OpenRaiser/Intern-Atlas huggingface dataset:
Enabling Real-time Neural Recovery for Cloud Gaming on Mobile Devices
Venue: arXiv cs.NI 2023
Year: 2023

Cloud gaming is a multi-billion dollar industry. A client in cloud gaming sends its movement to the game server on the Internet, which renders and transmits the resulting video back. In order to provide a good gaming experience, a latency below 80 ms is required. This means that video rendering, encoding, transmission, decoding, and display have to finish within that time frame, which is especially challenging to achieve due to server overload, network congestion, and losses. In this paper, we propose a new method for recovering lost or corrupted video frames in cloud gaming. Unlike traditional video frame recovery, our approach uses game states to significantly enhance recovery accuracy and utilizes partially decoded frames to recover lost portions. We develop a holistic system that consists of (i) efficiently extracting game states, (ii) modifying H.264 video decoder to generate a mask to indicate which portions of video frames need recovery, and (iii) designing a novel neural network to recover either complete or partial video frames. Our approach is extensively evaluated using iPhone 12 and laptop implementations, and we demonstrate the utility of game states in the game video recovery and the effectiveness of our overall design.
Benchmarking the Effectiveness of Classification Algorithms and SVM Kernels for Dry Beans
Venue: arXiv cs.LG 2023
Year: 2023

Plant breeders and agricultural researchers can increase crop productivity by identifying desirable features, disease resistance, and nutritional content by analysing the Dry Bean dataset. This study analyses and compares different Support Vector Machine (SVM) classification algorithms, namely linear, polynomial, and radial basis function (RBF), along with other popular classification algorithms. The analysis is performed on the Dry Bean Dataset, with PCA (Principal Component Analysis) conducted as a preprocessing step for dimensionality reduction. The primary evaluation metric used is accuracy, and the RBF SVM kernel algorithm achieves the highest Accuracy of 93.34%, Precision of 92.61%, Recall of 92.35% and F1 Score as 91.40%. Along with adept visualization and empirical analysis, this study offers valuable guidance by emphasizing the importance of considering different SVM algorithms for complex and non-linear structured datasets.
The SocialAI School: Insights from Developmental Psychology Towards Artificial Socio-Cultural Agents
Venue: arXiv cs.AI 2023
Year: 2023

Developmental psychologists have long-established the importance of socio-cognitive abilities in human intelligence. These abilities enable us to enter, participate and benefit from human culture. AI research on social interactive agents mostly concerns the emergence of culture in a multi-agent setting (often without a strong grounding in developmental psychology). We argue that AI research should be informed by psychology and study socio-cognitive abilities enabling to enter a culture too. We discuss the theories of Michael Tomasello and Jerome Bruner to introduce some of their concepts to AI and outline key concepts and socio-cognitive abilities. We present The SocialAI school - a tool including a customizable parameterized uite of procedurally generated environments, which simplifies conducting experiments regarding those concepts. We show examples of such experiments with RL agents and Large Language Models. The main motivation of this work is to engage the AI community around the problem of social intelligence informed by developmental psychology, and to provide a tool to simplify first steps in this direction. Refer to the project website for code and additional information: https://sites.google.com/view/socialai-school.
A structural study of Big Tech firm-switching of inventors in the post-recession era
Venue: arXiv cs.SI 2023
Year: 2023

Complex systems research and network science have recently been used to provide novel insights into economic phenomena such as patenting behavior and innovation in firms. Several studies have found that increased mobility of inventors, manifested through firm switching or transitioning, is associated with increased overall productivity. This paper proposes a novel structural study of such transitioning inventors, and the role they play in patent co-authorship networks, in a cohort of highly innovative and economically influential companies such as the five Big Tech firms (Apple, Microsoft, Google, Amazon and Meta) in the post-recession period (2010-2022). We formulate and empirically investigate three research questions using Big Tech patent data. Our results show that transitioning inventors tend to have higher degree centrality than the average Big Tech inventor, and that their removal can lead to greater network fragmentation than would be expected by chance. The rate of transition over the 12-year period of study was found to be highest between 2015-2017, suggesting that the Big Tech innovation ecosystem underwent non-trivial shifts during this time. Finally, transition was associated with higher estimated impact of co-authored patents post-transition.
Joint Beam Management and SLAM for mmWave Communication Systems
Venue: arXiv cs.IT 2023
Year: 2023

