pwc-archive/papers-with-abstracts huggingface dataset:
Runtime Construction of Large-Scale Spiking Neuronal Network Models on GPU Devices
Authors: Bruno Golosio, Jose Villamar, Gianmarco Tiddia, Elena Pastorelli, Jonas Stapmanns, Viviana Fanti, Pier Stanislao Paolucci, Abigail Morrison, Johanna Senk
Date: 2023-06-16 00:00:00
Tasks: Code Generation, GPU

Simulation speed matters for neuroscientific research: this includes not only how quickly the simulated model time of a large-scale spiking neuronal network progresses, but also how long it takes to instantiate the network model in computer memory. On the hardware side, acceleration via highly parallel GPUs is being increasingly utilized. On the software side, code generation approaches ensure highly optimized code, at the expense of repeated code regeneration and recompilation after modifications to the network model. Aiming for a greater flexibility with respect to iterative model changes, here we propose a new method for creating network connections interactively, dynamically, and directly in GPU memory through a set of commonly used high-level connection rules. We validate the simulation performance with both consumer and data center GPUs on two neuroscientifically relevant models: a cortical microcircuit of about 77,000 leaky-integrate-and-fire neuron models and 300 million static synapses, and a two-population network recurrently connected using a variety of connection rules. With our proposed ad hoc network instantiation, both network construction and simulation times are comparable or shorter than those obtained with other state-of-the-art simulation technologies, while still meeting the flexibility demands of explorative network modeling.
On Evolvability and Behavior Landscapes in Neuroevolutionary Divergent Search
Authors: Bruno Gašperov, Marko Đurasević
Date: 2023-06-16 00:00:00
Tasks: Diversity

Evolvability refers to the ability of an individual genotype (solution) to produce offspring with mutually diverse phenotypes. Recent research has demonstrated that divergent search methods, particularly novelty search, promote evolvability by implicitly creating selective pressure for it. The main objective of this paper is to provide a novel perspective on the relationship between neuroevolutionary divergent search and evolvability. In order to achieve this, several types of walks from the literature on fitness landscape analysis are first adapted to this context. Subsequently, the interplay between neuroevolutionary divergent search and evolvability under varying amounts of evolutionary pressure and under different diversity metrics is investigated. To this end, experiments are performed on Fetch Pick and Place, a robotic arm task. Moreover, the performed study in particular sheds light on the structure of the genotype-phenotype mapping (the behavior landscape). Finally, a novel definition of evolvability that takes into account the evolvability of offspring and is appropriate for use with discretized behavior spaces is proposed, together with a Markov-chain-based estimation method for it.
Regression-based Physics Informed Neural Networks (Reg-PINNs) for Magnetopause Tracking
Authors: Po-Han Hou, Sung-Chi Hsieh
Date: 2023-06-16 00:00:00
Tasks: Position, regression

Previous research in the scientific field has utilized statistical empirical models and machine learning to address fitting challenges. While empirical models have the advantage of numerical generalization, they often sacrifice accuracy. However, conventional machine learning methods can achieve high precision but may lack the desired generalization. The article introduces a Regression-based Physics-Informed Neural Networks (Reg-PINNs), which embeds physics-inspired empirical models into the neural network's loss function, thereby combining the benefits of generalization and high accuracy. The study validates the proposed method using the magnetopause boundary location as the target and explores the feasibility of methods including Shue et al. [1998], a data overfitting model, a fully-connected networks, Reg-PINNs with Shue's model, and Reg-PINNs with the overfitting model. Compared to Shue's model, this technique achieves approximately a 30% reduction in RMSE, presenting a proof-of-concept improved solution for the scientific community.
Modular Controllers Facilitate the Co-Optimization of Morphology and Control in Soft Robots
Authors: Alican Mertan, Nick Cheney
Date: 2023-06-12 00:00:00

