pwc-archive/papers-with-abstracts huggingface dataset:
Transformative Influence of LLM and AI Tools in Student Social Media Engagement: Analyzing Personalization, Communication Efficiency, and Collaborative Learning
Authors: Masoud Bashiri, Kamran Kowsari
Date: 2024-06-15 00:00:00

The advent of Large Language Models (LLMs) and Artificial Intelligence (AI) tools has revolutionized various facets of our lives, particularly in the realm of social media. For students, these advancements have unlocked unprecedented opportunities for learning, collaboration, and personal growth. AI-driven applications are transforming how students interact with social media, offering personalized content and recommendations, and enabling smarter, more efficient communication. Recent studies utilizing data from UniversityCube underscore the profound impact of AI tools on students' academic and social experiences. These studies reveal that students engaging with AI-enhanced social media platforms report higher academic performance, enhanced critical thinking skills, and increased engagement in collaborative projects. Moreover, AI tools assist in filtering out distracting content, allowing students to concentrate more on educational materials and pertinent discussions. The integration of LLMs in social media has further facilitated improved peer-to-peer communication and mentorship opportunities. AI algorithms effectively match students based on shared academic interests and career goals, fostering a supportive and intellectually stimulating online community, thereby contributing to increased student satisfaction and retention rates. In this article, we delve into the data provided by UniversityCube to explore how LLMs and AI tools are specifically transforming social media for students. Through case studies and statistical analyses, we offer a comprehensive understanding of the educational and social benefits these technologies offer. Our exploration highlights the potential of AI-driven tools to create a more enriched, efficient, and supportive educational environment for students in the digital age.
Digital Twin-based Driver Risk-Aware Intelligent Mobility Analytics for Urban Transportation Management
Authors: Tao Li, Zilin Bian, Haozhe Lei, Fan Zuo, Ya-Ting Yang, Quanyan Zhu, Zhenning Li, Zhibin Chen, Kaan Ozbay
Date: 2024-07-03 00:00:00
Tasks: Management

Traditional mobility management strategies emphasize macro-level mobility oversight from traffic-sensing infrastructures, often overlooking safety risks that directly affect road users. To address this, we propose a Digital Twin-based Driver Risk-Aware Intelligent Mobility Analytics (DT-DIMA) system. The DT-DIMA system integrates real-time traffic information from pan-tilt-cameras (PTCs), synchronizes this data into a digital twin to accurately replicate the physical world, and predicts network-wide mobility and safety risks in real time. The system's innovation lies in its integration of spatial-temporal modeling, simulation, and online control modules. Tested and evaluated under normal traffic conditions and incidental situations (e.g., unexpected accidents, pre-planned work zones) in a simulated testbed in Brooklyn, New York, DT-DIMA demonstrated mean absolute percentage errors (MAPEs) ranging from 8.40% to 15.11% in estimating network-level traffic volume and MAPEs from 0.85% to 12.97% in network-level safety risk prediction. In addition, the highly accurate safety risk prediction enables PTCs to preemptively monitor road segments with high driving risks before incidents take place. Such proactive PTC surveillance creates around a 5-minute lead time in capturing traffic incidents. The DT-DIMA system enables transportation managers to understand mobility not only in terms of traffic patterns but also driver-experienced safety risks, allowing for proactive resource allocation in response to various traffic situations. To the authors' best knowledge, DT-DIMA is the first urban mobility management system that considers both mobility and safety risks based on digital twin architecture.
RogueGPT: dis-ethical tuning transforms ChatGPT4 into a Rogue AI in 158 Words
Authors: Alessio Buscemi, Daniele Proverbio
Date: 2024-06-11 00:00:00

