pwc-archive/papers-with-abstracts huggingface dataset:
TaQ-DiT: Time-aware Quantization for Diffusion Transformers
Authors: Xinyan Liu, Huihong Shi, Yang Xu, Zhongfeng Wang
Date: 2024-11-21 00:00:00
Tasks: Denoising, Model Compression, Quantization, Video Generation

Transformer-based diffusion models, dubbed Diffusion Transformers (DiTs), have achieved state-of-the-art performance in image and video generation tasks. However, their large model size and slow inference speed limit their practical applications, calling for model compression methods such as quantization. Unfortunately, existing DiT quantization methods overlook (1) the impact of reconstruction and (2) the varying quantization sensitivities across different layers, which hinder their achievable performance. To tackle these issues, we propose innovative time-aware quantization for DiTs (TaQ-DiT). Specifically, (1) we observe a non-convergence issue when reconstructing weights and activations separately during quantization and introduce a joint reconstruction method to resolve this problem. (2) We discover that Post-GELU activations are particularly sensitive to quantization due to their significant variability across different denoising steps as well as extreme asymmetries and variations within each step. To address this, we propose time-variance-aware transformations to facilitate more effective quantization. Experimental results show that when quantizing DiTs' weights to 4-bit and activations to 8-bit (W4A8), our method significantly surpasses previous quantization methods.
Spiking neural networks: Towards bio-inspired multimodal perception in robotics
Authors: Katerina Maria Oikonomou, Vasiliki Balaska, Konstantinos A. Tsintotas, Christos N. Mavridis, Ioannis Kansizoglou, Antonios Gasteratos
Date: 2024-11-21 00:00:00

Spiking neural networks (SNNs) have captured apparent interest over the recent years, stemming from neuroscience and reaching the field of artificial intelligence. However, due to their nature SNNs remain far behind in achieving the exceptional performance of deep neural networks (DNNs). As a result, many scholars are exploring ways to enhance SNNs by using learning techniques from DNNs. While this approach has been proven to achieve considerable improvements in SNN performance, we propose another perspective: enhancing the biological plausibility of the models to leverage the advantages of SNNs fully. Our approach aims to propose a brain-like combination of audio-visual signal processing for recognition tasks, intended to succeed in more bio-plausible human-robot interaction applications.
Global and Local Attention-Based Transformer for Hyperspectral Image Change Detection
Authors: Ziyi Wang, Feng Gao, Junyu Dong, Qian Du
Date: 2024-11-21 00:00:00
Tasks: Change Detection

Recently Transformer-based hyperspectral image (HSI) change detection methods have shown remarkable performance. Nevertheless, existing attention mechanisms in Transformers have limitations in local feature representation. To address this issue, we propose Global and Local Attention-based Transformer (GLAFormer), which incorporates a global and local attention module (GLAM) to combine high-frequency and low-frequency signals. Furthermore, we introduce a cross-gating mechanism, called cross-gated feed-forward network (CGFN), to emphasize salient features and suppress noise interference. Specifically, the GLAM splits attention heads into global and local attention components to capture comprehensive spatial-spectral features. The global attention component employs global attention on downsampled feature maps to capture low-frequency information, while the local attention component focuses on high-frequency details using non-overlapping window-based local attention. The CGFN enhances the feature representation via convolutions and cross-gating mechanism in parallel paths. The proposed GLAFormer is evaluated on three HSI datasets. The results demonstrate its superiority over state-of-the-art HSI change detection methods. The source code of GLAFormer is available at \url{https://github.com/summitgao/GLAFormer}.
Integrated Positioning and Communication via LEO Satellites: Opportunities and Challenges
Authors: Jie Ma, Pinjun Zheng, Xing Liu, Yuchen Zhang, Tareq Y. Al-Naffouri
Date: 2024-11-21 00:00:00

