pwc-archive/papers-with-abstracts huggingface dataset:
IANS: Intelligibility-aware Null-steering Beamforming for Dual-Microphone Arrays
Authors: Wen-Yuan Ting, Syu-Siang Wang, Yu Tsao, Borching Su
Date: 2023-07-09 00:00:00

Beamforming techniques are popular in speech-related applications due to their effective spatial filtering capabilities. Nonetheless, conventional beamforming techniques generally depend heavily on either the target's direction-of-arrival (DOA), relative transfer function (RTF) or covariance matrix. This paper presents a new approach, the intelligibility-aware null-steering (IANS) beamforming framework, which uses the STOI-Net intelligibility prediction model to improve speech intelligibility without prior knowledge of the speech signal parameters mentioned earlier. The IANS framework combines a null-steering beamformer (NSBF) to generate a set of beamformed outputs, and STOI-Net, to determine the optimal result. Experimental results indicate that IANS can produce intelligibility-enhanced signals using a small dual-microphone array. The results are comparable to those obtained by null-steering beamformers with given knowledge of DOAs.
Model-Based End-to-End Learning for Multi-Target Integrated Sensing and Communication under Hardware Impairments
Authors: José Miguel Mateos-Ramos, Christian Häger, Musa Furkan Keskin, Luc Le Magoarou, Henk Wymeersch
Date: 2023-07-09 00:00:00
Tasks: Integrated sensing and communication, ISAC

We study model-based end-to-end learning in the context of integrated sensing and communication (ISAC) under hardware impairments. Hardware impairments are usually addressed by means of array calibration with a focus on communication performance. However, residual impairments may exist that affect sensing performance. This paper proposes a data-driven framework for mitigating such impairments. A monostatic orthogonal frequency-division multiplexing (OFDM) sensing and multiple-input single-output (MISO) communication scenario is considered, incorporating hardware imperfections at the ISAC transceiver antenna array. We propose a novel differentiable version of the orthogonal matching pursuit (OMP) algorithm that is suitable for multi-target sensing and allows for efficient end-to-end learning of the hardware impairments. Based on the differentiable OMP, we devise two model-based parameterization strategies of the ISAC beamformer and sensing receiver to account for hardware impairments: (i) learning a dictionary of steering vectors for different angles and (ii) learning the parameterized hardware impairments. We carry out a comprehensive performance analysis of the proposed model-based learning approaches and a strong baseline consisting of least-squares beamforming, conventional OMP, and maximum-likelihood symbol detection for communication. Results show that by parameterizing the hardware impairments, learning approaches offer gains in terms of higher detection probability, position estimation accuracy, and lower symbol error rate (SER) compared to the baseline. We demonstrate that learning the parameterized hardware impairments outperforms learning a dictionary of steering vectors, also exhibiting the lowest complexity.
Energy-Efficient Beamforming Design for Integrated Sensing and Communications Systems
Authors: Jiaqi Zou, Songlin Sun, Christos Masouros, Yuanhao Cui, Yafeng Liu, Derrick Wing Kwan Ng
Date: 2023-07-08 00:00:00
Tasks: ISAC

In this paper, we investigate the design of energy-efficient beamforming for an ISAC system, where the transmitted waveform is optimized for joint multi-user communication and target estimation simultaneously. We aim to maximize the system energy efficiency (EE), taking into account the constraints of a maximum transmit power budget, a minimum required signal-to-interference-plus-noise ratio (SINR) for communication, and a maximum tolerable Cramer-Rao bound (CRB) for target estimation. We first consider communication-centric EE maximization. To handle the non-convex fractional objective function, we propose an iterative quadratic-transform-Dinkelbach method, where Schur complement and semi-definite relaxation (SDR) techniques are leveraged to solve the subproblem in each iteration. For the scenarios where sensing is critical, we propose a novel performance metric for characterizing the sensing-centric EE and optimize the metric adopted in the scenario of sensing a point-like target and an extended target. To handle the nonconvexity, we employ the successive convex approximation (SCA) technique to develop an efficient algorithm for approximating the nonconvex problem as a sequence of convex ones. Furthermore, we adopt a Pareto optimization mechanism to articulate the tradeoff between the communication-centric EE and sensing-centric EE. We formulate the search of the Pareto boundary as a constrained optimization problem and propose a computationally efficient algorithm to handle it. Numerical results validate the effectiveness of our proposed algorithms compared with the baseline schemes and the obtained approximate Pareto boundary shows that there is a non-trivial tradeoff between communication-centric EE and sensing-centric EE, where the number of communication users and EE requirements have serious effects on the achievable tradeoff.
New Methods for MLE of Toeplitz Structured Covariance Matrices with Applications to RADAR Problems
Authors: Augusto Aubry, Prabhu Babu, Antonio De Maio, Massimo Rosamilia
Date: 2023-07-08 00:00:00

