pwc-archive/papers-with-abstracts huggingface dataset:
FireFly v2: Advancing Hardware Support for High-Performance Spiking Neural Network with a Spatiotemporal FPGA Accelerator
Authors: Jindong Li, Guobin Shen, Dongcheng Zhao, Qian Zhang, Yi Zeng
Date: 2023-09-28 00:00:00

Spiking Neural Networks (SNNs) are expected to be a promising alternative to Artificial Neural Networks (ANNs) due to their strong biological interpretability and high energy efficiency. Specialized SNN hardware offers clear advantages over general-purpose devices in terms of power and performance. However, there's still room to advance hardware support for state-of-the-art (SOTA) SNN algorithms and improve computation and memory efficiency. As a further step in supporting high-performance SNNs on specialized hardware, we introduce FireFly v2, an FPGA SNN accelerator that can address the issue of non-spike operation in current SOTA SNN algorithms, which presents an obstacle in the end-to-end deployment onto existing SNN hardware. To more effectively align with the SNN characteristics, we design a spatiotemporal dataflow that allows four dimensions of parallelism and eliminates the need for membrane potential storage, enabling on-the-fly spike processing and spike generation. To further improve hardware acceleration performance, we develop a high-performance spike computing engine as a backend based on a systolic array operating at 500-600MHz. To the best of our knowledge, FireFly v2 achieves the highest clock frequency among all FPGA-based implementations. Furthermore, it stands as the first SNN accelerator capable of supporting non-spike operations, which are commonly used in advanced SNN algorithms. FireFly v2 has doubled the throughput and DSP efficiency when compared to our previous version of FireFly and it exhibits 1.33 times the DSP efficiency and 1.42 times the power efficiency compared to the current most advanced FPGA accelerators.
Confidence and second-order errors in cortical circuits
Authors: Arno Granier, Mihai A. Petrovici, Walter Senn, Katharina A. Wilmes
Date: 2023-09-27 00:00:00
Tasks: Prediction

Minimization of cortical prediction errors has been considered a key computational goal of the cerebral cortex underlying perception, action and learning. However, it is still unclear how the cortex should form and use information about uncertainty in this process. Here, we formally derive neural dynamics that minimize prediction errors under the assumption that cortical areas must not only predict the activity in other areas and sensory streams but also jointly project their confidence (inverse expected uncertainty) in their predictions. In the resulting neuronal dynamics, the integration of bottom-up and top-down cortical streams is dynamically modulated based on confidence in accordance with the Bayesian principle. Moreover, the theory predicts the existence of cortical second-order errors, comparing confidence and actual performance. These errors are propagated through the cortical hierarchy alongside classical prediction errors and are used to learn the weights of synapses responsible for formulating confidence. We propose a detailed mapping of the theory to cortical circuitry, discuss entailed functional interpretations and provide potential directions for experimental work.
Nonparametric estimation of a covariate-adjusted counterfactual treatment regimen response curve
Authors: Ashkan Ertefaie, Luke Duttweiler, Brent A. Johnson, Mark J. Van Der Laan
Date: 2023-09-28 00:00:00
Tasks: counterfactual

Flexible estimation of the mean outcome under a treatment regimen (i.e., value function) is the key step toward personalized medicine. We define our target parameter as a conditional value function given a set of baseline covariates which we refer to as a stratum based value function. We focus on semiparametric class of decision rules and propose a sieve based nonparametric covariate adjusted regimen-response curve estimator within that class. Our work contributes in several ways. First, we propose an inverse probability weighted nonparametrically efficient estimator of the smoothed regimen-response curve function. We show that asymptotic linearity is achieved when the nuisance functions are undersmoothed sufficiently. Asymptotic and finite sample criteria for undersmoothing are proposed. Second, using Gaussian process theory, we propose simultaneous confidence intervals for the smoothed regimen-response curve function. Third, we provide consistency and convergence rate for the optimizer of the regimen-response curve estimator; this enables us to estimate an optimal semiparametric rule. The latter is important as the optimizer corresponds with the optimal dynamic treatment regimen. Some finite-sample properties are explored with simulations.
Decoding the Workplace & EOR: An Employee Survey Analysis by Data Science Techniques and Visualization
Authors: Kishankumar Bhimani, Khushbu Saradva
Date: 2023-09-28 00:00:00
Tasks: Management, Sociology

