pwc-archive/papers-with-abstracts huggingface dataset:
Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers
Authors: Apoorv Vyas, Nataraj Jammalamadaka, Xia Zhu, Dipankar Das, Bharat Kaul, Theodore L. Willke
Date: 2018-09-04 00:00:00
Proceeding: ECCV 2018 9
Tasks: Autonomous Driving, Out-of-Distribution Detection

As deep learning methods form a critical part in commercially important applications such as autonomous driving and medical diagnostics, it is important to reliably detect out-of-distribution (OOD) inputs while employing these algorithms. In this work, we propose an OOD detection algorithm which comprises of an ensemble of classifiers. We train each classifier in a self-supervised manner by leaving out a random subset of training data as OOD data and the rest as in-distribution (ID) data. We propose a novel margin-based loss over the softmax output which seeks to maintain at least a margin $m$ between the average entropy of the OOD and in-distribution samples. In conjunction with the standard cross-entropy loss, we minimize the novel loss to train an ensemble of classifiers. We also propose a novel method to combine the outputs of the ensemble of classifiers to obtain OOD detection score and class prediction. Overall, our method convincingly outperforms Hendrycks et al.[7] and the current state-of-the-art ODIN[13] on several OOD detection benchmarks.
CLIP-driven Outliers Synthesis for few-shot OOD detection
Authors: Hao Sun, Rundong He, Zhongyi Han, Zhicong Lin, Yongshun Gong, Yilong Yin
Date: 2024-03-30 00:00:00
Tasks: Prompt Learning

Few-shot OOD detection focuses on recognizing out-of-distribution (OOD) images that belong to classes unseen during training, with the use of only a small number of labeled in-distribution (ID) images. Up to now, a mainstream strategy is based on large-scale vision-language models, such as CLIP. However, these methods overlook a crucial issue: the lack of reliable OOD supervision information, which can lead to biased boundaries between in-distribution (ID) and OOD. To tackle this problem, we propose CLIP-driven Outliers Synthesis~(CLIP-OS). Firstly, CLIP-OS enhances patch-level features' perception by newly proposed patch uniform convolution, and adaptively obtains the proportion of ID-relevant information by employing CLIP-surgery-discrepancy, thus achieving separation between ID-relevant and ID-irrelevant. Next, CLIP-OS synthesizes reliable OOD data by mixing up ID-relevant features from different classes to provide OOD supervision information. Afterward, CLIP-OS leverages synthetic OOD samples by unknown-aware prompt learning to enhance the separability of ID and OOD. Extensive experiments across multiple benchmarks demonstrate that CLIP-OS achieves superior few-shot OOD detection capability.
Reinforced Knowledge Distillation for Time Series Regression
Authors: Qing Xu, Keyu Wu, Min Wu, Kezhi Mao, XiaoLi Li, and Zhenghua Chen
Date: 2024-06-21 00:00:00
Proceeding: IEEE Transactions on Artificial Intelligence 2024 6
Tasks: Knowledge Distillation, Model Compression, regression, Time Series, Time Series Regression

As one of the most popular and effective methods in model compression, knowledge distillation (KD) attempts to transfer knowledge from single or multiple large-scale networks (i.e., Teachers) to a compact network (i.e., Student). For the multiteacher scenario, existing methods either assign equal or fixed weights for different teacher models during distillation, which can be inefficient as teachers might perform variously or even oppositely on different training samples. To address this issue, we propose a novel reinforced knowledge distillation method with negatively correlated teachers which are generated via negative correlation learning. The negatively correlated teachers would encourage teachers to learn different aspects of data and thus the ensemble of them can be more comprehensive and suitable for multiteacher KD. Subsequently, a reinforced KD algorithm is proposed to dynamically employ proper teachers for different training instances via dueling double deep Q-network (DDQN). Our proposed method complements the existing KD procedure on teacher generation and selection. Extensive experimental results on two real-world time series regression tasks clearly demonstrate that the proposed approach could achieve superior performance over state-of-the-art (SOTA) methods. The PyTorch implementation of our proposed approach is available at https://github.com/xuqing88/RL-KD-for-time-series-regression.
Subgroups: A Python library for Subgroup Discovery
Authors: Antonio Lopez-Martinez-Carrasco, Jose M. Juarez, Manuel Campos, Francisco Mora-Caselles
Date: 2024-09-17 00:00:00
Proceeding: SoftwareX 2024 9
Tasks: Data Mining, Subgroup Discovery

