pwc-archive/papers-with-abstracts huggingface dataset:
Long-Term Effects of Hiring Subsidies for Low-Educated Unemployed Youths
Authors: Andrea Albanese, Bart Cockx, Muriel Dejemeppe
Date: 2024-06-12 00:00:00

We use regression discontinuity design and difference-in-differences methods to estimate the impact of a one-time hiring subsidy for low-educated unemployed youths in Belgium during the recovery from the Great Recession. Within a year of unemployment, the subsidy increases job-finding in the private sector by 10 percentage points. Over six years, high school graduates secure 2.8 more quarters of private employment. However, they transition from public jobs and self-employment, resulting in no net increase in overall employment, albeit with better wages. High school dropouts experience no lasting benefits. Additionally, in tight labor markets near Luxembourg's employment hub, the subsidy results in a complete deadweight loss.
Multimodal Representation Loss Between Timed Text and Audio for Regularized Speech Separation
Authors: Tsun-An Hsieh, Heeyoul Choi, Minje Kim
Date: 2024-06-12 00:00:00
Tasks: Language Modeling, Language Modelling, Speech Separation, Word Embeddings

Recent studies highlight the potential of textual modalities in conditioning the speech separation model's inference process. However, regularization-based methods remain underexplored despite their advantages of not requiring auxiliary text data during the test time. To address this gap, we introduce a timed text-based regularization (TTR) method that uses language model-derived semantics to improve speech separation models. Our approach involves two steps. We begin with two pretrained audio and language models, WavLM and BERT, respectively. Then, a Transformer-based audio summarizer is learned to align the audio and word embeddings and to minimize their gap. The summarizer Transformer, incorporated as a regularizer, promotes the separated sources' alignment with the semantics from the timed text. Experimental results show that the proposed TTR method consistently improves the various objective metrics of the separation results over the unregularized baselines.
Multi-Static ISAC based on Network-Assisted Full-Duplex Cell-Free Networks: Performance Analysis and Duplex Mode Optimization
Authors: Fan Zeng, Ruoyun Liu, Xiaoyu Sun, Jingxuan Yu, Jiamin Li, Pengchen Zhu, Dongming Wang, Xiaohu You
Date: 2024-06-12 00:00:00
Tasks: Integrated sensing and communication, ISAC

Multi-static integrated sensing and communication (ISAC) technology, which can achieve a wider coverage range and avoid self-interference, is an important trend for the future development of ISAC. Existing multi-static ISAC designs are unable to support the asymmetric uplink (UL)/downlink (DL) communication requirements in the scenario while simultaneously achieving optimal sensing performance. This paper proposes a design for multi-static ISAC based on network-assisted full-duplex (NAFD) cell-free networks can well solve the above problems. Under this design, closed-form expressions for the individual comunication rate and localization error rate are derived under imperfect channel state information, which are respectively utilized to assess the communication and sensing performances. Then, we propose a deep Q-network-based accesss point (AP) duplex mode optimization algorithm to obtain the trade-off between communication and sensing from the UL and DL perspectives of the APs. Simulation results demonstrate that the NAFD-based ISAC system proposed in this paper can achieve significantly better communication performance than other ISAC systems while ensuring minimal impact on sensing performance. Then, we validate the accuracy of the derived closed-form expressions. Furthermore, the proposed optimization algorithm achieves performance comparable to that of the exhaustion method with low complexity.
SCDNet: Self-supervised Learning Feature-based Speaker Change Detection
Authors: Yue Li, Xinsheng Wang, Li Zhang, Lei Xie
Date: 2024-06-12 00:00:00
Tasks: Change Detection, Contrastive Learning, Self-Supervised Learning