This research study explores the new dynamics of employee-organi-zation relationships (EOR) [6] using advanced data science methodologies and presents findings through accessible visualizations. Leveraging a dataset pro-cured from a comprehensive nationwide big employee survey, this study employs innovative strategy for theoretical researcher by using our state-of-the-art visual-ization. The results present insightful visualizations encapsulating demographic analysis, workforce satisfaction, work environment scrutiny, and the employee's view via word cloud interpretations and burnout predictions. The study underscores the profound implications of data science across various management sectors, enhancing understanding of workplace dynamics and pro-moting mutual growth and satisfaction. This multifaceted approach caters to a diverse array of readers, from researchers in sociology and management to firms seeking detailed understanding of their workforce's satisfaction, emphasizing on practicality and interpretability. The research encourages proactive measures to improve workplace environ-ments, boost employee satisfaction, and foster healthier, more productive organ-izations. It serves as a resourceful tool for those committed to these objectives, manifesting the transformative potential of data science in driving insightful nar-ratives about workplace dynamics and employee-organization relationships. In essence, this research unearths valuable insights to aid management, HR profes-sionals, and companies
Multi-Granularity Click Confidence Learning via Self-Distillation in Recommendation
Authors: Chong Liu, Xiaoyang Liu, Lixin Zhang, Feng Xia, Leyu Lin
Date: 2023-09-28 00:00:00
Tasks: Recommendation Systems, Self-Supervised Learning

Recommendation systems rely on historical clicks to learn user interests and provide appropriate items. However, current studies tend to treat clicks equally, which may ignore the assorted intensities of user interests in different clicks. In this paper, we aim to achieve multi-granularity Click confidence Learning via Self-Distillation in recommendation (CLSD). Due to the lack of supervised signals in click confidence, we first apply self-supervised learning to obtain click confidence scores via a global self-distillation method. After that, we define a local confidence function to adapt confidence scores at the user group level, since the confidence distributions can be varied among user groups. With the combination of multi-granularity confidence learning, we can distinguish the quality of clicks and model user interests more accurately without involving extra data and model structures. The significant improvements over different backbones on industrial offline and online experiments in a real-world recommender system prove the effectiveness of our model. Recently, CLSD has been deployed on a large-scale recommender system, affecting over 400 million users.
A Physics Informed Machine Learning Method for Power System Model Parameter Optimization
Authors: Georg Kordowich, Johann Jaeger
Date: 2023-09-28 00:00:00

This paper proposes a gradient descent based optimization method that relies on automatic differentiation for the computation of gradients. The method uses tools and techniques originally developed in the field of artificial neural networks and applies them to power system simulations. It can be used as a one-shot physics informed machine learning approach for the identification of uncertain power system simulation parameters. Additionally, it can optimize parameters with respect to a desired system behavior. The paper focuses on presenting the theoretical background and showing exemplary use-cases for both parameter identification and optimization using a single machine infinite busbar system. The results imply a generic applicability for a wide range of problems.
A survey of Transformer applications for histopathological image analysis: New developments and future directions
Authors: Chukwuemeka Clinton Atabansi, Jing Nie, Haijun Liu, Qianqian Song, Lingfeng Yan, Xichuan Zhou
Date: 2023-09-25 00:00:00
Proceeding: journal 2023 9
Tasks: Survey, Survival Analysis