This work considers Maximum Likelihood Estimation (MLE) of a Toeplitz structured covariance matrix. In this regard, an equivalent reformulation of the MLE problem is introduced and two iterative algorithms are proposed for the optimization of the equivalent statistical learning framework. Both the strategies are based on the Majorization Minimization (MM) paradigm and hence enjoy nice properties such as monotonicity and ensured convergence to a stationary point of the equivalent MLE problem. The proposed framework is also extended to deal with MLE of other practically relevant covariance structures, namely, the banded Toeplitz, block Toeplitz, and Toeplitz-block-Toeplitz. Through numerical simulations, it is shown that the new methods provide excellent performance levels in terms of both mean square estimation error (which is very close to the benchmark Cram\'er-Rao Bound (CRB)) and signal-to-interference-plus-noise ratio, especially in comparison with state of the art strategies.
Social-Mobility-Aware Joint Communication and Computation Resource Management in NOMA-Enabled Vehicular Networks
Authors: Tong Xue, Haixia Zhang, Hui Ding, Dongfeng Yuan
Date: 2023-07-08 00:00:00
Tasks: Edge-computing, Management

The existing computation and communication (2C) optimization schemes for vehicular edge computing (VEC) networks mainly focus on the physical domain without considering the influence from the social domain. This may greatly limit the potential of task offloading, making it difficult to fully boom the task offloading rate with given power, resulting in low energy efficiency (EE). To address the issue, this letter devotes itself to investigate social-mobility-aware VEC framework and proposes a novel EE-oriented 2C assignment scheme. In doing so, we assume that the task vehicular user (T-VU) can offload computation tasks to the service vehicular user (S-VU) and the road side unit (RSU) by non-orthogonal multiple access (NOMA). An optimization problem is formulated to jointly assign the 2C resources to maximize the system EE, which turns out to be a mixed integer non-convex objective function. To solve the problem, we transform it into separated computation and communication resource allocation subproblems. Dealing with the first subproblem, we propose a social-mobility-aware edge server selection and task splitting algorithm (SM-SSTSA) to achieve edge server selection and task splitting. Then, by solving the second subproblem, the power allocation and spectrum assignment solutions are obtained utilizing a tightening lower bound method and a Kuhn-Munkres algorithm. Finally, we solve the original problem through an iterative method. Simulation results demonstrate the superior EE performance of the proposed scheme.
Profiling Of Volatiles In Tissues Of Salacia Reticulata Wight. With Anti-Diabetic Potential Using GC-MS And Molecular Docking
Authors: Anurabh Charkravarty, Gnanam Ramasamy
Date: 2023-07-09 00:00:00
Tasks: Molecular Docking

Type 2 diabetes mellitus is a global pandemic, it is a chronic, progressive and an incompletely understood metabolic condition. The disease is characterized by higher levels of sugar in blood caused either due to insufficient production of insulin or because of insulin resistance. Major drugs used for the treatment of the condition are fraught with side effects. Hence, it is becoming an obligation to gaze at alternative agents showing marginal adverse effects. An important source of such agents are the medicinal plants. Several plants have been positively identified to show anti-diabetic effects. The species Salacia reticulata Wight., belonging to the family Celastraceae which is found in the forests of Southern India is one such promising plant to tackle type 2 diabetics. In this study, numerous volatile compounds were identified from various tissues through GC-MS analysis. Among these the compounds possessed suitable ADMET properties, and high binding affinities were compared with two approved {\alpha}-glucosidase inhibitors, Acarbose and Miglitol. The analysis indicated that the compounds with PubChem IDs, CID-240051 and CID-533471 exhibited potential as inhibitors of Human Maltase-Glucoamylase enzyme.
On Estimating Derivatives of Input Signals in Biochemistry
Authors: Mathieu Hemery, François Fages
Date: 2023-07-10 00:00:00