The millimeter-wave (mmWave) communication technology, which employs large-scale antenna arrays, enables inherent sensing capabilities. Simultaneous localization and mapping (SLAM) can utilize channel multipath angle estimates to realize integrated sensing and communication design in 6G communication systems. However, existing works have ignored the significant overhead required by the mmWave beam management when implementing SLAM with angle estimates. This study proposes a joint beam management and SLAM design that utilizes the strong coupling between the radio map and channel multipath for simultaneous beam management, localization, and mapping. In this approach, we first propose a hierarchical sweeping and sensing service design. The path angles are estimated in the hierarchical sweeping, enabling angle-based SLAM with the aid of an inertial measurement unit (IMU) to realize sensing service. Then, feature-aided tracking is proposed that utilizes prior angle information generated from the radio map and IMU. Finally, a switching module is introduced to enable flexible switching between hierarchical sweeping and feature-aided tracking. Simulations show that the proposed joint design can achieve sub-meter level localization and mapping accuracy (with an error < 0.5 m). Moreover, the beam management overhead can be reduced by approximately 40% in different wireless environments.
Generalizable and explainable prediction of potential miRNA-disease associations based on heterogeneous graph learning
Venue: arXiv cs.CE 2023
Year: 2023

Biomedical research has revealed the crucial role of miRNAs in the progression of many diseases, and computational prediction methods are increasingly proposed for assisting biological experiments to verify miRNA-disease associations (MDAs). However, the generalizability and explainability are currently underemphasized. It's significant to generalize effective predictions to entities with fewer or no existing MDAs and reveal how the prediction scores are derived. In this study, our work contributes to data, model, and result analysis. First, for better formulation of the MDA issue, we integrate multi-source data into a heterogeneous graph with a broader learning and prediction scope, and we split massive verified MDAs into independent training, validation, and test sets as a benchmark. Second, we construct an end-to-end data-driven model that performs node feature encoding, graph structure learning, and binary prediction sequentially, with a heterogeneous graph transformer as the central module. Finally, computational experiments illustrate that our method outperforms existing state-of-the-art methods, achieving better evaluation metrics and alleviating the neglect of unknown miRNAs and diseases effectively. Case studies further demonstrate that we can make reliable MDA detections on diseases without MDA records, and the predictions can be explained in general and case by case.
MargCTGAN: A "Marginally'' Better CTGAN for the Low Sample Regime
Venue: arXiv cs.LG 2023
Year: 2023

The potential of realistic and useful synthetic data is significant. However, current evaluation methods for synthetic tabular data generation predominantly focus on downstream task usefulness, often neglecting the importance of statistical properties. This oversight becomes particularly prominent in low sample scenarios, accompanied by a swift deterioration of these statistical measures. In this paper, we address this issue by conducting an evaluation of three state-of-the-art synthetic tabular data generators based on their marginal distribution, column-pair correlation, joint distribution and downstream task utility performance across high to low sample regimes. The popular CTGAN model shows strong utility, but underperforms in low sample settings in terms of utility. To overcome this limitation, we propose MargCTGAN that adds feature matching of de-correlated marginals, which results in a consistent improvement in downstream utility as well as statistical properties of the synthetic data.
LUCYD: A Feature-Driven Richardson-Lucy Deconvolution Network
Venue: arXiv cs.CV 2023
Year: 2023

The process of acquiring microscopic images in life sciences often results in image degradation and corruption, characterised by the presence of noise and blur, which poses significant challenges in accurately analysing and interpreting the obtained data. This paper proposes LUCYD, a novel method for the restoration of volumetric microscopy images that combines the Richardson-Lucy deconvolution formula and the fusion of deep features obtained by a fully convolutional network. By integrating the image formation process into a feature-driven restoration model, the proposed approach aims to enhance the quality of the restored images whilst reducing computational costs and maintaining a high degree of interpretability. Our results demonstrate that LUCYD outperforms the state-of-the-art methods in both synthetic and real microscopy images, achieving superior performance in terms of image quality and generalisability. We show that the model can handle various microscopy modalities and different imaging conditions by evaluating it on two different microscopy datasets, including volumetric widefield and light-sheet microscopy. Our experiments indicate that LUCYD can significantly improve resolution, contrast, and overall quality of microscopy images. Therefore, it can be a valuable tool for microscopy image restoration and can facilitate further research in various microscopy applications. We made the source code for the model accessible under https://github.com/ctom2/lucyd-deconvolution.
Revisiting Implicit Models: Sparsity Trade-offs Capability in Weight-tied Model for Vision Tasks
Venue: arXiv cs.LG 2023
Year: 2023