The ethical implications and potentials for misuse of Generative Artificial Intelligence are increasingly worrying topics. This paper explores how easily the default ethical guardrails of ChatGPT, using its latest customization features, can be bypassed by simple prompts and fine-tuning, that can be effortlessly accessed by the broad public. This malevolently altered version of ChatGPT, nicknamed "RogueGPT", responded with worrying behaviours, beyond those triggered by jailbreak prompts. We conduct an empirical study of RogueGPT responses, assessing its flexibility in answering questions pertaining to what should be disallowed usage. Our findings raise significant concerns about the model's knowledge about topics like illegal drug production, torture methods and terrorism. The ease of driving ChatGPT astray, coupled with its global accessibility, highlights severe issues regarding the data quality used for training the foundational model and the implementation of ethical safeguards. We thus underline the responsibilities and dangers of user-driven modifications, and the broader effects that these may have on the design of safeguarding and ethical modules implemented by AI programmers.
Learning Visual Grounding from Generative Vision and Language Model
Authors: Shijie Wang, Dahun Kim, Ali Taalimi, Chen Sun, Weicheng Kuo
Date: 2024-07-18 00:00:00
Tasks: Attribute, Language Modeling, Language Modelling, Object, object-detection, Object Detection, Referring Expression, Referring Expression Comprehension, text annotation, Visual Grounding

Visual grounding tasks aim to localize image regions based on natural language references. In this work, we explore whether generative VLMs predominantly trained on image-text data could be leveraged to scale up the text annotation of visual grounding data. We find that grounding knowledge already exists in generative VLM and can be elicited by proper prompting. We thus prompt a VLM to generate object-level descriptions by feeding it object regions from existing object detection datasets. We further propose attribute modeling to explicitly capture the important object attributes, and spatial relation modeling to capture inter-object relationship, both of which are common linguistic pattern in referring expression. Our constructed dataset (500K images, 1M objects, 16M referring expressions) is one of the largest grounding datasets to date, and the first grounding dataset with purely model-generated queries and human-annotated objects. To verify the quality of this data, we conduct zero-shot transfer experiments to the popular RefCOCO benchmarks for both referring expression comprehension (REC) and segmentation (RES) tasks. On both tasks, our model significantly outperform the state-of-the-art approaches without using human annotated visual grounding data. Our results demonstrate the promise of generative VLM to scale up visual grounding in the real world. Code and models will be released.
ViT LoS V2X: Vision Transformers for Environment-aware LoS Blockage Prediction for 6G Vehicular Networks
Authors: Ghazi Gharsallah, Georges Kaddoum
Date: 2024-06-27 00:00:00
Tasks: Beam Prediction

As wireless communication technology progresses towards the sixth generation (6G), high-frequency millimeter-wave (mmWave) communication has emerged as a promising candidate for enabling vehicular networks. It offers high data rates and low-latency communication. However, obstacles such as buildings, trees, and other vehicles can cause signal attenuation and blockage, leading to communication failures that can result in fatal accidents or traffic congestion. Predicting blockages is crucial for ensuring reliable and efficient communications. Furthermore, the advent of 6G technology is anticipated to integrate advanced sensing capabilities, utilizing a variety of sensor types. These sensors, ranging from traditional RF sensors to cameras and Lidar sensors, are expected to provide access to rich multimodal data, thereby enriching communication systems with a wealth of additional contextual information. Leveraging this multimodal data becomes essential for making precise network management decisions, including the crucial task of blockage detection. In this paper, we propose a Deep Learning (DL)-based approach that combines Convolutional Neural Networks (CNNs) and customized Vision Transformers (ViTs) to effectively extract essential information from multimodal data and predict blockages in vehicular networks. Our method capitalizes on the synergistic strengths of CNNs and ViTs to extract features from time-series multimodal data, which include images and beam vectors. To capture temporal dependencies between the extracted features and the blockage state at future time steps, we employ a Gated Recurrent Unit (GRU)-based architecture. Our results show that the proposed approach achieves high accuracy and outperforms state-of-the-art solutions, achieving more than $95\%$ accurate predictions.
A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention
Authors: Z Cui, P Zhou, X Wang, Z Zhang, Y Li, H Li, Y Zhang
Date: 2023-09-23 00:00:00
Proceeding: Remote Sensing 2023 9
Tasks: Blocking, Data Augmentation, Drone-view target localization, geo-localization, Image Retrieval, Retrieval, Triplet

