes in leaves and male reproductive tissues, and can be compared with genome-wide distribution regardless of the transcription level. Availability and implementation: GOLEM is freely available at https://golem.ncbr.muni.cz and its source codes are provided under the MIT licence at GitHub at https://github.com/sb-ncbr/golem.
A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian Learning and Free Energy Minimization
Authors: Alexander Ororbia, Mary Alexandria Kelly
Date: 2023-10-14 00:00:00

Over the last few years, large neural generative models, capable of synthesizing semantically rich passages of text or producing complex images, have recently emerged as a popular representation of what has come to be known as ``generative artificial intelligence'' (generative AI). Beyond opening the door to new opportunities as well as challenges for the domain of statistical machine learning, the rising popularity of generative AI brings with it interesting questions for Cognitive Science, which seeks to discover the nature of the processes that underpin minds and brains as well as to understand how such functionality might be acquired and instantianted in biological (or artificial) substrate. With this goal in mind, we argue that a promising research program lies in the crafting of cognitive architectures, a long-standing tradition of the field, cast fundamentally in terms of neuro-mimetic generative building blocks. Concretely, we discuss the COGnitive Neural GENerative system, such an architecture that casts the Common Model of Cognition in terms of Hebbian adaptation operating in service of optimizing a variational free energy functional.
Hierarchical Event Descriptor library schema for EEG data annotation
Authors: Dora Hermes, Tal Pal Attia, Sándor Beniczky, Jorge Bosch-Bayard, Arnaud Delorme, Brian Nils Lundstrom, Christine Rogers, Stefan Rampp, Seyed Yahya Shirazi, Dung Truong, Pedro Valdes-Sosa, Greg Worrell, Scott Makeig, Kay Robbins
Date: 2023-10-04 00:00:00
Tasks: EEG

Standardizing terminology to annotate electrophysiological events can improve both computational research and clinical care. Sharing data enriched with standard terms can facilitate data exploration, from case studies to mega-analyses. The machine readability of such electrophysiological event annotations is essential for performing analyses efficiently across software tools and packages. Hierarchical Event Descriptors (HED) provide a framework for describing events in neuroscience experiments. HED library schemas extend the standard HED schema vocabulary to include specialized vocabularies, such as standardized clinical terms for electrophysiological events. The Standardized Computer-based Organized Reporting of EEG (SCORE) defines terms for annotating EEG events, including artifacts. This study developed a HED library schema for SCORE, making the terms machine-readable. We demonstrate that the HED-SCORE library schema can be used to annotate events in EEG data stored in the Brain Imaging Data Structure (BIDS). Clinicians and researchers worldwide can now use the HED-SCORE library schema to annotate and compute on electrophysiological data obtained from the human brain.
Efficient CPU-Optimized Parameter Estimation for Modeling Fish Schooling Behavior in Large Particle Systems
Authors: S. Arabeei, S. Subbey
Date: 2023-10-24 00:00:00
Tasks: Collision Avoidance, CPU, Evolutionary Algorithms, parameter estimation

The schooling behavior of fish can be studied through simulations involving a large number of interacting particles. In such systems, each individual particle is guided by behavior rules, which include aggregation towards a centroid, collision avoidance, and direction alignment. The movement vector of each particle may be expressed as a linear combination of behaviors, with unknown parameters that define a trade-off among several behavioral constraints. A fitness function for collective schooling behavior encompasses all individual particle parameters. For a large number of interacting particles in a complex environment, heuristic methods, such as evolutionary algorithms, are used to optimize the fitness function, ensuring that the resulting decision rule preserves collective behavior. However, these algorithms exhibit slow convergence, making them inefficient in terms of CPU time cost. This paper proposes a CPU-efficient iterative (Cluster, Partition, Refine -- CPR) algorithm for estimating decision rule parameters for a large number of interacting particles. In the first step, we employ the K-Means (unsupervised learning) algorithm to cluster candidate solutions. Then, we partition the search space using Voronoi tessellation over the defined clusters. We assess the quality of each cluster based on the fitness function, with the centroid of their Voronoi cells representing the clusters. Subsequently, we refine the search space by introducing new cells into a number of identified well-fitting Voronoi cells. This process is repeated until convergence. A comparison of the performance of the CPR algorithm with a standard Genetic Algorithm reveals that the former converges faster than the latter. We also demonstrate that the application of the CPR algorithm results in a schooling behavior consistent with empirical observations.
Substantial Doubt Remains about the Efficacy of Anti-Amyloid Antibodies
Authors: Leonardino A. Digma MD, Joseph R. Winer PhD, Michael D. Greicius MD
Date: 2023-10-24 00:00:00