Low Earth orbit (LEO) satellites, as a prominent technology in the 6G non-terrestrial network, offer both positioning and communication capabilities. While these two applications have each been extensively studied and have achieved substantial progress in recent years, the potential synergistic benefits of integrating them remain an underexplored yet promising avenue. This article comprehensively analyzes the integrated positioning and communication (IPAC) systems on LEO satellites. By leveraging the distinct characteristics of LEO satellites, we examine how communication systems can enhance positioning accuracy and, conversely, how positioning information can be exploited to improve communication efficiency. In particular, we present two case studies to illustrate the potential of such integration. Finally, several key open research challenges in the LEO-based IPAC systems are discussed.
Universal Scanning GUI Tool for Available and Usable TV White Space (TVWS) Spectrum
Authors: Muneer Al-ZuBi, Mohamed-Slim Alouini
Date: 2024-11-21 00:00:00

In this era of advanced communication technologies, many remote rural and hard-to-reach areas still lack Internet access due to technological, geographical, and economic challenges. The TV white space (TVWS) technology has proven to be effective and feasible in connecting these areas to Internet service in many parts of the world. The TVWS-based systems operate based on geolocation white space databases (WSDB) to protect the primary systems from harmful interference and thus there is a critical need to know the available and usable channels that can be used by the secondary white space devices (WSDs) in a specific geographic area. In this work, we developed a generalized and flexible graphical user interface (GUI) tool to evaluate the availability and usability of the TVWS channels and their noise levels at each geographic location within the analyzed area. The developed tool has many features and capabilities such as allowing the users to scan the TVWS spectrum for any geographic area in the world and any frequency band in the TVWS spectrum. Moreover, it allows the user to apply widely used terrain-based radio propagation models. It provides the flexibility to import the elevation terrain profile of any region with the desired spatial accuracy and resolution. In addition, various system parameters including those related to regulation rules can be modified in the tool. This tool exports to an external dataset file the output data of the available and usable TVWS channels and their noise levels and it also visualizes these data interactively.
Semi-blind Channel Estimation for Massive MIMO LEO Satellite Communications
Authors: Abdollah Masoud Darya, Saeed Abdallah
Date: 2024-11-21 00:00:00

This letter proposes decision-directed semi-blind channel estimation for massive multiple-input multiple-output low-Earth-orbit satellite communications. Two semi-blind estimators are proposed. The first utilizes detected data symbols in addition to pilot symbols. The second, a modified semi-blind estimator, is specially designed to mitigate the channel-aging effect caused by the highly dynamic nature of low-Earth-orbit satellite communication channels -- an issue that adversely impacts the performance of pilot-based estimators. Consequently, this modified estimator outperforms an optimal pilot-based estimator in terms of normalized mean square error and achieves symbol error rate performance comparable to that of a Genie-aided (perfectly known channel) detector. The trade-offs between the proposed estimators are also examined.
Implementation of tools for lessening the influence of artifacts in EEG signal analysis
Authors: Mario Molina-Molina, Lorenzo J. Tardon, Ana M. Barbancho, Isabel Barbancho
Date: 2024-11-21 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

This manuscript describes and implementation of scripts of code aimed at reducing the influence of artifacts, specifically focused on ocular artifacts, in the measurement and processing of electroencephalogram (EEG) signals. This process is of importance because it benefits the analysis and study of long trial samples when the appearance of ocular artifacts cannot be avoided by simply discarding trials. The implementations provided to the reader illustrate, with slight modifications, previously proposed methods aimed at the partial or complete elimination of EEG channels or components are those that resemble the electro-oculogram (EOG) signals in which artifacts are detected. In addition to the description of each of the provided functions, examples of utilization and illustrative figures will be included to show the expected results and processing pipeline.
Which Channel in 6G, Low-rank or Full-rank, more needs RIS from a Perspective of DoF?
Authors: Feng Shu, Maolin Li, Ke Yang, Bin Deng
Date: 2024-11-21 00:00:00
Tasks: Diversity