Geo-localization has been widely applied as an important technique to get the longitude
and latitude for unmanned aerial vehicle (UAV) navigation in outdoor flight. Due to the possible
interference and blocking of GPS signals, the method based on image retrieval, which is less likely
to be interfered with, has received extensive attention in recent years. The geo-localization of UAVs
and satellites can be achieved by querying pre-obtained satellite images with GPS-tagged and drone
images from different perspectives. In this paper, an image transformation technique is used to extract
cross-view geo-localization information from UAVs and satellites. A single-stage training method
in UAV and satellite geo-localization is first proposed, which simultaneously realizes cross-view
feature extraction and image retrieval, and achieves higher accuracy than existing multi-stage training
techniques. A novel piecewise soft-margin triplet loss function is designed to avoid model parameters
being trapped in suboptimal sets caused by the lack of constraint on positive and negative samples.
The results illustrate that the proposed loss function enhances image retrieval accuracy and realizes
a better convergence. Moreover, a data augmentation method for satellite images is proposed to
overcome the disproportionate numbers of image samples. On the benchmark University-1652, the
proposed method achieves the state-of-the-art result with a 6.67% improvement in recall rate (R@1)
and 6.13% in average precision (AP). All codes will be publicized to promote reproducibility.
Iso-propagation vortices with OAM-independent size and divergence toward future faster optical communications
Authors: Wenxiang Yan, b Zhaozhong Chen, c Xian Long, b Yuan Gao, b Zheng Yuan, b Zhi-Cheng Ren, b Xi-Lin Wang, b Jianping Ding, a, D, * and Hui-Tian Wanga, B, *
Date: 2024-05-17 00:00:00
Proceeding: ADVANCED PHOTONICS 2024 5

Recognized in the 1990s, vortex beams’ ability to carry orbital angular momentum (OAM) has
significantly contributed to applications in optical manipulation and high-dimensional classical and
quantum information communication. However, inherent diffraction in free space results in the inevitable
expansion of beam size and divergence contingent upon the OAM, limiting vortex beams’ applicability in
areas such as spatial mode multiplexing communication, fiber-optic data transmission, and particle
manipulation. These domains necessitate vortex beams with OAM-independent propagation characteristics.
We introduce iso-propagation vortices (IPVs), vortex beams characterized by OAM-independent propagation
behavior, achieved through precise radial index configuration of Laguerre–Gaussian beams. IPVs display
notable transmission dynamics, including a reduced quality factor, resilience post-damage, and decreased
and uniform modal scattering under atmospheric turbulence. Their distinctive attributes render IPVs valuable
for potential applications in imaging, microscopy, optical communication, metrology, quantum information
processing, and light–matter interactions. Notably, within optical communication, the case study suggests
that the IPV basis, due to its OAM-independent propagation behavior, provides access to a more extensive
spectrum of data channels compared with conventional spatial multiplexing techniques, consequently
augmenting information capacity.
EfficientQAT: Efficient Quantization-Aware Training for Large Language Models
Authors: Mengzhao Chen, Wenqi Shao, Peng Xu, Jiahao Wang, Peng Gao, Kaipeng Zhang, Ping Luo
Date: 2024-07-10 00:00:00
Tasks: GPU, Quantization

Large language models (LLMs) are crucial in modern natural language processing and artificial intelligence. However, they face challenges in managing their significant memory requirements. Although quantization-aware training (QAT) offers a solution by reducing memory consumption through low-bit representations with minimal accuracy loss, it is impractical due to substantial training resources. To address this, we propose Efficient Quantization-Aware Training (EfficientQAT), a more feasible QAT algorithm. EfficientQAT involves two consecutive phases: Block-wise training of all parameters (Block-AP) and end-to-end training of quantization parameters (E2E-QP). To the best of our knowledge, Block-AP is the first method to enable direct training of all parameters in a block-wise manner, reducing accuracy loss in low-bit scenarios by enhancing the solution space during optimization. E2E-QP then trains only the quantization parameters (step sizes) end-to-end, further improving the performance of quantized models by considering interactions among all sub-modules. Extensive experiments demonstrate that EfficientQAT outperforms previous quantization methods across a range of models, including base LLMs, instruction-tuned LLMs, and multimodal LLMs, with scales from 7B to 70B parameters at various quantization bits. For instance, EfficientQAT obtains a 2-bit Llama-2-70B model on a single A100-80GB GPU in 41 hours, with less than 3 points accuracy degradation compared to the full precision (69.48 vs. 72.41). Code is available at https://github.com/OpenGVLab/EfficientQAT.
LaLaLoc++: Global Floor Plan Comprehension for Layout Localisation in Unvisited Environments
Authors: Henry Howard-Jenkins, Victor Adrian Prisacariu
Date: 2022-10-22 00:00:00
Proceeding: European Conference on Computer Vision 2022 10
Tasks: Camera Localization, Indoor Localization, Visual Localization