Speaker Change Detection (SCD) is to identify boundaries among speakers in a conversation. Motivated by the success of fine-tuning wav2vec 2.0 models for the SCD task, a further investigation of self-supervised learning (SSL) features for SCD is conducted in this work. Specifically, an SCD model, named SCDNet, is proposed. With this model, various state-of-the-art SSL models, including Hubert, wav2vec 2.0, and WavLm are investigated. To discern the most potent layer of SSL models for SCD, a learnable weighting method is employed to analyze the effectiveness of intermediate representations. Additionally, a fine-tuning-based approach is also implemented to further compare the characteristics of SSL models in the SCD task. Furthermore, a contrastive learning method is proposed to mitigate the overfitting tendencies in the training of both the fine-tuning-based method and SCDNet. Experiments showcase the superiority of WavLm in the SCD task and also demonstrate the good design of SCDNet.
Structured Active Inference (Extended Abstract)
Authors: Toby St Clere Smithe
Date: 2024-06-07 00:00:00

We introduce structured active inference, a large generalization and formalization of active inference using the tools of categorical systems theory. We cast generative models formally as systems "on an interface", with the latter being a compositional abstraction of the usual notion of Markov blanket; agents are then 'controllers' for their generative models, formally dual to them. This opens the active inference landscape to new horizons, such as: agents with structured interfaces (e.g. with 'mode-dependence', or that interact with computer APIs); agents that can manage other agents; and 'meta-agents', that use active inference to change their (internal or external) structure. With structured interfaces, we also gain structured ('typed') policies, which are amenable to formal verification, an important step towards safe artificial agents. Moreover, we can make use of categorical logic to describe express agents' goals as formal predicates, whose satisfaction may be dependent on the interaction context. This points towards powerful compositional tools to constrain and control self-organizing ensembles of agents.
Development of Focused X-ray Luminescence Compute Tomography Imaging
Authors: Yile Fang, Yibing Zhang, Changqing Li
Date: 2024-06-11 00:00:00
Tasks: Sensitivity

X-ray luminescence is produced when contrast agents absorb energy from X-ray photons and release a portion of that energy by emitting photons in the visible and near-infrared range. X-ray luminescence computed tomography (XLCT) was introduced in the past decade as a hybrid molecular imaging modality combining the merits of both X-ray imaging (high spatial resolution) and optical imaging (high sensitivity to tracer nanophosphors).
Identification and Inference on Treatment Effects under Covariate-Adaptive Randomization and Imperfect Compliance
Authors: Federico A. Bugni, Mengsi Gao, Filip Obradovic, Amilcar Velez
Date: 2024-06-12 00:00:00
Tasks: valid

Randomized controlled trials (RCTs) frequently utilize covariate-adaptive randomization (CAR) (e.g., stratified block randomization) and commonly suffer from imperfect compliance. This paper studies the identification and inference for the average treatment effect (ATE) and the average treatment effect on the treated (ATT) in such RCTs with a binary treatment. We first develop characterizations of the identified sets for both estimands. Since data are generally not i.i.d. under CAR, these characterizations do not follow from existing results. We then provide consistent estimators of the identified sets and asymptotically valid confidence intervals for the parameters. Our asymptotic analysis leads to concrete practical recommendations regarding how to estimate the treatment assignment probabilities that enter the estimated bounds. For the ATE bounds, using sample analog assignment frequencies is more efficient than relying on the true assignment probabilities. For the ATT bounds, the most efficient approach is to use the true assignment probability for the probabilities in the numerator and the sample analog for those in the denominator.
Rigorous Safety Analysis and Design of ADAS and ADS: Implications on Tools
Authors: Juan Pimentel
Date: 2024-06-12 00:00:00

Currently, a major concern is the insufficient level of safety offered by commercial automated vehicles and/or services such self-driving vehicles, self-driving trucks, and robotaxis. Unfortunately, stakeholders do not agree on definitions and characterizations of what is meant by safety of automated vehicles including how to measure it and how to design for it. This paper sheds some light into the answers to important questions about the safety of automated vehicles. In addition, we identify rigor as a significant missing requirement in the current literature, we also provide a discussion of rigor in the design, development, and commercialization of automated vehicles. Furthermore, we discuss software tool requirements at the organizational level to support a rigorous approach for the analysis, design, and commercialization of automated vehicles. An ALM tool, EwQIMS, is introduced emphasizing its rigorous features of its functional safety module that implements much of the ISO 26262 standard.
Heterogeneous Beliefs Model of Stock Market Predictability
Authors: Jiho Park
Date: 2024-06-12 00:00:00
Tasks: model