Reconfigurable intelligent surface (RIS), as an efficient tool to improve receive signal-to-noise ratio, extend coverage and create more spatial diversity, is viewed as a most promising technique for the future wireless networks like 6G. As you know, RIS is very suitable for a special wireless scenario with wireless link between BS and users being completely blocked, i.e., no link. In this paper, we extend its applications to a general scenario, i.e., rank-deficient channel, particularly some extremely low-rank ones such as no link, and line-of-sight (LoS, rank-one). Actually, there are several potential important low-rank applications like low-altitude, satellite, UAV, marine, and deep-space communications. In such a situation, it is found that RIS may make a dramatic degrees of freedom (DoF) enhancement over no RIS. By using a distributed RISs placement, the DoF of channel from BS to user in LoS channel may be even boosted from a low-rank like 0/1 to full-rank. This will achieve an extremely rate improvement via spatial parallel multiple-stream transmission from BS to user. In this paper, we present a complete review of making an in-depth discussion on DoF effect of RIS.
Optimal portfolio under ratio-type periodic evaluation in stochastic factor models under convex trading constraints
Authors: Wenyuan Wang, Kaixin Yan, Xiang Yu
Date: 2024-11-15 00:00:00

This paper studies a type of periodic utility maximization problems for portfolio management in incomplete stochastic factor models with convex trading constraints. The portfolio performance is periodically evaluated on the relative ratio of two adjacent wealth levels over an infinite horizon, featuring the dynamic adjustments in portfolio decision according to past achievements. Under power utility, we transform the original infinite horizon optimal control problem into an auxiliary terminal wealth optimization problem under a modified utility function. To cope with the convex trading constraints, we further introduce an auxiliary unconstrained optimization problem in a modified market model and develop the martingale duality approach to establish the existence of the dual minimizer such that the optimal unconstrained wealth process can be obtained using the dual representation. With the help of the duality results in the auxiliary problems, the relationship between the constrained and unconstrained models as well as some fixed point arguments, we finally derive and verify the optimal constrained portfolio process in a periodic manner for the original problem over an infinite horizon.
Machine learning framework to predict the performance of lipid nanoparticles for nucleic acid delivery
Authors: Gaurav Kumar, Arezoo M. Ardekani
Date: 2024-11-21 00:00:00

Lipid nanoparticles (LNPs) are highly effective carriers for gene therapies, including mRNA and siRNA delivery, due to their ability to transport nucleic acids across biological membranes, low cytotoxicity, improved pharmacokinetics, and scalability. A typical approach to formulate LNPs is to establish a quantitative structure-activity relationship (QSAR) between their compositions and in vitro/in vivo activities which allows for the prediction of activity based on molecular structure. However, developing QSAR for LNPs can be challenging due to the complexity of multi-component formulations, interactions with biological membranes, and stability in physiological environments. To address these challenges, we developed a machine learning framework to predict the activity and cell viability of LNPs for nucleic acid delivery. We curated data from 6,398 LNP formulations in the literature, applied nine featurization techniques to extract chemical information, and trained five machine learning models for binary and multiclass classification. Our binary models achieved over 90% accuracy, while the multiclass models reached over 95% accuracy. Our results demonstrated that molecular descriptors, particularly when used with random forest and gradient boosting models, provided the most accurate predictions. Our findings also emphasized the need for large training datasets and comprehensive LNP composition details, such as constituent structures, molar ratios, nucleic acid types, and dosages, to enhance predictive performance.
Interpretable QSPR Modeling using Recursive Feature Machines and Multi-scale Fingerprints
Authors: Jiaxuan Shen, HaiTao Zhang, Yunjie Wang, Yilong Wang, Song Tao, Bo Qiu, Ng Shyh-Chang
Date: 2024-11-21 00:00:00
Tasks: Feature Importance

