OpenRaiser/Intern-Atlas huggingface dataset:
Real-Time Parallel Trajectory Optimization with Spatiotemporal Safety Constraints for Autonomous Driving in Congested Traffic
Venue: arXiv cs.RO 2023
Year: 2023

Multi-modal behaviors exhibited by surrounding vehicles (SVs) can typically lead to traffic congestion and reduce the travel efficiency of autonomous vehicles (AVs) in dense traffic. This paper proposes a real-time parallel trajectory optimization method for the AV to achieve high travel efficiency in dynamic and congested environments. A spatiotemporal safety module is developed to facilitate the safe interaction between the AV and SVs in the presence of trajectory prediction errors resulting from the multi-modal behaviors of the SVs. By leveraging multiple shooting and constraint transcription, we transform the trajectory optimization problem into a nonlinear programming problem, which allows for the use of optimization solvers and parallel computing techniques to generate multiple feasible trajectories in parallel. Subsequently, these spatiotemporal trajectories are fed into a multi-objective evaluation module considering both safety and efficiency objectives, such that the optimal feasible trajectory corresponding to the optimal target lane can be selected. The proposed framework is validated through simulations in a dense and congested driving scenario with multiple uncertain SVs. The results demonstrate that our method enables the AV to safely navigate through a dense and congested traffic scenario while achieving high travel efficiency and task accuracy in real time.
Semantic Latent Decomposition with Normalizing Flows for Face Editing
Venue: arXiv cs.CV 2023
Year: 2023

Navigating in the latent space of StyleGAN has shown effectiveness for face editing. However, the resulting methods usually encounter challenges in complicated navigation due to the entanglement among different attributes in the latent space. To address this issue, this paper proposes a novel framework, termed SDFlow, with a semantic decomposition in original latent space using continuous conditional normalizing flows. Specifically, SDFlow decomposes the original latent code into different irrelevant variables by jointly optimizing two components: (i) a semantic encoder to estimate semantic variables from input faces and (ii) a flow-based transformation module to map the latent code into a semantic-irrelevant variable in Gaussian distribution, conditioned on the learned semantic variables. To eliminate the entanglement between variables, we employ a disentangled learning strategy under a mutual information framework, thereby providing precise manipulation controls. Experimental results demonstrate that SDFlow outperforms existing state-of-the-art face editing methods both qualitatively and quantitatively. The source code is made available at https://github.com/phil329/SDFlow.
DynamicScore: a Novel Metric for Quantifying Graph Dynamics
Venue: arXiv cs.DM 2023
Year: 2023

This study introduces a new metric called ''DynamicScore'' to evaluate the dynamics of graphs. It can be applied to both vertices and edges. Unlike traditional metrics, DynamicScore not only measures changes in the number of vertices or edges between consecutive time steps, but also takes into account the composition of these sets. To illustrate the possible contributions of this metric, we calculate it for increasing networks of preferential attachment (Barab{á}si-Albert model) and Edge-Markovian graphs. The results improve our understanding of the dynamics inherent in these generated evolving graphs.
Empirical study on the efficiency of Spiking Neural Networks with axonal delays, and algorithm-hardware benchmarking
Venue: arXiv cs.ET 2023
Year: 2023

The role of axonal synaptic delays in the efficacy and performance of artificial neural networks has been largely unexplored. In step-based analog-valued neural network models (ANNs), the concept is almost absent. In their spiking neuroscience-inspired counterparts, there is hardly a systematic account of their effects on model performance in terms of accuracy and number of synaptic operations.This paper proposes a methodology for accounting for axonal delays in the training loop of deep Spiking Neural Networks (SNNs), intending to efficiently solve machine learning tasks on data with rich temporal dependencies. We then conduct an empirical study of the effects of axonal delays on model performance during inference for the Adding task, a benchmark for sequential regression, and for the Spiking Heidelberg Digits dataset (SHD), commonly used for evaluating event-driven models. Quantitative results on the SHD show that SNNs incorporating axonal delays instead of explicit recurrent synapses achieve state-of-the-art, over 90% test accuracy while needing less than half trainable synapses. Additionally, we estimate the required memory in terms of total parameters and energy consumption of accomodating such delay-trained models on a modern neuromorphic accelerator. These estimations are based on the number of synaptic operations and the reference GF-22nm FDX CMOS technology. As a result, we demonstrate that a reduced parameterization, which incorporates axonal delays, leads to approximately 90% energy and memory reduction in digital hardware implementations for a similar performance in the aforementioned task.
SPEChpc 2021 Benchmarks on Ice Lake and Sapphire Rapids Infiniband Clusters: A Performance and Energy Case Study
Venue: arXiv cs.PF 2023
Year: 2023