We present LaLaLoc++, a method for floor plan localisation in unvisited environments through latent representations of room layout. We perform localisation by aligning room layout inferred from a panorama image with the floor plan of a scene. To process a floor plan prior, previous methods required that the plan first be used to construct an explicit 3D representation of the scene. This process requires that assumptions be made about the scene geometry and can result in expensive steps becoming necessary, such as rendering. LaLaLoc++ instead introduces a global floor plan comprehension module that is able to efficiently infer structure densely and directly from the 2D plan, removing any need for explicit modelling or rendering. On the Structured3D dataset this module alone improves localisation accuracy by more than 31%, all while increasing throughput by an order of magnitude. Combined with the further addition of a transformer-based panorama embedding module, LaLaLoc++ improves accuracy over earlier methods by more than 37% with dramatically faster inference.
Synth4bench: a framework for generating synthetic genomics data for the evaluation of tumor-only somatic variant calling algorithms
Authors: Styliani-Christina Fragkouli, Nikos Pechlivanis, Anastasia Anastasiadou, Georgios Karakatsoulis, Aspasia Orfanou, Panagoula Kollia, Andreas Agathangelidis, Fotis Psomopoulos
Date: 2024-03-08 00:00:00
Proceeding: bioRxiv 2024 3
Tasks: Benchmarking, Synthetic Data Generation

Motivation
Somatic variant calling algorithms are widely used to detect genomic alterations associated with cancer. Evaluating their performance, even though being crucial, can be challenging due to the lack of high-quality ground truth datasets. To address this issue, we developed a synthetic data generation framework for benchmarking these algorithms, focusing on the TP53 gene, utilizing the NEATv3.3 simulator. We thoroughly evaluated the performance of Mutect2, Freebayes, VarDict, VarScan2 and LoFreq and compared their results with our synthetic ground truth, while observing their behavior. Synth4bench attempts to shed light on the underlying principles of each variant caller by presenting them with data from a given range across the genomics data feature space and inspecting their response.

Results 
Using synthetic dataset as ground truth provides an excellent approach for evaluating the performance of tumor-only somatic variant calling algorithms. Our findings are supported by an independent statistical analysis that was performed on the same data and output from all callers. Overall, synth4bench leverages the effort of benchmarking algorithms by offering the opportunity to utilize a generated ground truth dataset. This kind of framework is essential in the field of cancer genomics, where precision is an ultimate necessity, especially for variants of low frequency. In this context, our approach makes comparison of various algorithms transparent, straightforward and also enhances their comparability.
Attention-Guided Low-Rank Tensor Completion
Authors: Truong Thanh Nhat Mai, Edmund Y. Lam, Chul Lee
Date: 2024-07-17 00:00:00
Proceeding: IEEE Transactions on Pattern Analysis and Machine Intelligence 2024 7
Tasks: Image Restoration