Transformers have been widely used in many computer vision challenges and have shown the capability of producing better results than convolutional neural networks (CNNs). Taking advantage of capturing long-range contextual information and learning more complex relations in the image data, Transformers have been used and applied to histopathological image processing tasks. In this survey, we make an effort to present a thorough analysis of the uses of Transformers in histopathological image analysis, covering several topics, from the newly built Transformer models to unresolved challenges. To be more precise, we first begin by outlining the fundamental principles of the attention mechanism included in Transformer models and other key frameworks. Second, we analyze Transformer-based applications in the histopathological imaging domain and provide a thorough evaluation of more than 100 research publications across different downstream tasks to cover the most recent innovations, including survival analysis and prediction, segmentation, classification, detection, and representation. Within this survey work, we also compare the performance of CNN-based techniques to Transformers based on recently published papers, highlight major challenges, and provide interesting future research directions. Despite the outstanding performance of the Transformer-based architectures in a number of papers reviewed in this survey, we anticipate that further improvements and exploration of Transformers in the histopathological imaging domain are still required in the future. We hope that this survey paper will give readers in this field of study a thorough understanding of Transformer-based techniques in histopathological image analysis, and an up-to-date paper list summary will be provided at https://github.com/S-domain/Survey-Paper.
Beyond Citations: Measuring Novel Scientific Ideas and their Impact in Publication Text
Authors: Sam Arts, Nicola Melluso, Reinhilde Veugelers
Date: 2023-09-28 00:00:00
Tasks: Novelty Detection

New scientific ideas drive progress, yet measuring scientific novelty remains challenging. We use natural language processing to detect the origin and impact of new ideas in scientific publications. To validate our methods, we analyze Nobel Prize-winning papers, which likely pioneered impactful new ideas, and literature review papers, which typically consolidate existing knowledge. We also show that novel papers have more intellectual neighbors published after them, indicating they are ahead of their intellectual peers. Finally, papers introducing new ideas, particularly those with greater follow-on reuse, attract more citations.
Introducing the Mathematics Meme Repository
Authors: Abdelghani Belgaid
Date: 2021-10-19 00:00:00
Proceeding: - 2021 10
Tasks: Math

In the era of digital communication, memes have become a potent medium for conveying ideas, humor, and cultural references. This paper introduces the “Mathematical Mathematics Memes Dataset”, a comprehensive collection of over 10,000 math-related memes sourced from the “Mathematical Mathematics Memes” Facebook group. These memes offer a unique perspective on the intersection of mathematics and humor. We discuss the dataset's origins, content, and potential applications, including meme generation, abusive meme detection, and text extraction for popularity prediction. This dataset serves as a valuable resource for researchers and meme enthusiasts interested in exploring the realm of mathematical memes.
Assessing the Solvency of Virtual Asset Service Providers: Are Current Standards Sufficient?
Authors: Pietro Saggese, Esther Segalla, Michael Sigmund, Burkhard Raunig, Felix Zangerl, Bernhard Haslhofer
Date: 2023-09-28 00:00:00

Entities like centralized cryptocurrency exchanges fall under the business category of virtual asset service providers (VASPs). As any other enterprise, they can become insolvent. VASPs enable the exchange, custody, and transfer of cryptoassets organized in wallets across distributed ledger technologies (DLTs). Despite the public availability of DLT transactions, the cryptoasset holdings of VASPs are not yet subject to systematic auditing procedures. In this paper, we propose an approach to assess the solvency of a VASP by cross-referencing data from three distinct sources: cryptoasset wallets, balance sheets from the commercial register, and data from supervisory entities. We investigate 24 VASPs registered with the Financial Market Authority in Austria and provide regulatory data insights such as who are the customers and where do they come from. Their yearly incoming and outgoing transaction volume amount to 2 billion EUR for around 1.8 million users. We describe what financial services they provide and find that they are most similar to traditional intermediaries such as brokers, money exchanges, and funds, rather than banks. Next, we empirically measure DLT transaction flows of four VASPs and compare their cryptoasset holdings to balance sheet entries. Data are consistent for two VASPs only. This enables us to identify gaps in the data collection and propose strategies to address them. We remark that any entity in charge of auditing requires proof that a VASP actually controls the funds associated with its on-chain wallets. It is also important to report fiat and cryptoasset and liability positions broken down by asset types at a reasonable frequency.
Compilation as a Defense: Enhancing DL Model Attack Robustness via Tensor Optimization
Authors: Stefan Trawicki, William Hackett, Lewis Birch, Neeraj Suri, Peter Garraghan
Date: 2023-09-20 00:00:00

