OpenRaiser/Intern-Atlas huggingface dataset:
Safe Region Multi-Agent Formation Control With Velocity Tracking
Venue: arXiv cs.MA 2023
Year: 2023

This paper provides a solution to the problem of safe region formation control with reference velocity tracking for a second-order multi-agent system without velocity measurements. Safe region formation control is a control problem where the agents are expected to attain the desired formation while reaching the target region and simultaneously ensuring collision and obstacle avoidance. To tackle this control problem, we break it down into two distinct objectives: safety and region formation control, to provide a completely distributed algorithm. Region formation control is modeled as a high-level abstract objective, whereas safety and actuator saturation are modeled as a low-level objective designed independently, without any knowledge of the former, and being minimally invasive. Our approach incorporates connectivity preservation, actuator saturation, safety considerations, and lack of velocity measurement from other agents with second-order system dynamics which are important constraints in practical applications. Both internal safety for collision avoidance among agents and external safety for avoiding unsafe regions are ensured using exponential control barrier functions. We provide theoretical results for asymptotic convergence and numerical simulation to show the approach's effectiveness.
NTT-PIM: Row-Centric Architecture and Mapping for Efficient Number-Theoretic Transform on PIM
Venue: arXiv cs.AR 2023
Year: 2023

Recently DRAM-based PIMs (processing-in-memories) with unmodified cell arrays have demonstrated impressive performance for accelerating AI applications. However, due to the very restrictive hardware constraints, PIM remains an accelerator for simple functions only. In this paper we propose NTT-PIM, which is based on the same principles such as no modification of cell arrays and very restrictive area budget, but shows state-of-the-art performance for a very complex application such as NTT, thanks to features optimized for the application's characteristics, such as in-place update and pipelining via multiple buffers. Our experimental results demonstrate that our NTT-PIM can outperform previous best PIM-based NTT accelerators in terms of runtime by 1.7 ~ 17 times while having negligible area and power overhead.
FuseSR: Super Resolution for Real-time Rendering through Efficient Multi-resolution Fusion
Venue: arXiv cs.GR 2023
Year: 2023

The workload of real-time rendering is steeply increasing as the demand for high resolution, high refresh rates, and high realism rises, overwhelming most graphics cards. To mitigate this problem, one of the most popular solutions is to render images at a low resolution to reduce rendering overhead, and then manage to accurately upsample the low-resolution rendered image to the target resolution, a.k.a. super-resolution techniques. Most existing methods focus on exploiting information from low-resolution inputs, such as historical frames. The absence of high frequency details in those LR inputs makes them hard to recover fine details in their high-resolution predictions. In this paper, we propose an efficient and effective super-resolution method that predicts high-quality upsampled reconstructions utilizing low-cost high-resolution auxiliary G-Buffers as additional input. With LR images and HR G-buffers as input, the network requires to align and fuse features at multi resolution levels. We introduce an efficient and effective H-Net architecture to solve this problem and significantly reduce rendering overhead without noticeable quality deterioration. Experiments show that our method is able to produce temporally consistent reconstructions in $4 \times 4$ and even challenging $8 \times 8$ upsampling cases at 4K resolution with real-time performance, with substantially improved quality and significant performance boost compared to existing works.
SVM based Multiclass Classifier for Gait phase Classification using Shank IMU Sensor
Venue: arXiv cs.LG 2023
Year: 2023

In this study, a gait phase classification method based on SVM multiclass classification is introduced, with a focus on the precise identification of the stance and swing phases, which are further subdivided into seven phases. Data from individual IMU sensors, such as Shank Acceleration X, Y, Z, Shank Gyro X, and Knee Angles, are used as features in this classification model. The suggested technique successfully classifies the various gait phases with a significant accuracy of about 90.3%. Gait phase classification is crucial, especially in the domains of exoskeletons and prosthetics, where accurate identification of gait phases enables seamless integration with assistive equipment, improving mobility, stability, and energy economy. This study extends the study of gait and offers an effective method for correctly identifying gait phases from Shank IMU sensor data, with potential applications in biomechanical research, exoskeletons, rehabilitation, and prosthetics.
Evaluating Intelligent Algorithms for Gait Phase Classification in Lower Limb Robotic Systems
Venue: arXiv cs.RO 2023
Year: 2023