This manuscript introduces Subgroups, an openly accessible Python library designed to ease the use of Subgroup Discovery (SD) algorithms for machine learning and data science. The Subgroups Library offers several advantages: (1) Efficiency Enhancement: Developed in native Python, unlike other software available, the library prioritizes efficiency to ensure seamless execution of SD algorithms; (2) User-Friendly Interface: Modeled after the popular scikit-learn framework, the library boasts an intuitive interface, streamlining the utilization process for practitioners and non-expert programmers; (3) Trustworthy Algorithm Implementations: Drawing from scientific publications authored by leading experts, the Subgroups Library incorporates rigorously tested algorithmic implementations, ensuring reliability and accuracy in results; (4) Customization and Expansion: The modular architecture of the library facilitates effortless integration of additional quality measures, data structures, and SD algorithms, empowering users to tailor their analyses to specific needs and explore new avenues of research. Furthermore, the Subgroups Library has been successfully employed in diverse scientific papers and projects, underscoring its efficacy and versatility as a valuable tool for SD exploration and application.
SNRGAN: The Semi Noise Reduction GAN for Image Denoising
Authors: Mehrshad Momen-Tayefeh, Mehrdad Momen-Tayefeh, Fatemeh Zahra Hasheminasab, S. AmirAli Gh. Ghahramani
Date: 2024-04-25 00:00:00
Proceeding: Conference 2024 4
Tasks: Denoising, Generative Adversarial Network, Image Denoising

Conventional noise reduction methods often fail to effectively handle high levels of noise, leading to artifacts and distortions. This paper proposes a Generative Adversarial Network (GAN) approach for noise reduction with low complexity. The proposed Semi Noise Reduction GAN (SNRGAN) effectively learns the underlying patterns of noise and generates denoised versions of noisy images, even with different noise levels. Training our model on three diverse datasets yielded admissible results, as evidenced by superior PSNR and NMSE scores. Furthermore, our model excelled in both subjective evaluations and objective metrics and its efficacy in handling elevated noise levels positions it as a promising solution for real-world applications.
A Way to Synthetic Triple Difference
Authors: Castiel Chen Zhuang
Date: 2024-09-18 00:00:00
Tasks: Causal Inference

This paper discusses a practical approach that combines synthetic control with triple difference to address violations of the parallel trends assumption. By transforming triple difference into a DID structure, we can apply synthetic control to a triple-difference framework, enabling more robust estimates when parallel trends are violated across multiple dimensions. The proposed procedure is applied to a real-world dataset to illustrate when and how we should apply this practice, while cautions are presented afterwards. This method contributes to improving causal inference in policy evaluations and offers a valuable tool for researchers dealing with heterogeneous treatment effects across subgroups.
Machine listening in a neonatal intensive care unit
Authors: Modan Tailleur, Vincent Lostanlen, Jean-Philippe Rivière, Pierre Aumond
Date: 2024-09-16 00:00:00
Tasks: Cloud Computing, Edge-computing, Time Series

Oxygenators, alarm devices, and footsteps are some of the most common sound sources in a hospital. Detecting them has scientific value for environmental psychology but comes with challenges of its own: namely, privacy preservation and limited labeled data. In this paper, we address these two challenges via a combination of edge computing and cloud computing. For privacy preservation, we have designed an acoustic sensor which computes third-octave spectrograms on the fly instead of recording audio waveforms. For sample-efficient machine learning, we have repurposed a pretrained audio neural network (PANN) via spectral transcoding and label space adaptation. A small-scale study in a neonatological intensive care unit (NICU) confirms that the time series of detected events align with another modality of measurement: i.e., electronic badges for parents and healthcare professionals. Hence, this paper demonstrates the feasibility of polyphonic machine listening in a hospital ward while guaranteeing privacy by design.
Analysis of flexible traffic control method in SDN
Authors: Marta Szymczyk
Date: 2024-09-15 00:00:00
Tasks: Management