This paper proposes a theory of stock market predictability patterns based on a model of heterogeneous beliefs. In a discrete finite time framework, some agents receive news about an asset's fundamental value through a noisy signal. The investors are heterogeneous in that they have different beliefs about the stochastic supply. A momentum in the stock price arises from those agents who incorrectly underestimate the signal accuracy, dampening the initial price impact of the signal. A reversal in price occurs because the price reverts to the fundamental value in the long run. An extension of the model to multiple assets case predicts co-movement and lead-lag effect, in addition to cross-sectional momentum and reversal. The heterogeneous beliefs of investors about news demonstrate how the main predictability anomalies arise endogenously in a model of bounded rationality.
A conceptual predator-prey model with super-long transients
Authors: Misha Chai, Holger Kantz
Date: 2024-06-12 00:00:00

Drawing on the understanding of the logistic map, we propose a simple predator-prey model where predators and prey adapt to each other, leading to the co-evolution of the system. The special dynamics observed in periodic windows contribute to the coexistence of multiple time scales, adding to the complexity of the system. Typical dynamics in ecosystems, such as the persistence and coexistence of population cycles and chaotic behaviors, the emergence of super-long transients, regime shifts, and the quantifying of resilience, are encapsulated within this single model. The simplicity of our model allows for detailed analysis, reinforcing its potential as a conceptual tool for understanding ecosystems deeply.
Deep reinforcement learning with positional context for intraday trading
Authors: Sven Goluža, Tomislav Kovačević, Tessa Bauman, Zvonko Kostanjčar
Date: 2024-06-12 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, Feature Importance, reinforcement-learning, Reinforcement Learning

Deep reinforcement learning (DRL) is a well-suited approach to financial decision-making, where an agent makes decisions based on its trading strategy developed from market observations. Existing DRL intraday trading strategies mainly use price-based features to construct the state space. They neglect the contextual information related to the position of the strategy, which is an important aspect given the sequential nature of intraday trading. In this study, we propose a novel DRL model for intraday trading that introduces positional features encapsulating the contextual information into its sparse state space. The model is evaluated over an extended period of almost a decade and across various assets including commodities and foreign exchange securities, taking transaction costs into account. The results show a notable performance in terms of profitability and risk-adjusted metrics. The feature importance results show that each feature incorporating contextual information contributes to the overall performance of the model. Additionally, through an exploration of the agent's intraday trading activity, we unveil patterns that substantiate the effectiveness of our proposed model.
Fully Few-shot Class-incremental Audio Classification Using Expandable Dual-embedding Extractor
Authors: Yongjie Si, Yanxiong Li, Jialong Li, Jiaxin Tan, Qianhua He
Date: 2024-06-12 00:00:00
Tasks: Audio Classification

It's assumed that training data is sufficient in base session of few-shot class-incremental audio classification. However, it's difficult to collect abundant samples for model training in base session in some practical scenarios due to the data scarcity of some classes. This paper explores a new problem of fully few-shot class-incremental audio classification with few training samples in all sessions. Moreover, we propose a method using expandable dual-embedding extractor to solve it. The proposed model consists of an embedding extractor and an expandable classifier. The embedding extractor consists of a pretrained Audio Spectrogram Transformer (AST) and a finetuned AST. The expandable classifier consists of prototypes and each prototype represents a class. Experiments are conducted on three datasets (LS-100, NSynth-100 and FSC-89). Results show that our method exceeds seven baseline ones in average accuracy with statistical significance. Code is at: https://github.com/YongjieSi/EDE.
DehazeDCT: Towards Effective Non-Homogeneous Dehazing via Deformable Convolutional Transformer
Authors: Wei Dong, Han Zhou, Ruiyi Wang, Xiaohong Liu, Guangtao Zhai, Jun Chen
Date: 2024-06-12 00:00:00
Proceeding: CVPRW 2024 6
Tasks: Image Dehazing, Nonhomogeneous Image Dehazing, Representation Learning, Single Image Dehazing