Accurate and rapid detection of gait phases is of utmost importance in achieving optimal performance of powered lower-limb prostheses and exoskeletons. With the increasing versatility and complexity of these robotic systems, there is a growing need to enhance the performance of gait detection algorithms. The development of reliable and functional gait detection algorithms holds the potential to enhance precision, stability, and safety in prosthetic devices and other rehabilitation technologies. In this systematic review, we delve into the extensive body of research and development in the domain of gait event detection methods, with a specific focus on their application to prosthetic devices. Our review critically assesses various proposed methods, aiming to identify the most effective approaches for gait phase classification in lower limb robotic systems. Through a comprehensive comparative analysis, we highlight the strengths and weaknesses of different algorithms, shedding light on their performance characteristics, applicability, and potential for further improvements. This comprehensive review was conducted by screening two databases, namely IEEE and Scopus. The search was limited to 204 papers published from 2010 to 2023. A total of 6 papers that focused on Heuristic, Thresholding, and Amplitude Zero Crossing involved techniques were identified and included in the review. 33.3% of implemented Algorithms used kinematic parameters such as joint angles, joint linear and angular velocity, and joint angular acceleration. This study purely focuses on threshold-based algorithms and thus paper focusing on other gait phase detection methods were excluded.
Automatic Bill of Materials
Venue: arXiv cs.CR 2023
Year: 2023

Ensuring the security of software supply chains requires reliable identification of upstream dependencies. We present the Automatic Bill of Materials, or ABOM, a technique for embedding dependency metadata in binaries at compile time. Rather than relying on developers to explicitly enumerate dependency names and versions, ABOM embeds a hash of each distinct input source code file into the binary emitted by a compiler. Hashes are stored in Compressed Bloom Filters, highly space-efficient probabilistic data structures, which enable querying for the presence of dependencies without the possibility of false negatives. If leveraged across the ecosystem, ABOMs provide a zero-touch, backwards-compatible, drop-in solution for fast supply chain attack detection in real-world, language-independent software.
Decoding Modular Reconfigurable Robots: A Survey on Mechanisms and Design
Venue: arXiv cs.RO 2023
Year: 2023

The intrinsic modularity and reconfigurability of modular reconfigurable robots (MRR) confer advantages such as versatility, fault tolerance, and economic efficacy, thereby showcasing considerable potential across diverse applications. The continuous evolution of the technology landscape and the emergence of diverse conceptual designs have generated multiple MRR categories, each described by its respective morphology or capability characteristics, leading to some ambiguity in the taxonomy. This paper conducts a comprehensive survey encompassing the entirety of MRR hardware and design, spanning from the inception in 1985 to 2023. This paper introduces an innovative, unified conceptual framework for understanding MRR hardware, which encompasses three pivotal elements: connectors, actuators, and homogeneity. Through the utilization of this trilateral framework, this paper provide an intuitive understanding of the diverse spectrum of MRR hardware iterations while systematically deciphering and classifying the entire range, offering a more structured perspective. This survey elucidates the fundamental attributes characterizing MRRs and their compositional aspects, providinig insights into their design, technology, functionality, and categorization. Augmented by the proposed trilateral framework, this paper also elaborates on the trajectory of evolution, prevailing trends, principal challenges, and potential prospects within the field of MRRs.
Cell-Free Massive MIMO Surveillance Systems
Venue: arXiv cs.IT 2023
Year: 2023

Wireless surveillance, in which untrusted communications links are proactively monitored by legitimate agencies, has started to garner a lot of interest for enhancing the national security. In this paper, we propose a new cell-free massive multiple-input multiple-output (CF-mMIMO) wireless surveillance system, where a large number of distributed multi-antenna aided legitimate monitoring nodes (MNs) embark on either observing or jamming untrusted communication links. To facilitate concurrent observing and jamming, a subset of the MNs is selected for monitoring the untrusted transmitters (UTs), while the remaining MNs are selected for jamming the untrusted receivers (URs). We analyze the performance of CF-mMIMO wireless surveillance and derive a closed-form expression for the monitoring success probability of MNs. We then propose a greedy algorithm for the observing vs, jamming mode assignment of MNs, followed by the conception of a jamming transmit power allocation algorithm for maximizing the minimum monitoring success probability concerning all the UT and UR pairs based on the associated long-term channel state information knowledge. In conclusion, our proposed CF-mMIMO system is capable of significantly improving the performance of the MNs compared to that of the state-of-the-art baseline. In scenarios of a mediocre number of MNs, our proposed scheme provides an 11-fold improvement in the minimum monitoring success probability compared to its co-located mMIMO benchmarker.
Parking Problem by Oblivious Mobile Robots in Infinite Grids
Venue: arXiv cs.DC 2023
Year: 2023