Adversarial Machine Learning (AML) is a rapidly growing field of security research, with an often overlooked area being model attacks through side-channels. Previous works show such attacks to be serious threats, though little progress has been made on efficient remediation strategies that avoid costly model re-engineering. This work demonstrates a new defense against AML side-channel attacks using model compilation techniques, namely tensor optimization. We show relative model attack effectiveness decreases of up to 43% using tensor optimization, discuss the implications, and direction of future work.
Model Predictive Planning: Trajectory Planning in Obstruction-Dense Environments for Low-Agility Aircraft
Authors: Matthew T. Wallace, Brett Streetman, Laurent Lessard
Date: 2023-09-27 00:00:00
Tasks: Motion Planning, Navigate, Trajectory Planning

We present Model Predictive Planning (MPP), a trajectory planner for low-agility vehicles such as a fixed-wing aircraft to navigate obstacle-laden environments. MPP consists of (1) a multi-path planning procedure that identifies candidate paths, (2) a raytracing procedure that generates linear constraints around these paths to enforce obstacle avoidance, and (3) a convex quadratic program that finds a feasible trajectory within these constraints if one exists. Low-agility aircraft cannot track arbitrary paths, so refining a given path into a trajectory that respects the vehicle's limited maneuverability and avoids obstacles often leads to an infeasible optimization problem. The critical feature of MPP is that it efficiently considers multiple candidate paths during the refinement process, thereby greatly increasing the chance of finding a feasible and trackable trajectory. We demonstrate the effectiveness of MPP on a longitudinal aircraft model.
Meeting Recognition with Continuous Speech Separation and Transcription-Supported Diarization
Authors: Thilo von Neumann, Christoph Boeddeker, Tobias Cord-Landwehr, Marc Delcroix, Reinhold Haeb-Umbach
Date: 2023-09-28 00:00:00
Tasks: Sentence, Speech Separation

We propose a modular pipeline for the single-channel separation, recognition, and diarization of meeting-style recordings and evaluate it on the Libri-CSS dataset. Using a Continuous Speech Separation (CSS) system with a TF-GridNet separation architecture, followed by a speaker-agnostic speech recognizer, we achieve state-of-the-art recognition performance in terms of Optimal Reference Combination Word Error Rate (ORC WER). Then, a d-vector-based diarization module is employed to extract speaker embeddings from the enhanced signals and to assign the CSS outputs to the correct speaker. Here, we propose a syntactically informed diarization using sentence- and word-level boundaries of the ASR module to support speaker turn detection. This results in a state-of-the-art Concatenated minimum-Permutation Word Error Rate (cpWER) for the full meeting recognition pipeline.
PP-MeT: a Real-world Personalized Prompt based Meeting Transcription System
Authors: Xiang Lyu, Yuhang Cao, Qing Wang, JingJing Yin, Yuguang Yang, Pengpeng Zou, Yanni Hu, Heng Lu
Date: 2023-09-28 00:00:00
Tasks: Action Detection, Activity Detection, Automatic Speech Recognition, speech-recognition, Speech Recognition

Speaker-attributed automatic speech recognition (SA-ASR) improves the accuracy and applicability of multi-speaker ASR systems in real-world scenarios by assigning speaker labels to transcribed texts. However, SA-ASR poses unique challenges due to factors such as speaker overlap, speaker variability, background noise, and reverberation. In this study, we propose PP-MeT system, a real-world personalized prompt based meeting transcription system, which consists of a clustering system, target-speaker voice activity detection (TS-VAD), and TS-ASR. Specifically, we utilize target-speaker embedding as a prompt in TS-VAD and TS-ASR modules in our proposed system. In constrast with previous system, we fully leverage pre-trained models for system initialization, thereby bestowing our approach with heightened generalizability and precision. Experiments on M2MeT2.0 Challenge dataset show that our system achieves a cp-CER of 11.27% on the test set, ranking first in both fixed and open training conditions.
Hierarchical Cross-Modality Knowledge Transfer with Sinkhorn Attention for CTC-based ASR
Authors: Xugang Lu, Peng Shen, Yu Tsao, Hisashi Kawai
Date: 2023-09-28 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, speech-recognition, Speech Recognition, Transfer Learning