In this work, fundamental performance, power, and energy characteristics of the full SPEChpc 2021 benchmark suite are assessed on two different clusters based on Intel Ice Lake and Sapphire Rapids CPUs using the MPI-only codes' variants. We use memory bandwidth, data volume, and scalability metrics in order to categorize the benchmarks and pinpoint relevant performance and scalability bottlenecks on the node and cluster levels. Common patterns such as memory bandwidth limitation, dominating communication and synchronization overhead, MPI serialization, superlinear scaling, and alignment issues could be identified, in isolation or in combination, showing that SPEChpc 2021 is representative of many HPC workloads. Power dissipation and energy measurements indicate that the modern Intel server CPUs have such a high idle power level that race-to-idle is the paramount strategy for energy to solution and energy-delay product minimization. On the chip level, only memory-bound code shows a clear advantage of Sapphire Rapids compared to Ice Lake in terms of energy to solution.
Collective PV-RCNN: A Novel Fusion Technique using Collective Detections for Enhanced Local LiDAR-Based Perception
Venue: arXiv cs.CV 2023
Year: 2023

Comprehensive perception of the environment is crucial for the safe operation of autonomous vehicles. However, the perception capabilities of autonomous vehicles are limited due to occlusions, limited sensor ranges, or environmental influences. Collective Perception (CP) aims to mitigate these problems by enabling the exchange of information between vehicles. A major challenge in CP is the fusion of the exchanged information. Due to the enormous bandwidth requirement of early fusion approaches and the interchangeability issues of intermediate fusion approaches, only the late fusion of shared detections is practical. Current late fusion approaches neglect valuable information for local detection, this is why we propose a novel fusion method to fuse the detections of cooperative vehicles within the local LiDAR-based detection pipeline. Therefore, we present Collective PV-RCNN (CPV-RCNN), which extends the PV-RCNN++ framework to fuse collective detections. Code is available at https://github.com/ekut-es
SlideSpeech: A Large-Scale Slide-Enriched Audio-Visual Corpus
Venue: arXiv cs.SD 2023
Year: 2023

Multi-Modal automatic speech recognition (ASR) techniques aim to leverage additional modalities to improve the performance of speech recognition systems. While existing approaches primarily focus on video or contextual information, the utilization of extra supplementary textual information has been overlooked. Recognizing the abundance of online conference videos with slides, which provide rich domain-specific information in the form of text and images, we release SlideSpeech, a large-scale audio-visual corpus enriched with slides. The corpus contains 1,705 videos, 1,000+ hours, with 473 hours of high-quality transcribed speech. Moreover, the corpus contains a significant amount of real-time synchronized slides. In this work, we present the pipeline for constructing the corpus and propose baseline methods for utilizing text information in the visual slide context. Through the application of keyword extraction and contextual ASR methods in the benchmark system, we demonstrate the potential of improving speech recognition performance by incorporating textual information from supplementary video slides.
Design and Validation of a Wireless Drone Docking Station
Venue: arXiv cs.RO 2023
Year: 2023

Drones are increasingly operating autonomously, and the need for extending drone power autonomy is rapidly increasing. One of the most promising solutions to extend drone power autonomy is the use of docking stations to support both landing and recharging of the drone. To this end, we introduce a novel wireless drone docking station with three commercial wireless charging modules. We have developed two independent units, both in mechanical and electrical aspects: the energy transmitting unit and the energy receiving unit. We have also studied the efficiency of wireless power transfer and demonstrated the advantages of connecting three receiver modules connected in series and parallel. We have achieved maximum output power of 96.5 W with a power transfer efficiency of 56.6% for the series connection of coils. Finally, we implemented the system in practice on a drone and tested both energy transfer and landing.
Emissions and energy efficiency on large-scale high performance computing facilities: ARCHER2 UK national supercomputing service case study
Venue: arXiv cs.DC 2023
Year: 2023

Large supercomputing facilities are critical to research in many areas that impact on decisions such as how to address the current climate emergency. For example, climate modelling, renewable energy facility design and new battery technologies. However, these systems themselves are a source of large amounts of emissions due to the embodied emissions associated with their construction, transport, and decommissioning; and the power consumption associated with running the facility. Recently, the UK National Supercomputing Service, ARCHER2, has been analysing the impact of the facility in terms of energy and emissions. Based on this work, we have made changes to the operation of the service that give a cumulative saving of more than 20% in power draw of the computational resources with all application benchmarks showing reduced power to solution. In this paper, we describe our analysis and the changes made to the operation of the service to improve its energy efficiency, and thereby reduce its climate impacts.
Many Cores, Many Models: GPU Programming Model vs. Vendor Compatibility Overview
Venue: arXiv cs.DC 2023
Year: 2023