The aim of this paper is to analyze methods of flexible control in SDN networks and to propose a self-developed solution that will enable intelligent adaptation of SDN controller performance. This work aims not only to review existing solutions, but also to develop an approach that will increase the efficiency and adaptability of network management. The project uses a modern type of machine learning, Reinforcement Learning, which allows autonomous decisions of a network that learns based on its choices in a dynamically changing environment, which is most similar to the way humans learn. The solution aims not only to improve the network's performance, but also its flexibility and real-time adaptability - flexible traffic control.
Iteration of Thought: Leveraging Inner Dialogue for Autonomous Large Language Model Reasoning
Authors: Santosh Kumar Radha, Yasamin Nouri Jelyani, Ara Ghukasyan, Oktay Goktas
Date: 2024-09-19 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Multi-hop Question Answering, Question Answering

Iterative human engagement is a common and effective means of leveraging the advanced language processing power of large language models (LLMs). Using well-structured prompts in a conversational manner, human users can effectively influence an LLM to develop more thoughtful and accurate responses. Motivated by this insight, we propose the Iteration of Thought (IoT) framework for enhancing LLM responses by generating "thought"-provoking prompts vis a vis an input query and the current iteration of an LLM's response. Unlike static or semi-static approaches, e.g. Chain of Thought (CoT) or Tree of Thoughts (ToT), IoT adapts its reasoning path dynamically, based on evolving context, and without generating alternate explorative thoughts which are ultimately discarded. The three components of the IoT framework are (1) an Inner Dialogue Agent (IDA) responsible for generating instructive, context-specific prompts; (2) an LLM Agent (LLMA) that processes these prompts to refine its responses; and (3) an iterative prompting loop that implements a conversation between the former two components. We introduce two variants of our framework: Autonomous Iteration of Thought (AIoT), where an LLM decides when to stop iterating, and Guided Iteration of Thought (GIoT), which always forces a fixed number iterations. We investigate the performance of IoT across various datasets, spanning complex reasoning tasks from the GPQA dataset, explorative problem-solving in Game of 24, puzzle solving in Mini Crosswords, and multi-hop question answering from the HotpotQA dataset. Our results show that IoT represents a viable paradigm for autonomous response refinement in LLMs, showcasing significant improvements over CoT and thereby enabling more adaptive and efficient reasoning systems that minimize human intervention.
Domain-stratified Training for Cross-organ and Cross-scanner Adenocarcinoma Segmentation in the COSAS 2024 Challenge
Authors: Huang Jiayan, Ji Zheng, Kuang Jinbo, Xu Shuoyu
Date: 2024-09-19 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Task 2

This manuscript presents an image segmentation algorithm developed for the Cross-Organ and Cross-Scanner Adenocarcinoma Segmentation (COSAS 2024) challenge. We adopted an organ-stratified and scanner-stratified approach to train multiple Upernet-based segmentation models and subsequently ensembled the results. Despite the challenges posed by the varying tumor characteristics across different organs and the differing imaging conditions of various scanners, our method achieved a final test score of 0.7643 for Task 1 and 0.8354 for Task 2. These results demonstrate the adaptability and efficacy of our approach across diverse conditions. Our model's ability to generalize across various datasets underscores its potential for real-world applications.
Mixture of Diverse Size Experts
Authors: Manxi Sun, Wei Liu, Jian Luan, Pengzhi Gao, Bin Wang
Date: 2024-09-18 00:00:00
Tasks: Mixture-of-Experts