Soft robotics is a rapidly growing area of robotics research that would benefit greatly from design automation, given the challenges of manually engineering complex, compliant, and generally non-intuitive robot body plans and behaviors. It has been suggested that a major hurdle currently limiting soft robot brain-body co-optimization is the fragile specialization between a robot's controller and the particular body plan it controls, resulting in premature convergence. Here we posit that modular controllers are more robust to changes to a robot's body plan. We demonstrate a decreased reduction in locomotion performance after morphological mutations to soft robots with modular controllers, relative to those with similar global controllers - leading to fitter offspring. Moreover, we show that the increased transferability of modular controllers to similar body plans enables more effective brain-body co-optimization of soft robots, resulting in an increased rate of positive morphological mutations and higher overall performance of evolved robots. We hope that this work helps provide specific methods to improve soft robot design automation in this particular setting, while also providing evidence to support our understanding of the challenges of brain-body co-optimization more generally.
Geometric-Based Pruning Rules For Change Point Detection in Multiple Independent Time Series
Authors: Liudmila Pishchagina, Guillem Rigaill, Vincent Runge
Date: 2023-06-15 00:00:00
Tasks: Change Point Detection, Computational Efficiency, Time Series

We consider the problem of detecting multiple changes in multiple independent time series. The search for the best segmentation can be expressed as a minimization problem over a given cost function. We focus on dynamic programming algorithms that solve this problem exactly. When the number of changes is proportional to data length, an inequality-based pruning rule encoded in the PELT algorithm leads to a linear time complexity. Another type of pruning, called functional pruning, gives a close-to-linear time complexity whatever the number of changes, but only for the analysis of univariate time series. We propose a few extensions of functional pruning for multiple independent time series based on the use of simple geometric shapes (balls and hyperrectangles). We focus on the Gaussian case, but some of our rules can be easily extended to the exponential family. In a simulation study we compare the computational efficiency of different geometric-based pruning rules. We show that for small dimensions (2, 3, 4) some of them ran significantly faster than inequality-based approaches in particular when the underlying number of changes is small compared to the data length.
GRM: Generative Relevance Modeling Using Relevance-Aware Sample Estimation for Document Retrieval
Authors: Iain Mackie, Ivan Sekulic, Shubham Chatterjee, Jeffrey Dalton, Fabio Crestani
Date: 2023-06-16 00:00:00
Tasks: Document Ranking, Retrieval

Recent studies show that Generative Relevance Feedback (GRF), using text generated by Large Language Models (LLMs), can enhance the effectiveness of query expansion. However, LLMs can generate irrelevant information that harms retrieval effectiveness. To address this, we propose Generative Relevance Modeling (GRM) that uses Relevance-Aware Sample Estimation (RASE) for more accurate weighting of expansion terms. Specifically, we identify similar real documents for each generated document and use a neural re-ranker to estimate their relevance. Experiments on three standard document ranking benchmarks show that GRM improves MAP by 6-9% and R@1k by 2-4%, surpassing previous methods.
Smart Sentiment Analysis-based Search Engine Classification Intelligence
Authors: Mike Nkongolo
Date: 2023-06-16 00:00:00
Tasks: Articles, Classification, Clustering, Sentiment Analysis

Search engines are widely used for finding information on the internet. However, there are limitations in the current search approach, such as providing popular but not necessarily relevant results. This research addresses the issue of polysemy in search results by implementing a search function that determines the sentimentality of the retrieved information. The study utilizes a web crawler to collect data from the British Broadcasting Corporation (BBC) news site, and the sentimentality of the news articles is determined using the Sentistrength program. The results demonstrate that the proposed search function improves recall value while accurately retrieving nonpolysemous news. Furthermore, Sentistrength outperforms deep learning and clustering methods in classifying search results. The methodology presented in this article can be applied to analyze the sentimentality and reputation of entities on the internet.
Personalized Summarization of Scientific Scholarly Texts
Authors: Alka Khurana, Vasudha Bhatnagar, Vikas Kumar
Date: 2023-06-16 00:00:00
Tasks: Articles, Sentence