In recent history, GPUs became a key driver of compute performance in HPC. With the installation of the Frontier supercomputer, they became the enablers of the Exascale era; further largest-scale installations are in progress (Aurora, El Capitan, JUPITER). But the early-day dominance by NVIDIA and their CUDA programming model has changed: The current HPC GPU landscape features three vendors (AMD, Intel, NVIDIA), each with native and derived programming models. The choices are ample, but not all models are supported on all platforms, especially if support for Fortran is needed; in addition, some restrictions might apply. It is hard for scientific programmers to navigate this abundance of choices and limits. This paper gives a guide by matching the GPU platforms with supported programming models, presented in a concise table and further elaborated in detailed comments. An assessment is made regarding the level of support of a model on a platform.
Personality Detection and Analysis using Twitter Data
Venue: arXiv cs.CL 2023
Year: 2023

Personality types are important in various fields as they hold relevant information about the characteristics of a human being in an explainable format. They are often good predictors of a person's behaviors in a particular environment and have applications ranging from candidate selection to marketing and mental health. Recently automatic detection of personality traits from texts has gained significant attention in computational linguistics. Most personality detection and analysis methods have focused on small datasets making their experimental observations often limited. To bridge this gap, we focus on collecting and releasing the largest automatically curated dataset for the research community which has 152 million tweets and 56 thousand data points for the Myers-Briggs personality type (MBTI) prediction task. We perform a series of extensive qualitative and quantitative studies on our dataset to analyze the data patterns in a better way and infer conclusions. We show how our intriguing analysis results often follow natural intuition. We also perform a series of ablation studies to show how the baselines perform for our dataset.
On the detection of Out-Of-Distribution samples in Multiple Instance Learning
Venue: arXiv cs.CV 2023
Year: 2023

The deployment of machine learning solutions in real-world scenarios often involves addressing the challenge of out-of-distribution (OOD) detection. While significant efforts have been devoted to OOD detection in classical supervised settings, the context of weakly supervised learning, particularly the Multiple Instance Learning (MIL) framework, remains under-explored. In this study, we tackle this challenge by adapting post-hoc OOD detection methods to the MIL setting while introducing a novel benchmark specifically designed to assess OOD detection performance in weakly supervised scenarios. Across extensive experiments based on diverse public datasets, KNN emerges as the best-performing method overall. However, it exhibits significant shortcomings on some datasets, emphasizing the complexity of this under-explored and challenging topic. Our findings shed light on the complex nature of OOD detection under the MIL framework, emphasizing the importance of developing novel, robust, and reliable methods that can generalize effectively in a weakly supervised context. The code for the paper is available here: https://github.com/loic-lb/OOD_MIL.
Distance-Aware eXplanation Based Learning
Venue: arXiv cs.CV 2023
Year: 2023

eXplanation Based Learning (XBL) is an interactive learning approach that provides a transparent method of training deep learning models by interacting with their explanations. XBL augments loss functions to penalize a model based on deviation of its explanations from user annotation of image features. The literature on XBL mostly depends on the intersection of visual model explanations and image feature annotations. We present a method to add a distance-aware explanation loss to categorical losses that trains a learner to focus on important regions of a training dataset. Distance is an appropriate approach for calculating explanation loss since visual model explanations such as Gradient-weighted Class Activation Mapping (Grad-CAMs) are not strictly bounded as annotations and their intersections may not provide complete information on the deviation of a model's focus from relevant image regions. In addition to assessing our model using existing metrics, we propose an interpretability metric for evaluating visual feature-attribution based model explanations that is more informative of the model's performance than existing metrics. We demonstrate performance of our proposed method on three image classification tasks.
Multiplierless Design of High-Speed Very Large Constant Multiplications
Venue: arXiv cs.CR 2023
Year: 2023

In cryptographic algorithms, the constants to be multiplied by a variable can be very large due to security requirements. Thus, the hardware complexity of such algorithms heavily depends on the design architecture handling large constants. In this paper, we introduce an electronic design automation tool, called LEIGER, which can automatically generate the realizations of very large constant multiplications for low-complexity and high-speed applications, targeting the ASIC design platform. LEIGER can utilize the shift-adds architecture and use 3-input operations, i.e., carry-save adders (CSAs), where the number of CSAs is reduced using a prominent optimization algorithm. It can also generate constant multiplications under a hybrid design architecture, where 2-and 3-input operations are used at different stages. Moreover, it can describe constant multiplications under a design architecture using compressor trees. As a case study, high-speed Montgomery multiplication, which is a fundamental operation in cryptographic algorithms, is designed with its constant multiplication block realized under the proposed architectures. Experimental results indicate that LEIGER enables a designer to explore the trade-off between area and delay of the very large constant and Montgomery multiplications and leads to designs with area-delay product, latency, and energy consumption values significantly better than those obtained by a recently proposed algorithm.
Solving Partial Differential Equations with Monte Carlo / Random Walk on an Analog-Digital Hybrid Computer
Venue: arXiv cs.ET 2023
Year: 2023