The Sparsely-Activated Mixture-of-Experts (MoE) has gained increasing popularity for scaling up large language models (LLMs) without exploding computational costs. Despite its success, the current design faces a challenge where all experts have the same size, limiting the ability of tokens to choose the experts with the most appropriate size for generating the next token. In this paper, we propose the Mixture of Diverse Size Experts (MoDSE), a new MoE architecture with layers designed to have experts of different sizes. Our analysis of difficult token generation tasks shows that experts of various sizes achieve better predictions, and the routing path of the experts tends to be stable after a training period. However, having experts of diverse sizes can lead to uneven workload distribution. To tackle this limitation, we introduce an expert-pair allocation strategy to evenly distribute the workload across multiple GPUs. Comprehensive evaluations across multiple benchmarks demonstrate the effectiveness of MoDSE, as it outperforms existing MoEs by allocating the parameter budget to experts adaptively while maintaining the same total parameter size and the number of experts.
ViolinDiff: Enhancing Expressive Violin Synthesis with Pitch Bend Conditioning
Authors: Daewoong Kim, Hao-Wen Dong, Dasaem Jeong
Date: 2024-09-19 00:00:00
Tasks: Audio Synthesis

Modeling the natural contour of fundamental frequency (F0) plays a critical role in music audio synthesis. However, transcribing and managing multiple F0 contours in polyphonic music is challenging, and explicit F0 contour modeling has not yet been explored for polyphonic instrumental synthesis. In this paper, we present ViolinDiff, a two-stage diffusion-based synthesis framework. For a given violin MIDI file, the first stage estimates the F0 contour as pitch bend information, and the second stage generates mel spectrogram incorporating these expressive details. The quantitative metrics and listening test results show that the proposed model generates more realistic violin sounds than the model without explicit pitch bend modeling. Audio samples are available online: daewoung.github.io/ViolinDiff-Demo.
An Occlusion‐aware Edge‐Based Method for Monocular 3D Object Tracking using Edge Confidence.
Authors: Hong Huang, Fan Zhong, Yuqing Sun, Xueying Qin
Date: 2020-11-24 00:00:00
Proceeding: Paciﬁc Graphics 2020 11
Tasks: 3D Object Tracking, Multi-Object Tracking, Object, Object Tracking, Pose Tracking

We propose an edge-based method for 6DOF pose tracking of rigid objects using a monocular RGB camera. One of the critical problem for edge-based methods is to search the object contour points in the image corresponding to the known 3D model points. However, previous methods often produce false object contour points in case of cluttered backgrounds and partial occlusions. In this paper, we propose a novel edge-based 3D objects tracking method to tackle this problem. To search the object contour points, foreground and background clutter points are first filtered out using edge color cue, then object contour points are searched by maximizing their edge confidence which combines edge color and distance cues. Furthermore, the edge confidence is integrated into the edge-based energy function to reduce the influence of false contour points caused by cluttered backgrounds and partial occlusions. We also extend our method to multi-object tracking which can handle mutual occlusions. We compare our method with the recent state-of-art methods on challenging public datasets. Experiments demonstrate that our method improves robustness and accuracy against cluttered backgrounds and partial occlusions.
Optical Flow Matters: an Empirical Comparative Study on Fusing Monocular Extracted Modalities for Better Steering
Authors: Fouad Makiyeh, Mark Bastourous, Anass Bairouk, Wei Xiao, Mirjana Maras, Tsun-Hsuan Wangb, Marc Blanchon, Ramin Hasani, Patrick Chareyre, Daniela Rus
Date: 2024-09-18 00:00:00
Tasks: Autonomous Navigation, Autonomous Vehicles, Decision Making, Depth Completion, Optical Flow Estimation, Self-Driving Cars

Autonomous vehicle navigation is a key challenge in artificial intelligence, requiring robust and accurate decision-making processes. This research introduces a new end-to-end method that exploits multimodal information from a single monocular camera to improve the steering predictions for self-driving cars. Unlike conventional models that require several sensors which can be costly and complex or rely exclusively on RGB images that may not be robust enough under different conditions, our model significantly improves vehicle steering prediction performance from a single visual sensor. By focusing on the fusion of RGB imagery with depth completion information or optical flow data, we propose a comprehensive framework that integrates these modalities through both early and hybrid fusion techniques. We use three distinct neural network models to implement our approach: Convolution Neural Network - Neutral Circuit Policy (CNN-NCP) , Variational Auto Encoder - Long Short-Term Memory (VAE-LSTM) , and Neural Circuit Policy architecture VAE-NCP. By incorporating optical flow into the decision-making process, our method significantly advances autonomous navigation. Empirical results from our comparative study using Boston driving data show that our model, which integrates image and motion information, is robust and reliable. It outperforms state-of-the-art approaches that do not use optical flow, reducing the steering estimation error by 31%. This demonstrates the potential of optical flow data, combined with advanced neural network architectures (a CNN-based structure for fusing data and a Recurrence-based network for inferring a command from latent space), to enhance the performance of autonomous vehicles steering estimation.
Semiparametric Bayesian Testing Procedure for Noninferiority Trials with Binary Endpoints
Authors: Muhtarjan Osman and Sujit K. Ghosh
Date: 2011-08-10 00:00:00
Proceeding: Journal of Biopharmaceutical Statistics 2011 8
Tasks: Decision Making