This study pioneers the application of Recursive Feature Machines (RFM) in QSPR modeling, introducing a tailored feature importance analysis approach to enhance interpretability. By leveraging deep feature learning through AGOP, RFM achieves state-of-the-art (SOTA) results in predicting molecular properties, as demonstrated through solubility prediction across nine benchmark datasets. To capture a wide array of structural information, we employ diverse molecular representations, including MACCS keys, Morgan fingerprints, and a custom multi-scale hybrid fingerprint (HF) derived from global descriptors and SMILES local fragmentation techniques. Notably, the HF offers significant advantages over MACCS and Morgan fingerprints in revealing structural determinants of molecular properties. The feature importance analysis in RFM provides robust local and global explanations, effectively identifying structural features that drive molecular behavior and offering valuable insights for drug development. Additionally, RFM demonstrates strong redundancy-filtering abilities, as model performance remains stable even after removing redundant features within custom fingerprints. Importantly, RFM introduces the deep feature learning capabilities of the average gradient outer product (AGOP) matrix into ultra-fast kernel machine learning, to imbue kernel machines with interpretable deep feature learning capabilities. We extend this approach beyond the Laplace Kernel to the Matern, Rational Quadratic, and Gaussian kernels, to find that the Matern and Laplace kernels deliver the best performance, thus reinforcing the flexibility and effectiveness of AGOP in RFM. Experimental results show that RFM-HF surpasses both traditional machine learning models and advanced graph neural networks.
Adaptive flexibility of cells through nonequilibrium entropy production
Authors: Yuika Ueda, Shinji Deguchi
Date: 2024-11-21 00:00:00

Cellular adaptation to environmental changes relies on the dynamic remodeling of subcellular structures. Among these, sarcomere structures are fundamental to the organization and function of the cytoskeletal architecture. In muscle-type cells, sarcomeres exhibit ordered structures of consistent lengths, optimized for stable force generation. By contrast, nonmuscle-type cells display a higher degree of structural variability, with sarcomeres of varying lengths that contribute not only to force generation but also to adaptive remodeling upon environmental cues. While these differences in sarcomere structures have traditionally been attributed to the unique properties of specific proteins expressed in each cell type, the functional implications of such structural variability remain unclear. Here, we present a nonequilibrium physics framework to elucidate the role of sarcomere variability in cytoskeletal adaptation. Specifically, we demonstrate that the effective binding strength of sarcomere components can be evaluated by analyzing structural randomness using Shannon entropy. The increased entropy associated with the inherent randomness of sarcomere structures in nonmuscle-type cells lowers the energy barrier for cytoskeletal remodeling, enabling flexible adaptation to environmental demands. Meanwhile, the ordered sarcomere arrangements in muscle-type cells correspond to higher binding energies and more stable cytoskeletal configurations. Although structural disorder is often regarded as unfavorable in terms of stability, our study suggests that it plays a key role in enabling adaptive responses in cellular systems.
Evidence of epigenetic oncogenesis: a turning point in cancer research
Authors: Jean-Pascal Capp, Benoît Aliaga, Vera Pancaldi
Date: 2024-11-21 00:00:00

In cancer research, the term epigenetics was used in the 1970s in its modern sense encompassing non-genetic events modifying the chromatin state, mainly to oppose the emerging oncogene paradigm. However, starting from the establishment of this prominent concept, the importance of these epigenetic phenomena in cancer rarely led to questioning the causal role of genetic alterations. Only in the last 10 years, the accumulation of problematic data, better experimental technologies, and some ambitious models pushed the idea that epigenetics could be at least as important as genetics in early oncogenesis. Until this year, a direct demonstration of epigenetic oncogenesis was still lacking. Now Parreno, Cavalli and colleagues, using a refined experimental model in the fruit fly Drosophila melanogaster, enforced the initiation of tumours solely by imposing a transient loss of Polycomb repression, leading to a purely epigenetic oncogenesis phenomenon. Despite a few caveats that we discuss, this pioneering work represents a major breakpoint in cancer research that leads us to consider the theoretical and conceptual implications on oncogenesis and to search for links between this artificial experimental model and naturally occurring processes, while revisiting cancer theories that were previously proposed as alternatives to the oncogene-centered paradigm.
gggenomes: effective and versatile visualizations for comparative genomics
Authors: Thomas Hackl, Markus Ankenbrand, Bart van Adrichem, David Wilkins, Kristina Haslinger
Date: 2024-11-05 00:00:00