In this paper, the parking problem of a swarm of mobile robots has been studied. The robots are deployed at the nodes of an infinite grid, which has a subset of prefixed nodes marked as parking nodes. Each parking node p_i has a capacity of k_i which is given as input and equals the maximum number of robots a parking node can accommodate. As a solution to the parking problem, robots need to partition themselves into groups so that each parking node contains a number of robots that are equal to the capacity of the node in the final configuration. It is assumed that the number of robots in the initial configuration represents the sum of the capacities of the parking nodes. The robots are assumed to be autonomous, anonymous, homogeneous, identical and oblivious. They operate under an asynchronous scheduler. They neither have any agreement on the coordinate axes nor do they agree on a common chirality. All the initial configurations for which the problem is unsolvable have been identified. A deterministic distributed algorithm has been proposed for the remaining configurations, ensuring the solvability of the problem.
Bounding and Filling: A Fast and Flexible Framework for Image Captioning
Venue: arXiv cs.CV 2023
Year: 2023

Most image captioning models following an autoregressive manner suffer from significant inference latency. Several models adopted a non-autoregressive manner to speed up the process. However, the vanilla non-autoregressive manner results in subpar performance, since it generates all words simultaneously, which fails to capture the relationships between words in a description. The semi-autoregressive manner employs a partially parallel method to preserve performance, but it sacrifices inference speed. In this paper, we introduce a fast and flexible framework for image captioning called BoFiCap based on bounding and filling techniques. The BoFiCap model leverages the inherent characteristics of image captioning tasks to pre-define bounding boxes for image regions and their relationships. Subsequently, the BoFiCap model fills corresponding words in each box using two-generation manners. Leveraging the box hints, our filling process allows each word to better perceive other words. Additionally, our model offers flexible image description generation: 1) by employing different generation manners based on speed or performance requirements, 2) producing varied sentences based on user-specified boxes. Experimental evaluations on the MS-COCO benchmark dataset demonstrate that our framework in a non-autoregressive manner achieves the state-of-the-art on task-specific metric CIDEr (125.6) while speeding up 9.22x than the baseline model with an autoregressive manner; in a semi-autoregressive manner, our method reaches 128.4 on CIDEr while a 3.69x speedup. Our code and data is available at https://github.com/ChangxinWang/BoFiCap.
Score-Based Methods for Discrete Optimization in Deep Learning
Venue: arXiv cs.LG 2023
Year: 2023

Discrete optimization problems often arise in deep learning tasks, despite the fact that neural networks typically operate on continuous data. One class of these problems involve objective functions which depend on neural networks, but optimization variables which are discrete. Although the discrete optimization literature provides efficient algorithms, they are still impractical in these settings due to the high cost of an objective function evaluation, which involves a neural network forward-pass. In particular, they require $O(n)$ complexity per iteration, but real data such as point clouds have values of $n$ in thousands or more. In this paper, we investigate a score-based approximation framework to solve such problems. This framework uses a score function as a proxy for the marginal gain of the objective, leveraging embeddings of the discrete variables and speed of auto-differentiation frameworks to compute backward-passes in parallel. We experimentally demonstrate, in adversarial set classification tasks, that our method achieves a superior trade-off in terms of speed and solution quality compared to heuristic methods.
Reformulating NLP tasks to Capture Longitudinal Manifestation of Language Disorders in People with Dementia
Venue: arXiv cs.CL 2023
Year: 2023

Dementia is associated with language disorders which impede communication. Here, we automatically learn linguistic disorder patterns by making use of a moderately-sized pre-trained language model and forcing it to focus on reformulated natural language processing (NLP) tasks and associated linguistic patterns. Our experiments show that NLP tasks that encapsulate contextual information and enhance the gradient signal with linguistic patterns benefit performance. We then use the probability estimates from the best model to construct digital linguistic markers measuring the overall quality in communication and the intensity of a variety of language disorders. We investigate how the digital markers characterize dementia speech from a longitudinal perspective. We find that our proposed communication marker is able to robustly and reliably characterize the language of people with dementia, outperforming existing linguistic approaches; and shows external validity via significant correlation with clinical markers of behaviour. Finally, our proposed linguistic disorder markers provide useful insights into gradual language impairment associated with disease progression.
Estimating Uncertainty in Multimodal Foundation Models using Public Internet Data
Venue: arXiv cs.AI 2023
Year: 2023