The online estimation of the derivative of an input signal is widespread in control theory and engineering. In the realm of chemical reaction networks (CRN), this raises however a number of specific issues on the different ways to achieve it. A CRN pattern for implementing a derivative block has already been proposed for the PID control of biochemical processes, and proved correct using Tikhonov's limit theorem. In this paper, we give a detailed mathematical analysis of that CRN, thus clarifying the computed quantity and quantifying the error done as a function of the reaction kinetic parameters. In a synthetic biology perspective, we show how this can be used to design error correcting terms to compute online functions involving derivatives with CRNs. In the systems biology perspective, we give the list of models in BioModels containing (in the sense of subgraph epimorphisms) the core derivative CRN, most of which being models of oscillators and control systems in the cell, and discuss in detail two such examples: one model of the circadian clock and one model of a bistable switch.
Better Research Software Tools to Elevate the Rate of Scientific Discovery -- or why we need to invest in research software engineering
Authors: Joran Deschamps, Damian Dalle Nogare, Florian Jug
Date: 2023-07-08 00:00:00
Tasks: scientific discovery

In the past decade, enormous progress has been made in advancing the state-of-the-art in bioimage analysis - a young computational field that works in close collaboration with the life sciences on the quantitative analysis of scientific image data. In many cases, tremendous effort has been spent to package these new advances into usable software tools and, as a result, users can nowadays routinely apply cutting-edge methods to their analysis problems using software tools such as ilastik [1], cellprofiler [2], Fiji/ImageJ2 [3,4] and its many modern plugins that build on the BigDataViewer ecosystem [5], and many others. Such software tools have now become part of a critical infrastructure for science [6]. Unfortunately, overshadowed by the few exceptions that have had long-lasting impact, many other potentially useful tools fail to find their way into the hands of users. While there are many reasons for this, we believe that at least some of the underlying problems, which we discuss in more detail below, can be mitigated. In this opinion piece, we specifically argue that embedding teams of research software engineers (RSEs) within imaging and image analysis core facilities would be a major step towards sustainable bioimage analysis software.
Identifying metabolites from protein identifiers with P2M
Authors: Christine H. Chang, Bryan J. Killinger, Ryan S. Renslow, Sean M. Colby
Date: 2023-07-07 00:00:00

The identification of metabolites from complex biological samples often involves matching experimental mass spectrometry data to signatures of compounds derived from massive chemical databases. However, misidentifications may result due to the complexity of potential chemical space that leads to databases containing compounds with nearly identical structures. Prior knowledge of compounds that may be enzymatically consumed or produced by an organism can help reduce misidentifications by restricting initial database searching to compounds that are likely to be present in a biological system. While databases such as UniProt allow for the identification of small molecules that may be consumed or generated by enzymes encoded in an organism's genome, currently no tool exists for identifying SMILES strings of metabolites associated with protein identifiers and expanding R-containing substructures to fully defined, biologically relevant chemical structures. Here we present Proteome2Metabolome (P2M), a tool that performs these tasks using external database querying behind a simple command line interface. Beyond mass spectrometry based applications, P2M can be generally used to identify biologically relevant chemical structures likely to be observed in a biological system.
Exploring Dynamic Asset Pricing within Bachelier Market Model
Authors: Nancy Asare Nyarko, Bhathiya Divelgama, Jagdish Gnawali, Blessing Omotade, Svetlozar Rachev, Peter Yegon
Date: 2023-07-08 00:00:00

This paper delves into the dynamics of asset pricing within Bachelier market model, elucidating the representation of risky asset price dynamics and the definition of riskless assets.
dYdX: Liquidity Providers' Incentive Programme Review
Authors: Colin Chan
Date: 2023-07-08 00:00:00
Tasks: Management