In this paper, we present a proposal for an unsupervised algorithm, P-Summ, that generates an extractive summary of scientific scholarly text to meet the personal knowledge needs of the user. The method delves into the latent semantic space of the document exposed by Weighted Non-negative Matrix Factorization, and scores sentences in consonance with the knowledge needs of the user. The novelty of the algorithm lies in its ability to include desired knowledge and eliminate unwanted knowledge in the personal summary. We also propose a multi-granular evaluation framework, which assesses the quality of generated personal summaries at three levels of granularity - sentence, terms and semantic. The framework uses system generated generic summary instead of human generated summary as gold standard for evaluating the quality of personal summary generated by the algorithm. The effectiveness of the algorithm at the semantic level is evaluated by taking into account the reference summary and the knowledge signals. We evaluate the performance of P-Summ algorithm over four data-sets consisting of scientific articles. Our empirical investigations reveal that the proposed method has the capability to meet negative (or positive) knowledge preferences of the user.
Contrastive-Regularized U-Net for Video Anomaly Detection
Authors: Kian Yu Gan, Yu Tong Cheng, Hung-Khoon Tan, Hui-Fuang Ng, Maylor Karhang Leung, Joon Huang Chuah
Date: 2023-04-11 00:00:00
Proceeding: IEEE Access 2023 4
Tasks: Anomaly Detection, Anomaly Detection In Surveillance Videos, Decoder, Video Anomaly Detection

Video anomaly detection aims to identify anomalous segments in a video. It is typically trained with weakly supervised video-level labels. This paper focuses on two crucial factors affecting the performance of video anomaly detection models. First, we explore how to capture the local and global temporal dependencies more effectively. Previous architectures are effective at capturing either local and global information, but not both. We propose to employ a U-Net like structure to model both types of dependencies in a unified structure where the encoder learns global dependencies hierarchically on top of local ones; then the decoder propagates this global information back to the segment level for classification. Second, overfitting is a non-trivial issue for video anomaly detection due to limited training data. We propose weakly supervised contrastive regularization which adopts a feature-based approach to regularize the network. Contrastive regularization learns more generalizable features by enforcing inter-class separability and intra-class compactness. Extensive experiments on the UCF-Crime dataset shows that our approach outperforms several state-of-the-art methods.
Automated Formulaic Alpha Generation for Quantitative Investing using Evolutionary Algorithms
Authors: Zhao Meng, Prof. Dr. Roger Wattenhofer
Date: 2022-03-13 00:00:00
Proceeding: 2022 2022 3
Tasks: Evolutionary Algorithms

A cosmic ray consists of mostly highly energetic protons that emanate from the
sun, the Milky Way and distant galaxies. By colliding with particles in our atmosphere they trigger a chain reaction that leads to so called cosmic-ray showers
of lower energetic particles like pions [1]. In modern biology these are held responsible for inducing the random genetic mutations that led to the development
of life on our planet as we know it [2].
In the frame of this thesis we will explore how we can make use of these
random mutations of the genetic representation of competing candidates to find
functions that correlate well with the stock market. This will yield a set of
formulaic alphas that are used in quantitative investing to recognise patterns in
a stock’s price development and trade on them accordingly. We will evaluate the
performance of a set of eight formulaic alphas that are generated by a genetic
program on the Nasdaq 100 and realize that they are highly correlated with
the development of the federal reserve’s balance sheet. The assessment of a
simple trading algorithm’s performance increase allows the assumption that the
generated formulaic alphas help to recognise patterns in the stock market.
FLAIR: A Metric for Liquidity Provider Competitiveness in Automated Market Makers
Authors: Jason Milionis, Xin Wan, Austin Adams
Date: 2023-06-15 00:00:00

This paper aims to enhance the understanding of liquidity provider (LP) returns in automated market makers (AMMs). LPs face market risk as well as adverse selection due to risky asset holdings in the pool that they provide liquidity to and the informational asymmetry between informed traders (arbitrageurs) and AMMs. Loss-versus-rebalancing (LVR) quantifies the adverse selection cost (Milionis et al., 2022a), and is a popular metric to evaluate the flow toxicity to an AMM. However, individual LP returns are critically affected by another factor orthogonal to the above: the competitiveness among LPs. This work introduces a novel metric for LP competitiveness, called FLAIR (short for fee liquidity-adjusted instantaneous returns), that aims to supplement LVR in assessments of LP performance to capture the dynamic behavior of LPs in a pool. Our metric reflects the characteristics of fee return-on-capital, and differentiates active liquidity provisioning strategies in AMMs. To illustrate how both flow toxicity, accounting for the sophistication of the counterparty of LPs, as well as LP competitiveness, accounting for the sophistication of the competition among LPs, affect individual LP returns, we propose a quadrant interpretation where all of these characteristics may be readily visualized. We examine LP competitiveness in an ex-post fashion, and show example cases in all of which our metric confirms the expected nuances and intuition of competitiveness among LPs. FLAIR has particular merit in empirical analyses, and is able to better inform practical assessments of AMM pools.
House-Swapping with Objective Indifferences
Authors: Will Sandholtz, Andrew Tai
Date: 2023-06-15 00:00:00