Due to the modality discrepancy between textual and acoustic modeling, efficiently transferring linguistic knowledge from a pretrained language model (PLM) to acoustic encoding for automatic speech recognition (ASR) still remains a challenging task. In this study, we propose a cross-modality knowledge transfer (CMKT) learning framework in a temporal connectionist temporal classification (CTC) based ASR system where hierarchical acoustic alignments with the linguistic representation are applied. Additionally, we propose the use of Sinkhorn attention in cross-modality alignment process, where the transformer attention is a special case of this Sinkhorn attention process. The CMKT learning is supposed to compel the acoustic encoder to encode rich linguistic knowledge for ASR. On the AISHELL-1 dataset, with CTC greedy decoding for inference (without using any language model), we achieved state-of-the-art performance with 3.64% and 3.94% character error rates (CERs) for the development and test sets, which corresponding to relative improvements of 34.18% and 34.88% compared to the baseline CTC-ASR system, respectively.
Does Single-channel Speech Enhancement Improve Keyword Spotting Accuracy? A Case Study
Authors: Avamarie Brueggeman, Takuya Higuchi, Masood Delfarah, Stephen Shum, Vineet Garg
Date: 2023-09-27 00:00:00
Tasks: Automatic Speech Recognition, Keyword Spotting, Speech Enhancement, speech-recognition, Speech Recognition

Noise robustness is a key aspect of successful speech applications. Speech enhancement (SE) has been investigated to improve automatic speech recognition accuracy; however, its effectiveness for keyword spotting (KWS) is still under-investigated. In this paper, we conduct a comprehensive study on single-channel speech enhancement for keyword spotting on the Google Speech Command (GSC) dataset. To investigate robustness to noise, the GSC dataset is augmented with noise signals from the WSJ0 Hipster Ambient Mixtures (WHAM!) noise dataset. Our investigation includes not only applying SE before KWS but also performing joint training of the SE frontend and KWS backend models. Moreover, we explore audio injection, a common approach to reduce distortions by using a weighted average of the enhanced and original signals. Audio injection is then further optimized by using another model that predicts the weight for each utterance. Our investigation reveals that SE can improve KWS accuracy on noisy speech when the backend model is trained on clean speech; however, despite our extensive exploration, it is difficult to improve the KWS accuracy with SE when the backend is trained on noisy speech.
Neural Network Augmented Kalman Filter for Robust Acoustic Howling Suppression
Authors: Yixuan Zhang, Hao Zhang, Meng Yu, Dong Yu
Date: 2023-09-27 00:00:00

Acoustic howling suppression (AHS) is a critical challenge in audio communication systems. In this paper, we propose a novel approach that leverages the power of neural networks (NN) to enhance the performance of traditional Kalman filter algorithms for AHS. Specifically, our method involves the integration of NN modules into the Kalman filter, enabling refining reference signal, a key factor in effective adaptive filtering, and estimating covariance metrics for the filter which are crucial for adaptability in dynamic conditions, thereby obtaining improved AHS performance. As a result, the proposed method achieves improved AHS performance compared to both standalone NN and Kalman filter methods. Experimental evaluations validate the effectiveness of our approach.
Advancing Acoustic Howling Suppression through Recursive Training of Neural Networks
Authors: Hao Zhang, Yixuan Zhang, Meng Yu, Dong Yu
Date: 2023-09-27 00:00:00
Tasks: Acoustic echo cancellation

In this paper, we introduce a novel training framework designed to comprehensively address the acoustic howling issue by examining its fundamental formation process. This framework integrates a neural network (NN) module into the closed-loop system during training with signals generated recursively on the fly to closely mimic the streaming process of acoustic howling suppression (AHS). The proposed recursive training strategy bridges the gap between training and real-world inference scenarios, marking a departure from previous NN-based methods that typically approach AHS as either noise suppression or acoustic echo cancellation. Within this framework, we explore two methodologies: one exclusively relying on NN and the other combining NN with the traditional Kalman filter. Additionally, we propose strategies, including howling detection and initialization using pre-trained offline models, to bolster trainability and expedite the training process. Experimental results validate that this framework offers a substantial improvement over previous methodologies for acoustic howling suppression.