Low-rank tensor completion (LRTC) aims to recover missing data of high-dimensional structures from a limited set of observed entries. Despite recent significant successes, the original structures of data tensors are still not effectively preserved in LRTC algorithms, yielding less accurate restoration results. Moreover, LRTC algorithms often incur high computational costs, which hinder their applicability. In this work, we propose an attention-guided low-rank tensor completion (AGTC) algorithm, which can faithfully restore the original structures of data tensors using deep unfolding attention-guided tensor factorization. First, we formulate the LRTC task as a robust factorization problem based on low-rank and sparse error assumptions. Low-rank tensor recovery is guided by an attention mechanism to better preserve the structures of the original data. We also develop implicit regularizers to compensate for modeling inaccuracies. Then, we solve the optimization problem by employing an iterative technique. Finally, we design a multistage deep network by unfolding the iterative algorithm, where each stage corresponds to an iteration of the algorithm; at each stage, the optimization variables and regularizers are updated by closed-form solutions and learned deep networks, respectively. Experimental results for high dynamic range imaging and hyperspectral image restoration show that the proposed algorithm outperforms state-of-the-art algorithms.
ITERTL: An Iterative Framework for Fine-tuning LLMs for RTL Code Generation
Authors: Peiyang Wu, Nan Guo, Xiao Xiao, WenMing Li, Xiaochun Ye, Dongrui Fan
Date: 2024-06-28 00:00:00
Tasks: Code Generation

Recently, large language models (LLMs) have demonstrated excellent performance in understanding human instructions and generating code, which has inspired researchers to explore the feasibility of generating RTL code with LLMs. However, the existing approaches to fine-tune LLMs on RTL codes typically are conducted on fixed datasets, which do not fully stimulate the capability of LLMs and require large amounts of reference data. To mitigate these issues , we introduce a simple yet effective iterative training paradigm named ITERTL. During each iteration, samples are drawn from the model trained in the previous cycle. Then these new samples are employed for training in this loop. Through this iterative approach, the distribution mismatch between the model and the training samples is reduced. Additionally, the model is thus enabled to explore a broader generative space and receive more comprehensive feedback. Theoretical analyses are conducted to investigate the mechanism of the effectiveness. Experimental results show the model trained through our proposed approach can compete with and even outperform the state-of-the-art (SOTA) open-source model with nearly 37\% reference samples, achieving remarkable 42.9\% and 62.2\% pass@1 rate on two VerilogEval evaluation datasets respectively. While using the same amount of reference samples, our method can achieved a relative improvement of 16.9\% and 12.5\% in pass@1 compared to the non-iterative method. This study facilitates the application of LLMs for generating RTL code in practical scenarios with limited data.
Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural Networks
Authors: Ziyi Huang, Henry Lam, Haofeng Zhang
Date: 2023-06-09 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Uncertainty Quantification

Uncertainty quantification (UQ) is important for reliability assessment and enhancement of machine learning models. In deep learning, uncertainties arise not only from data, but also from the training procedure that often injects substantial noises and biases. These hinder the attainment of statistical guarantees and, moreover, impose computational challenges on UQ due to the need for repeated network retraining. Building upon the recent neural tangent kernel theory, we create statistically guaranteed schemes to principally \emph{characterize}, and \emph{remove}, the uncertainty of over-parameterized neural networks with very low computation effort. In particular, our approach, based on what we call a procedural-noise-correcting (PNC) predictor, removes the procedural uncertainty by using only \emph{one} auxiliary network that is trained on a suitably labeled dataset, instead of many retrained networks employed in deep ensembles. Moreover, by combining our PNC predictor with suitable light-computation resampling methods, we build several approaches to construct asymptotically exact-coverage confidence intervals using as low as four trained networks without additional overheads.
Learning Efficient Surrogate Dynamic Models with Graph Spline Networks
Authors: Chuanbo Hua, Federico Berto, Michael Poli, Stefano Massaroli, Jinkyoo Park
Date: 2023-10-25 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Deep Learning

While complex simulations of physical systems have been widely used in engineering and scientific computing, lowering their often prohibitive computational requirements has only recently been tackled by deep learning approaches. In this paper, we present GraphSplineNets, a novel deep-learning method to speed up the forecasting of physical systems by reducing the grid size and number of iteration steps of deep surrogate models. Our method uses two differentiable orthogonal spline collocation methods to efficiently predict response at any location in time and space. Additionally, we introduce an adaptive collocation strategy in space to prioritize sampling from the most important regions. GraphSplineNets improve the accuracy-speedup tradeoff in forecasting various dynamical systems with increasing complexity, including the heat equation, damped wave propagation, Navier-Stokes equations, and real-world ocean currents in both regular and irregular domains.