The effective visualization of genomic data is crucial for exploring and interpreting complex relationships within and across genes and genomes. Despite advances in developing dedicated bioinformatics software, common visualization tools often fail to efficiently integrate the diverse datasets produced in comparative genomics, lack intuitive interfaces to construct complex plots and are missing functionalities to inspect the underlying data iteratively and at scale. Here, we introduce gggenomes, a versatile R package designed to overcome these challenges by extending the widely used ggplot2 framework for comparative genomics. gggenomes is available from CRAN and GitHub, accompanied by detailed and user-friendly documentation (https://thackl.github.io/gggenomes).
Inward rectifier potassium channels interact with calcium channels to promote robust and physiological bistability
Authors: Anaëlle De Worm, Guillaume Drion, Pierre Sacré
Date: 2024-11-21 00:00:00

In the dorsal horn, projection neurons play a crucial role in pain processing by transmitting sensory stimuli to supraspinal centers during nociception. Following exposure to intense noxious stimuli, a sensitization process occurs that alters the functional state of the dorsal horn. Notably, projection neurons can undergo a switch in firing pattern -- from tonic firing to plateau potentials with sustained afterdischarges. For afterdischarges to occur following this switch, the neuron must exhibit bistability, defined as the ability to exhibit resting and spiking states at the same input current depending on the prior context. In many cases, neuronal bistability arises through the activity of voltage-gated calcium channels. However, computational studies have shown a trade-off between bistability and the plausibility of resting states when calcium channels are counterbalanced by voltage-gated potassium channels. Despite this, the mechanisms underlying the emergence of robust bistability, plateau potentials, and sustained afterdischarges through calcium channels remain poorly understood. In this study, we used a conductance-based model to investigate how L-type calcium (CaL) channels can give rise to bistability when combined with either M-type potassium (KM) channels or inward-rectifying potassium (Kir) channels. Unlike KM channels, Kir channels enhance bistability. When combined with CaL channels, KM and Kir channels promote distinct forms of bistability, differing in both robustness and functional implications. An analysis of their inward and outward current properties revealed that these differences stem from their distinct steady-state current profiles. Altogether, the complementarity between CaL and Kir channels provides a robust mechanism for central sensitization in the dorsal horn.
Re-examining aggregation in the Tallis-Leyton model of parasite acquisition
Authors: R. McVinish
Date: 2024-11-21 00:00:00

The Tallis-Leyton model is a simple model of parasite acquisition where there is no interaction between the host and the acquired parasites. We examine the effect of model parameters on the distribution of the host's parasite burden in the sense of the Lorenz order. This fits with an alternate view of parasite aggregation that has become widely used in empirical studies but is rarely used in the analysis of mathematical models of parasite acquisition.
Finding the nonnegative minimal solutions of Cauchy PDEs in a volatility-stabilized market
Authors: Nicole Tianjiao Yang, Tomoyuki Ichiba
Date: 2024-11-06 00:00:00

The strong relative arbitrage problem in Stochastic Portfolio Theory seeks an investment strategy that almost surely outperforms a benchmark portfolio at the end of a given time horizon. The highest relative return in relative arbitrage opportunities is characterized by the smallest nonnegative continuous solution of a Cauchy problem for a partial differential equation (PDE). However, solving this type of PDE poses analytical and numerical challenges, due to the high dimensionality and its non-unique solutions. In this paper, we discuss numerical methods to address the relative arbitrage problem and the associated PDE in a volatility-stabilized market, using time-changed Bessel bridges. We present a practical algorithm and demonstrate numerical results through an example in volatility-stabilized markets.
The Value of Information from Sell-side Analysts
Authors: Linying Lv
Date: 2024-11-21 00:00:00

I examine the value of information from sell-side analysts by analyzing a large corpus of their written reports. Using embeddings from state-of-the-art large language models, I show that qualitative information in analyst reports explains above 10% of contemporaneous stock returns out-of-sample, a value that is more economically significant than quantitative forecasts. I then perform a Shapley value decomposition to assess how much each topic within the reports contributes to explaining stock returns. The results show that analysts' income statement analyses account for more than half of the reports' explanatory power. Expressing these findings in economic terms, I estimate that early acquisition of analyst reports can yield significant profits. Analyst information value peaks in the first week following earnings announcements, highlighting their vital role in interpreting new financial data.