Current digital computers are about to hit basic physical boundaries with respect to integration density, clock frequencies, and particularly energy consumption. This requires the application of new computing paradigms, such as quantum and analog computing in the near future. Although neither quantum nor analog computer are general purpose computers they will play an important role as co-processors to offload certain classes of compute intensive tasks from classic digital computers, thereby not only reducing run time but also and foremost power consumption. In this work, we describe a random walk approach to the solution of certain types of partial differential equations which is well suited for combinations of digital and analog computers (hybrid computers). The experiments were performed on an Analog Paradigm Model-1 analog computer attached to a digital computer by means of a hybrid interface. At the end we give some estimates of speedups and power consumption obtainable by using future analog computers on chip.
Creating a Systematic ESG (Environmental Social Governance) Scoring System Using Social Network Analysis and Machine Learning for More Sustainable Company Practices
Venue: arXiv cs.SI 2023
Year: 2023

Environmental Social Governance (ESG) is a widely used metric that measures the sustainability of a company practices. Currently, ESG is determined using self-reported corporate filings, which allows companies to portray themselves in an artificially positive light. As a result, ESG evaluation is subjective and inconsistent across raters, giving executives mixed signals on what to improve. This project aims to create a data-driven ESG evaluation system that can provide better guidance and more systemized scores by incorporating social sentiment. Social sentiment allows for more balanced perspectives which directly highlight public opinion, helping companies create more focused and impactful initiatives. To build this, Python web scrapers were developed to collect data from Wikipedia, Twitter, LinkedIn, and Google News for the S&P 500 companies. Data was then cleaned and passed through NLP algorithms to obtain sentiment scores for ESG subcategories. Using these features, machine-learning algorithms were trained and calibrated to S&P Global ESG Ratings to test their predictive capabilities. The Random-Forest model was the strongest model with a mean absolute error of 13.4% and a correlation of 26.1% (p-value 0.0372), showing encouraging results. Overall, measuring ESG social sentiment across sub-categories can help executives focus efforts on areas people care about most. Furthermore, this data-driven methodology can provide ratings for companies without coverage, allowing more socially responsible firms to thrive.
Effective Proxy for Human Labeling: Ensemble Disagreement Scores in Large Language Models for Industrial NLP
Venue: arXiv cs.CL 2023
Year: 2023

Large language models (LLMs) have demonstrated significant capability to generalize across a large number of NLP tasks. For industry applications, it is imperative to assess the performance of the LLM on unlabeled production data from time to time to validate for a real-world setting. Human labeling to assess model error requires considerable expense and time delay. Here we demonstrate that ensemble disagreement scores work well as a proxy for human labeling for language models in zero-shot, few-shot, and fine-tuned settings, per our evaluation on keyphrase extraction (KPE) task. We measure fidelity of the results by comparing to true error measured from human labeled ground truth. We contrast with the alternative of using another LLM as a source of machine labels, or silver labels. Results across various languages and domains show disagreement scores provide a better estimation of model performance with mean average error (MAE) as low as 0.4% and on average 13.8% better than using silver labels.
Task-Oriented Cross-System Design for Timely and Accurate Modeling in the Metaverse
Venue: arXiv cs.RO 2023
Year: 2023

In this paper, we establish a task-oriented cross-system design framework to minimize the required packet rate for timely and accurate modeling of a real-world robotic arm in the Metaverse, where sensing, communication, prediction, control, and rendering are considered. To optimize a scheduling policy and prediction horizons, we design a Constraint Proximal Policy Optimization(C-PPO) algorithm by integrating domain knowledge from relevant systems into the advanced reinforcement learning algorithm, Proximal Policy Optimization(PPO). Specifically, the Jacobian matrix for analyzing the motion of the robotic arm is included in the state of the C-PPO algorithm, and the Conditional Value-at-Risk(CVaR) of the state-value function characterizing the long-term modeling error is adopted in the constraint. Besides, the policy is represented by a two-branch neural network determining the scheduling policy and the prediction horizons, respectively. To evaluate our algorithm, we build a prototype including a real-world robotic arm and its digital model in the Metaverse. The experimental results indicate that domain knowledge helps to reduce the convergence time and the required packet rate by up to 50%, and the cross-system design framework outperforms a baseline framework in terms of the required packet rate and the tail distribution of the modeling error.