We study the classic house-swapping problem of Shapley and Scarf (1974) in a setting where agents may have "objective" indifferences, i.e., indifferences that are shared by all agents. In other words, if any one agent is indifferent between two houses, then all agents are indifferent between those two houses. The most direct interpretation is the presence of multiple copies of the same object. Our setting is a special case of the house-swapping problem with general indifferences. We derive a simple, easily interpretable algorithm that produces the unique strict core allocation of the house-swapping market, if it exists. Our algorithm runs in square polynomial time, a substantial improvement over the cubed time methods for the more general problem.
Crowdsourcing and Evaluating Text-Based Audio Retrieval Relevances
Authors: Huang Xie, Khazar Khorrami, Okko Räsänen, Tuomas Virtanen
Date: 2023-06-16 00:00:00
Tasks: Audio captioning, Contrastive Learning, Retrieval

This paper explores grading text-based audio retrieval relevances with crowdsourcing assessments. Given a free-form text (e.g., a caption) as a query, crowdworkers are asked to grade audio clips using numeric scores (between 0 and 100) to indicate their judgements of how much the sound content of an audio clip matches the text, where 0 indicates no content match at all and 100 indicates perfect content match. We integrate the crowdsourced relevances into training and evaluating text-based audio retrieval systems, and evaluate the effect of using them together with binary relevances from audio captioning. Conventionally, these binary relevances are defined by captioning-based audio-caption pairs, where being positive indicates that the caption describes the paired audio, and being negative applies to all other pairs. Experimental results indicate that there is no clear benefit from incorporating crowdsourced relevances alongside binary relevances when the crowdsourced relevances are binarized for contrastive learning. Conversely, the results suggest that using only binary relevances defined by captioning-based audio-caption pairs is sufficient for contrastive learning.
MFSN: Multi-perspective Fusion Search Network For Pre-training Knowledge in Speech Emotion Recognition
Authors: Haiyang Sun, FuLin Zhang, Yingying Gao, Zheng Lian, Shilei Zhang, Junlan Feng
Date: 2023-06-12 00:00:00
Tasks: Emotion Recognition, Quantization, Speech Emotion Recognition

Speech Emotion Recognition (SER) is an important research topic in human-computer interaction. Many recent works focus on directly extracting emotional cues through pre-trained knowledge, frequently overlooking considerations of appropriateness and comprehensiveness. Therefore, we propose a novel framework for pre-training knowledge in SER, called Multi-perspective Fusion Search Network (MFSN). Considering comprehensiveness, we partition speech knowledge into Textual-related Emotional Content (TEC) and Speech-related Emotional Content (SEC), capturing cues from both semantic and acoustic perspectives, and we design a new architecture search space to fully leverage them. Considering appropriateness, we verify the efficacy of different modeling approaches in capturing SEC and fills the gap in current research. Experimental results on multiple datasets demonstrate the superiority of MFSN.
A note on the convergence of a class of adaptive optimal identifiers
Authors: Laurent Bako
Date: 2023-06-16 00:00:00

This paper proposes a unifying framework for the convergence analysis of a class of adaptive optimal identifiers. The considered class of identifiers is constructed from the sequence of minimizing sets of a family of objective functions. For the purpose of the analysis we introduce a generalized version of the classical persistence of excitation condition. Based on this an Input-to-State-Stability (ISS)-type of property is derived for the studied class of adaptive estimators.
Overtaking-enabled Eco-approach Control at Signalized Intersections for Connected and Automated Vehicles
Authors: Haoxuan Dong, Weichao Zhuang, Guoyuan Wu, Zhaojian Li, Guodong Yin, Ziyou Song
Date: 2023-06-16 00:00:00