Liquidity providers are currently incentivised to provide liquidity through the LP Incentives Programme on dYdX. Based on the various parameters - makerVolume, depths and spreads, they are rewarded accordingly based on their activities. Given the maturity of the BTC and ETH markets, alongside other altcoins which enjoy a consistent amount of liquidity, this paper aims to update the formula to encourage more active and efficient liquidity, improving the overall trading experience. In this research, I begin by providing a basic understanding of spread management, before introducing the methodology with the various metrics and conditions. This includes gathering orderbooks on a minute interval and reconstructing the depths based on historical trades to establish an upper bound. I end off by providing recommendations to update the maxSpread parameter and alternative mechanisms/solutions to improve the existing market structures.
A note on the induction of comonotonic additive risk measures from acceptance sets
Authors: Samuel Solgon Santos, Marlon Ruoso Moresco, Marcelo Brutti Righi, Eduardo de Oliveira Horta
Date: 2023-07-10 00:00:00

We present simple general conditions on the acceptance sets under which their induced monetary risk and deviation measures are comonotonic additive. We show that acceptance sets induce comonotonic additive risk measures if and only if the acceptance sets and their complements are stable under convex combinations of comonotonic random variables. A generalization of this result applies to risk measures that are additive for random variables with a priori specified dependence structures, e.g., perfectly correlated, uncorrelated, or independent random variables.
An analysis of least squares regression and neural networks approximation for the pricing of swing options
Authors: Christian Yeo
Date: 2023-07-10 00:00:00
Tasks: regression

Least Squares regression was first introduced for the pricing of American-style options, but it has since been expanded to include swing options pricing. The swing options price may be viewed as a solution to a Backward Dynamic Programming Principle, which involves a conditional expectation known as the continuation value. The approximation of the continuation value using least squares regression involves two levels of approximation. First, the continuation value is replaced by an orthogonal projection over a subspace spanned by a finite set of $m$ squared-integrable functions (regression functions) yielding a first approximation $V^m$ of the swing value function. In this paper, we prove that, with well-chosen regression functions, $V^m$ converges to the swing actual price $V$ as $m \to + \infty$. A similar result is proved when the regression functions are replaced by neural networks. For both methods (least squares or neural networks), we analyze the second level of approximation involving practical computation of the swing price using Monte Carlo simulations and yielding an approximation $V^{m, N}$ (where $N$ denotes the Monte Carlo sample size). Especially, we prove that $V^{m, N} \to V^m$ as $N \to + \infty$ for both methods and using Hilbert basis in the least squares regression. Besides, a convergence rate of order $\mathcal{O}\big(\frac{1}{\sqrt{N}} \big)$ is proved in the least squares case. Several convergence results in this paper are based on the continuity of the swing value function with respect to cumulative consumption, which is also proved in the paper and has not been yet explored in the literature before for the best of our knowledge.
Importance Sampling for Minimization of Tail Risks: A Tutorial
Authors: Anand Deo, Karthyek Murthy
Date: 2023-07-10 00:00:00
Tasks: Stochastic Optimization

This paper provides an introductory overview of how one may employ importance sampling effectively as a tool for solving stochastic optimization formulations incorporating tail risk measures such as Conditional Value-at-Risk. Approximating the tail risk measure by its sample average approximation, while appealing due to its simplicity and universality in use, requires a large number of samples to be able to arrive at risk-minimizing decisions with high confidence. This is primarily due to the rarity with which the relevant tail events get observed in the samples. In simulation, Importance Sampling is among the most prominent methods for substantially reducing the sample requirement while estimating probabilities of rare events. Can importance sampling be used for optimization as well? If so, what are the ingredients required for making importance sampling an effective tool for optimization formulations involving rare events? This tutorial aims to provide an introductory overview of the two key ingredients in this regard, namely, (i) how one may arrive at an importance sampling change of measure prescription at every decision, and (ii) the prominent techniques available for integrating such a prescription within a solution paradigm for stochastic optimization formulations.
Replicate immunosequencing as a robust probe of B cell repertoire diversity
Authors: William DeWitt, Paul Lindau, Thomas Snyder, Marissa Vignali, Ryan Emerson, Harlan Robins
Date: 2014-10-01 00:00:00
Tasks: Diversity, Experimental Design