Image dehazing, a pivotal task in low-level vision, aims to restore the visibility and detail from hazy images. Many deep learning methods with powerful representation learning capability demonstrate advanced performance on non-homogeneous dehazing, however, these methods usually struggle with processing high-resolution
images (e.g., 4000 × 6000) due to their heavy computational demands. To address these challenges, we introduce an innovative non-homogeneous Dehazing method via Deformable Convolutional Transformer-like architecture
(DehazeDCT). Specifically, we first design a transformerlike network based on deformable convolution v4, which offers long-range dependency and adaptive spatial aggregation capabilities and demonstrates faster convergence and forward speed. Furthermore, we leverage a lightweight Retinex-inspired transformer to achieve color correction and structure refinement. Extensive experiment results and highly competitive performance of our method in NTIRE 2024 Dense and Non-Homogeneous Dehazing Challenge, ranking second among all 16 submissions, demonstrate the superior capability of our proposed method. The code is available: https://github.com/movingforward100/Dehazing_R.
A Parameterized Nonlinear Magnetic Equivalent Circuit for Design and Fast Analysis of Radial Flux Magnetic Gears with Bridges
Authors: Danial Kazemikia, Matthew Gardner
Date: 2024-06-12 00:00:00

Magnetic gears offer significant advantages over mechanical gears, including contactless power transfer, but require efficient and accurate modeling tools for optimization and commercialization. This paper presents the first fast and accurate 2D nonlinear magnetic equivalent circuit (MEC) model for radial flux magnetic gears (RFMG), capable of analyzing designs with bridges critical structural elements that introduce intense localized magnetic saturation. The proposed model systematically incorporates nonlinear effects while maintaining rapid simulation times through a parameterized geometry and adaptable flux tube distribution. A robust initialization strategy ensures reliable performance across diverse designs. Extensive validation against nonlinear finite element analysis (FEA) confirms the model's accuracy in torque and flux density predictions. A comprehensive parametric study of 140,000 designs demonstrates close agreement with FEA results, with simulations running up to 100 times faster. Unlike previous MEC approaches, this model provides a generalized, computationally efficient solution for analyzing a wide range of RFMG designs with or without bridges, making it particularly well-suited for large scale design optimization.
DMS: Addressing Information Loss with More Steps for Pragmatic Adversarial Attacks
Authors: Zhiyu Zhu, Jiayu Zhang, Xinyi Wang, Zhibo Jin, Huaming Chen
Date: 2024-06-09 00:00:00
Tasks: Adversarial Attack

Despite the exceptional performance of deep neural networks (DNNs) across different domains, they are vulnerable to adversarial samples, in particular for tasks related to computer vision. Such vulnerability is further influenced by the digital container formats used in computers, where the discrete numerical values are commonly used for storing the pixel values. This paper examines how information loss in file formats impacts the effectiveness of adversarial attacks. Notably, we observe a pronounced hindrance to the adversarial attack performance due to the information loss of the non-integer pixel values. To address this issue, we explore to leverage the gradient information of the attack samples within the model to mitigate the information loss. We introduce the Do More Steps (DMS) algorithm, which hinges on two core techniques: gradient ascent-based \textit{adversarial integerization} (DMS-AI) and integrated gradients-based \textit{attribution selection} (DMS-AS). Our goal is to alleviate such lossy process to retain the attack performance when storing these adversarial samples digitally. In particular, DMS-AI integerizes the non-integer pixel values according to the gradient direction, and DMS-AS selects the non-integer pixels by comparing attribution results. We conduct thorough experiments to assess the effectiveness of our approach, including the implementations of the DMS-AI and DMS-AS on two large-scale datasets with various latest gradient-based attack methods. Our empirical findings conclusively demonstrate the superiority of our proposed DMS-AI and DMS-AS pixel integerization methods over the standardised methods, such as rounding, truncating and upper approaches, in maintaining attack integrity.
Situated Ground Truths: Enhancing Bias-Aware AI by Situating Data Labels with SituAnnotate
Authors: Delfina Sol Martinez Pandiani, Valentina Presutti
Date: 2024-06-10 00:00:00