Preceding vehicles typically dominate the movement of following vehicles in traffic systems, thereby significantly influencing the efficacy of eco-driving control that concentrates on vehicle speed optimization. To potentially mitigate the negative effect of preceding vehicles on eco-driving control at the signalized intersection, this paper proposes an overtakingenabled eco-approach control (OEAC) strategy. It combines driving lane planning and speed optimization for connected and automated vehicles to relax the first-in-first-out queuing policy at the signalized intersection, minimizing the target vehicle's energy consumption and travel delay. The OEAC adopts a receding horizon two-stage control framework to derive optimal driving trajectories for adapting to dynamic traffic conditions. In the first stage, the driving lane optimization problem is formulated as a Markov decision process and solved using dynamic programming, which takes into account the uncertain disturbance from preceding vehicles. In the second stage, the vehicle's speed trajectory with the minimal driving cost is optimized rapidly using Pontryagin's minimum principle to obtain the closed-form analytical optimal solution. Extensive simulations are conducted to evaluate the effectiveness of the OEAC. The results show that the OEAC is excellent in driving cost reduction over constant speed and regular eco-approach and departure strategies in various traffic scenarios, with an average improvement of 20.91% and 5.62%, respectively.
Combinatorial-restless-bandit-based Transmitter-Receiver Online Selection for Distributed MIMO Radars With Non-Stationary Channels
Authors: Yuhang Hao, Zengfu Wang, Jing Fu, Xianglong Bai, Can Li, Quan Pan
Date: 2023-06-16 00:00:00
Tasks: State Estimation

We track moving targets with a distributed multiple-input multiple-output (MIMO) radar, for which the transmitters and receivers are appropriately paired and selected with a limited number of radar stations. We aim to maximize the sum of the signal-to-interference-plus-noise ratios (SINRs) of all the targets by sensibly selecting the transmitter-receiver pairs during the tracking period. A key is to model the optimization problem of selecting the transmitter-receiver pairs by a restless multi-armed bandit (RMAB) model that is able to formulate the time-varying signals of the transceiver channels whenever the channels are being probed or not. We regard the estimated mean reward (i.e., SINR) as the state of an arm. If an arm is probed, the estimated mean reward of the arm is the weighted sum of the observed reward and the predicted mean reward; otherwise, it is the predicted mean reward. We associate the predicted mean reward with the estimated mean reward at the previous time slot and the state of the target, which is estimated via the interacting multiple model-unscented Kalman filter (IMM-UKF). The optimized selection of transmitter-receiver pairs at each time is accomplished by using Binary Particle Swarm Optimization (BPSO) based on indexes of arms, each of which is designed by the upper confidence bound (UCB1) algorithm. Above all, a multi-group combinatorial-restless-bandit technique taking into account of different combinations of transmitters and receivers and the closed-loop scheme between transmitter-receiver pair selection and target state estimation, namely MG-CRB-CL, is developed to achieve a near-optimal selection strategy and improve multi-target tracking performance. Simulation results for different scenarios are provided to verify the effectiveness and superior performance of our MG-CRB-CL algorithm.
Krasovskii Passivity for Sampled-data Stabilization and Output Consensus
Authors: Yu Kawano, Alessio Moreschini, Michele Cucuzzella
Date: 2023-06-16 00:00:00

In this paper, we establish the novel concept of Krasovskii passivity for sampled discrete-time nonlinear systems, enabling Krasovskii-passivity-based control design under sampling. We consider two separate control objectives: stabilization and output consensus, where the latter is studied under the presence of an unknown constant disturbance. Inspired by methodologies in the continuous-time case, we develop sampled-data control schemes for each control objective based on Krasovskii passivity. The proposed sampled discrete-time controllers are respectively validated through simulations on a DC microgrid of boost converters and a DC microgrid of buck converters whose continuous-time models and their implicit midpoint discretizations are Krasovskii passive in each time scale.