A semiparametric testing approach based on the Bayes factor is developed for
non-inferiority trials with binary endpoints. The proposed method is shown to work
for a broad class of hypotheses by accommodating a variety of dissimilarity measures
between two binomial parameters. Two of the unique features of the proposed testing
procedure include: (i) construction of a flexible class of conjugate priors using a
mixture of beta densities to maintain approximate equality of prior probabilities of
the competing hypotheses; and (ii) automatic determination of the cutoff value of
the Bayes factor to facilitate the decision making process. In contrast to the use of
Jeffreys’s rule of thumb, two forms of total weighted average error criteria are used to
determine the cutoff value. Through several simulation studies it is demonstrated that
the proposed Bayesian procedure has competitive frequentist properties of controlling
type I error as compared to the default frequentist test and meanwhile the proposed
criterion improves the statistical power, especially in small samples. The method is
further illustrated using the data from a streptococcal pharyngitis clinical trial.
Signal reconstruction in diffusion-based molecular communication
Authors: Baris Atakan, Fatih Gulec
Date: 2019-01-31 00:00:00

Molecular Communication (MC) is an important nanoscale communication paradigm, which is employed for the interconnection of the nanomachines (NMs) to form nanonetworks. A transmitter NM (TN) sends the information symbols by emitting molecules into the transmission medium and a receiver NM (RN) receives the information symbols by sensing the molecule concentration. In this paper, a model of how a RN measures and reconstructs the molecular signal is proposed. The signal around the RN is assumed to be a Gaussian random process instead of deterministic approach in a more realistic way. After the reconstructed signal is derived as a Doubly Stochastic Poisson Process (DSPP), the distortion between the signal around the RN and the reconstructed signal is derived as a new performance parameter in MC systems. The derived distortion which is a function of system parameters such as RN radius, sampling period and the diffusion coefficient of the channel, is shown to be valid by employing random walk simulations. Then, it is shown that the original signal can be satisfactorily reconstructed with a sufficiently low-level of distortion. Finally, optimum RN design parameters, namely RN radius, sampling period and sampling frequency, are derived by minimizing the signal distortion. The simulation results reveal that there is a trade-off among the RN design parameters which can be jointly set for a desired signal distortion.
A mmWave Software-Defined Array Platform for Wireless Experimentation at 24-29.5 GHz
Authors: Ashwini Pondeycherry Ganesh, Anthony Perre, Alphan Sahin, Ismail Guvenc, Brian A. Floyd
Date: 2024-09-17 00:00:00

Advanced millimeter-wave software-defined array (SDA) platforms, or testbeds at affordable costs and high performance are essential for the wireless community. In this paper, we present a low-cost, portable, and programmable SDA that allows for accessible research and experimentation in real time. The proposed platform is based on a 16-element phased-array transceiver operating across 24-29.5 GHz, integrated with a radio-frequency system-on-chip board that provides data conversion and baseband signal-processing capabilities. All radio-communication parameters and phased-array beam configurations are controlled through a high-level application program interface. We present measurements evaluating the beamforming and communication link performance. Our experimental results validate that the SDA has a beam scan range of -45 to +45 degrees (azimuth), a 3 dB beamwidth of 20 degrees, and support up to a throughput of 1.613 Gb/s using 64-QAM. The signal-to-noise ratio is as high as 30 dB at short-range distances when the transmit and receive beams are aligned.