Foundation models are trained on vast amounts of data at scale using self-supervised learning, enabling adaptation to a wide range of downstream tasks. At test time, these models exhibit zero-shot capabilities through which they can classify previously unseen (user-specified) categories. In this paper, we address the problem of quantifying uncertainty in these zero-shot predictions. We propose a heuristic approach for uncertainty estimation in zero-shot settings using conformal prediction with web data. Given a set of classes at test time, we conduct zero-shot classification with CLIP-style models using a prompt template, e.g., "an image of a <category>", and use the same template as a search query to source calibration data from the open web. Given a web-based calibration set, we apply conformal prediction with a novel conformity score that accounts for potential errors in retrieved web data. We evaluate the utility of our proposed method in Biomedical foundation models; our preliminary results show that web-based conformal prediction sets achieve the target coverage with satisfactory efficiency on a variety of biomedical datasets.
Seeking Next Layer Neurons' Attention for Error-Backpropagation-Like Training in a Multi-Agent Network Framework
Venue: arXiv cs.NE 2023
Year: 2023

Despite considerable theoretical progress in the training of neural networks viewed as a multi-agent system of neurons, particularly concerning biological plausibility and decentralized training, their applicability to real-world problems remains limited due to scalability issues. In contrast, error-backpropagation has demonstrated its effectiveness for training deep networks in practice. In this study, we propose a local objective for neurons that, when pursued by neurons individually, align them to exhibit similarities to error-backpropagation in terms of efficiency and scalability during training. For this purpose, we examine a neural network comprising decentralized, self-interested neurons seeking to maximize their local objective -- attention from subsequent layer neurons -- and identify the optimal strategy for neurons. We also analyze the relationship between this strategy and backpropagation, establishing conditions under which the derived strategy is equivalent to error-backpropagation. Lastly, we demonstrate the learning capacity of these multi-agent neural networks through experiments on three datasets and showcase their superior performance relative to error-backpropagation in a catastrophic forgetting benchmark.
Tabletop Transparent Scene Reconstruction via Epipolar-Guided Optical Flow with Monocular Depth Completion Prior
Venue: arXiv cs.RO 2023
Year: 2023

Reconstructing transparent objects using affordable RGB-D cameras is a persistent challenge in robotic perception due to inconsistent appearances across views in the RGB domain and inaccurate depth readings in each single-view. We introduce a two-stage pipeline for reconstructing transparent objects tailored for mobile platforms. In the first stage, off-the-shelf monocular object segmentation and depth completion networks are leveraged to predict the depth of transparent objects, furnishing single-view shape prior. Subsequently, we propose Epipolar-guided Optical Flow (EOF) to fuse several single-view shape priors from the first stage to a cross-view consistent 3D reconstruction given camera poses estimated from opaque part of the scene. Our key innovation lies in EOF which employs boundary-sensitive sampling and epipolar-line constraints into optical flow to accurately establish 2D correspondences across multiple views on transparent objects. Quantitative evaluations demonstrate that our pipeline significantly outperforms baseline methods in 3D reconstruction quality, paving the way for more adept robotic perception and interaction with transparent objects.
The Reality of the Situation: A Survey of Situated Analytics
Venue: arXiv cs.HC 2023
Year: 2023

The advent of low cost, accessible, and high performance augmented reality (AR) has shed light on a situated form of analytics where in-situ visualizations embedded in the real world can facilitate sensemaking based on the user's physical location. In this work, we identify prior literature in this emerging field with a focus on situated analytics. After collecting 47 relevant situated analytics systems, we classify them using a taxonomy of three dimensions: situating triggers, view situatedness, and data depiction. We then identify four archetypical patterns in our classification using an ensemble cluster analysis. We also assess the level which these systems support the sensemaking process. Finally, we discuss insights and design guidelines that we learned from our analysis.
An Empirical Study of Super-resolution on Low-resolution Micro-expression Recognition
Venue: arXiv cs.CV 2023
Year: 2023

Micro-expression recognition (MER) in low-resolution (LR) scenarios presents an important and complex challenge, particularly for practical applications such as group MER in crowded environments. Despite considerable advancements in super-resolution techniques for enhancing the quality of LR images and videos, few study has focused on investigate super-resolution for improving LR MER. The scarcity of investigation can be attributed to the inherent difficulty in capturing the subtle motions of micro-expressions, even in original-resolution MER samples, which becomes even more challenging in LR samples due to the loss of distinctive features. Furthermore, a lack of systematic benchmarking and thorough analysis of super-resolution-assisted MER methods has been noted. This paper tackles these issues by conducting a series of benchmark experiments that integrate both super-resolution (SR) and MER methods, guided by an in-depth literature survey. Specifically, we employ seven cutting-edge state-of-the-art (SOTA) MER techniques and evaluate their performance on samples generated from 13 SOTA SR techniques, thereby addressing the problem of super-resolution in MER. Through our empirical study, we uncover the primary challenges associated with SR-assisted MER and identify avenues to tackle these challenges by leveraging recent advancements in both SR and MER methodologies. Our analysis provides insights for progressing toward more efficient SR-assisted MER.