Alzheimer's disease (AD) is a prevalent, progressive, and ultimately fatal neurodegenerative disorder that is defined pathologically by the accumulation of amyloid plaques and tau neurofibrillary tangles in the brain. There remains an unmet need for therapies that can halt or slow the course of AD. To address this need, the FDA has provided a mechanism, under its Accelerated Approval pathway, for potential therapeutics to be approved based in part on their ability to reduce brain amyloid. Through this pathway, two monoclonal anti-amyloid antibodies, aducanumab and lecanemab, have been approved for clinical use. More recently, another amyloid-lowering antibody, donanemab, generated a statistically significant outcome in a phase 3 clinical trial and will shortly come under FDA review. While these monoclonal antibodies are not yet routinely used in clinical practice, the series of recent positive clinical trials has fostered enthusiasm amongst some AD experts. Here, we discuss three key limitations regarding recent anti-amyloid clinical trials: (1) there is little to no evidence that amyloid reduction correlates with clinical outcome, (2) the reported efficacy of anti-amyloid therapies may be partly, or wholly, explained by functional unblinding, and (3) donanemab in its phase 3 trial had no effect on tau burden, the pathological hallmark more closely related to cognition. Taken together, these observations call into question the efficacy of anti-amyloid therapies.
Understanding Code Semantics: An Evaluation of Transformer Models in Summarization
Authors: Debanjan Mondal, Abhilasha Lodha, Ankita Sahoo, Beena Kumari
Date: 2023-10-25 00:00:00
Tasks: Code Summarization

This paper delves into the intricacies of code summarization using advanced transformer-based language models. Through empirical studies, we evaluate the efficacy of code summarization by altering function and variable names to explore whether models truly understand code semantics or merely rely on textual cues. We have also introduced adversaries like dead code and commented code across three programming languages (Python, Javascript, and Java) to further scrutinize the model's understanding. Ultimately, our research aims to offer valuable insights into the inner workings of transformer-based LMs, enhancing their ability to understand code and contributing to more efficient software development practices and maintenance workflows.
From Simple to Complex: A Progressive Framework for Document-level Informative Argument Extraction
Authors: Quzhe Huang, Yanxi Zhang, Dongyan Zhao
Date: 2023-10-25 00:00:00
Tasks: Event Argument Extraction

Document-level Event Argument Extraction (EAE) requires the model to extract arguments of multiple events from a single document. Considering the underlying dependencies between these events, recent efforts leverage the idea of "memory", where the results of already predicted events are cached and can be retrieved to help the prediction of upcoming events. These methods extract events according to their appearance order in the document, however, the event that appears in the first sentence does not mean that it is the easiest to extract. Existing methods might introduce noise to the extraction of upcoming events if they rely on an incorrect prediction of previous events. In order to provide more reliable memory, we propose a simple-to-complex progressive framework for document-level EAE. Specifically, we first calculate the difficulty of each event and then, we conduct the extraction following a simple-to-complex order. In this way, the memory will store the most certain results, and the model could use these reliable sources to help the prediction of more difficult events. Experiments on WikiEvents show that our model outperforms SOTA by 1.4% in F1, indicating the proposed simple-to-complex framework is useful in the EAE task.