Fundamental to quantitative characterization of the B cell receptor repertoire is clonal diversity - the number of distinct somatically recombined receptors present in the repertoire and their relative abundances, defining the search space available for immune response. This study synthesizes flow cytometry and immunosequencing to study memory and naive B cells from the peripheral blood of three adults. A combinatorial experimental design was employed, constituting a sample abundance probe robust to amplification stochasticity, a crucial quantitative advance over previous sequencing studies of diversity. These data are leveraged to interrogate repertoire diversity, motivating an extension of a canonical diversity model in ecology and corpus linguistics. Maximum likelihood diversity estimates are provided for memory and naive B cell repertoires. Both evince domination by rare clones and regimes of power law scaling in abundance. Memory clones have more disparate repertoire abundances than naive clones, and most naive clones undergo no proliferation prior to antigen recognition.
Empowering recommender systems using automatically generated Knowledge Graphs and Reinforcement Learning
Authors: Ghanshyam Verma, Shovon Sengupta, Simon Simanta, Huan Chen, Janos A. Perge, Devishree Pillai, John P. McCrae, Paul Buitelaar
Date: 2023-07-11 00:00:00
Tasks: Decision Making, Knowledge Graphs, Management, Marketing, Recommendation Systems, reinforcement-learning, Reinforcement Learning (RL)

Personalized recommender systems play a crucial role in direct marketing, particularly in financial services, where delivering relevant content can enhance customer engagement and promote informed decision-making. This study explores interpretable knowledge graph (KG)-based recommender systems by proposing two distinct approaches for personalized article recommendations within a multinational financial services firm. The first approach leverages Reinforcement Learning (RL) to traverse a KG constructed from both structured (tabular) and unstructured (textual) data, enabling interpretability through Path Directed Reasoning (PDR). The second approach employs the XGBoost algorithm, with post-hoc explainability techniques such as SHAP and ELI5 to enhance transparency. By integrating machine learning with automatically generated KGs, our methods not only improve recommendation accuracy but also provide interpretable insights, facilitating more informed decision-making in customer relationship management.
Bio-Inspired Night Image Enhancement Based on Contrast Enhancement and Denoising
Authors: Xinyi Bai, Steffi Agino Priyanka, Hsiao-Jung Tung, Yuankai Wang
Date: 2023-07-11 00:00:00
Tasks: Denoising, Image Enhancement, object-detection, Object Detection

Due to the low accuracy of object detection and recognition in many intelligent surveillance systems at nighttime, the quality of night images is crucial. Compared with the corresponding daytime image, nighttime image is characterized as low brightness, low contrast and high noise. In this paper, a bio-inspired image enhancement algorithm is proposed to convert a low illuminance image to a brighter and clear one. Different from existing bio-inspired algorithm, the proposed method doesn't use any training sequences, we depend on a novel chain of contrast enhancement and denoising algorithms without using any forms of recursive functions. Our method can largely improve the brightness and contrast of night images, besides, suppress noise. Then we implement on real experiment, and simulation experiment to test our algorithms. Both results show the advantages of proposed algorithm over contrast pair, Meylan and Retinex.
ISLTranslate: Dataset for Translating Indian Sign Language
Authors: Abhinav Joshi, Susmit Agrawal, Ashutosh Modi
Date: 2023-07-11 00:00:00
Tasks: Sentence, Sign Language Translation, Translation

Sign languages are the primary means of communication for many hard-of-hearing people worldwide. Recently, to bridge the communication gap between the hard-of-hearing community and the rest of the population, several sign language translation datasets have been proposed to enable the development of statistical sign language translation systems. However, there is a dearth of sign language resources for the Indian sign language. This resource paper introduces ISLTranslate, a translation dataset for continuous Indian Sign Language (ISL) consisting of 31k ISL-English sentence/phrase pairs. To the best of our knowledge, it is the largest translation dataset for continuous Indian Sign Language. We provide a detailed analysis of the dataset. To validate the performance of existing end-to-end Sign language to spoken language translation systems, we benchmark the created dataset with a transformer-based model for ISL translation.