In the contemporary world of AI and data-driven applications, supervised machines often derive their understanding, which they mimic and reproduce, through annotations--typically conveyed in the form of words or labels. However, such annotations are often divorced from or lack contextual information, and as such hold the potential to inadvertently introduce biases when subsequently used for training. This paper introduces SituAnnotate, a novel ontology explicitly crafted for 'situated grounding,' aiming to anchor the ground truth data employed in training AI systems within the contextual and culturally-bound situations from which those ground truths emerge. SituAnnotate offers an ontology-based approach to structured and context-aware data annotation, addressing potential bias issues associated with isolated annotations. Its representational power encompasses situational context, including annotator details, timing, location, remuneration schemes, annotation roles, and more, ensuring semantic richness. Aligned with the foundational Dolce Ultralight ontology, it provides a robust and consistent framework for knowledge representation. As a method to create, query, and compare label-based datasets, SituAnnotate empowers downstream AI systems to undergo training with explicit consideration of context and cultural bias, laying the groundwork for enhanced system interpretability and adaptability, and enabling AI models to align with a multitude of cultural contexts and viewpoints.
Compressive Beam Alignment for Indoor Millimeter-Wave Systems
Authors: April Junio, Rafaela Lomboy, Raj Sai Sohel Bandari, Mohammed E. Eltayeb
Date: 2024-06-12 00:00:00
Tasks: compressed sensing, Quantization

The dynamic nature of indoor environments poses unique challenges for next-generation millimeter-wave (mmwave) connectivity. These challenges arise from blockages due to mobile obstacles, mm-wave signal scattering caused by indoor surfaces, and user phased antenna array imperfections. Traditional compressed sensing (CS) based beam alignment techniques enable swift mm-wave connectivity with a limited number of measurements. These techniques, however, rely on prior knowledge of the communication channel model and the user's array manifold to design the sensing matrix and minimize angle quantization errors. This limits their effectiveness in dynamic environments. This paper proposes a novel CS-based beam alignment technique for mm-wave systems operating in indoor environments. Unlike prior work that rely on knowledge of the user's antenna architecture, communication codebook, and channel, the proposed technique is agnostic to these factors. The proposed formulation eliminates angle quantization errors by mapping the recovered angular directions onto the user's specific codebook. This is achieved by exploiting the energy compaction property of the Discrete Cosine Transform (DCT) to compress and identify the strongest cluster locations in the transform domain for robust beamforming. Experimental results at 60 GHz demonstrate successful recovery of the mm-wave power distribution in the angular domain, facilitating accurate beam alignment with limited measurements compared to exhaustive search solutions.
GFPack++: Improving 2D Irregular Packing by Learning Gradient Field with Attention
Authors: Tianyang Xue, Lin Lu, Yang Liu, Mingdong Wu, Hao Dong, Yanbin Zhang, Renmin Han, Baoquan Chen
Date: 2024-06-09 00:00:00
Tasks: Combinatorial Optimization

2D irregular packing is a classic combinatorial optimization problem with various applications, such as material utilization and texture atlas generation. This NP-hard problem requires efficient algorithms to optimize space utilization. Conventional numerical methods suffer from slow convergence and high computational cost. Existing learning-based methods, such as the score-based diffusion model, also have limitations, such as no rotation support, frequent collisions, and poor adaptability to arbitrary boundaries, and slow inferring. The difficulty of learning from teacher packing is to capture the complex geometric relationships among packing examples, which include the spatial (position, orientation) relationships of objects, their geometric features, and container boundary conditions. Representing these relationships in latent space is challenging. We propose GFPack++, an attention-based gradient field learning approach that addresses this challenge. It consists of two pivotal strategies: \emph{attention-based geometry encoding} for effective feature encoding and \emph{attention-based relation encoding} for learning complex relationships. We investigate the utilization distribution between the teacher and inference data and design a weighting function to prioritize tighter teacher data during training, enhancing learning effectiveness. Our diffusion model supports continuous rotation and outperforms existing methods on various datasets. We achieve higher space utilization over several widely used baselines, one-order faster than the previous diffusion-based method, and promising generalization for arbitrary boundaries. We plan to release our source code and datasets to support further research in this direction.