Implicit models such as Deep Equilibrium Models (DEQs) have garnered significant attention in the community for their ability to train infinite layer models with elegant solution-finding procedures and constant memory footprint. However, despite several attempts, these methods are heavily constrained by model inefficiency and optimization instability. Furthermore, fair benchmarking across relevant methods for vision tasks is missing. In this work, we revisit the line of implicit models and trace them back to the original weight-tied models. Surprisingly, we observe that weight-tied models are more effective, stable, as well as efficient on vision tasks, compared to the DEQ variants. Through the lens of these simple-yet-clean weight-tied models, we further study the fundamental limits in the model capacity of such models and propose the use of distinct sparse masks to improve the model capacity. Finally, for practitioners, we offer design guidelines regarding the depth, width, and sparsity selection for weight-tied models, and demonstrate the generalizability of our insights to other learning paradigms.
Breaking Down the Task: A Unit-Grained Hybrid Training Framework for Vision and Language Decision Making
Venue: arXiv cs.CV 2023
Year: 2023

Vision language decision making (VLDM) is a challenging multimodal task. The agent have to understand complex human instructions and complete compositional tasks involving environment navigation and object manipulation. However, the long action sequences involved in VLDM make the task difficult to learn. From an environment perspective, we find that task episodes can be divided into fine-grained \textit{units}, each containing a navigation phase and an interaction phase. Since the environment within a unit stays unchanged, we propose a novel hybrid-training framework that enables active exploration in the environment and reduces the exposure bias. Such framework leverages the unit-grained configurations and is model-agnostic. Specifically, we design a Unit-Transformer (UT) with an intrinsic recurrent state that maintains a unit-scale cross-modal memory. Through extensive experiments on the TEACH benchmark, we demonstrate that our proposed framework outperforms existing state-of-the-art methods in terms of all evaluation metrics. Overall, our work introduces a novel approach to tackling the VLDM task by breaking it down into smaller, manageable units and utilizing a hybrid-training framework. By doing so, we provide a more flexible and effective solution for multimodal decision making.
Analysing Gender Bias in Text-to-Image Models using Object Detection
Venue: arXiv cs.CV 2023
Year: 2023

This work presents a novel strategy to measure bias in text-to-image models. Using paired prompts that specify gender and vaguely reference an object (e.g. "a man/woman holding an item") we can examine whether certain objects are associated with a certain gender. In analysing results from Stable Diffusion, we observed that male prompts generated objects such as ties, knives, trucks, baseball bats, and bicycles more frequently. On the other hand, female prompts were more likely to generate objects such as handbags, umbrellas, bowls, bottles, and cups. We hope that the method outlined here will be a useful tool for examining bias in text-to-image models.
STAR-RIS Enhanced Joint Physical Layer Security and Covert Communications for Multi-antenna mmWave Systems
Venue: arXiv cs.IT 2023
Year: 2023

This paper investigates the utilization of simultaneously transmitting and reflecting RIS (STAR-RIS) in supporting joint physical layer security (PLS) and covert communications (CCs) in a multi-antenna millimeter wave (mmWave) system, where the base station (BS) communicates with both covert and security users while defeating eavesdropping by wardens with the help of a STAR-RIS. Specifically, analytical derivations are performed to obtain the closed-form expression of warden's minimum detection error probability (DEP). Furthermore, the asymptotic result of the minimum DEP and the lower bound of the secure rates are derived, considering the practical assumption that BS only knows the statistical channel state information (CSI) between STAR-RIS and the wardens. Subsequently, an optimization problem is formulated with the aim of maximizing the average sum of the covert rate and the minimum secure rate while ensuring the covert requirement and quality of service (QoS) for legal users by jointly optimizing the active and passive beamformers. Due to the strong coupling among variables, an iterative algorithm based on the alternating strategy and the semi-definite relaxation (SDR) method is proposed to solve the non-convex optimization problem. Simulation results indicate that the performance of the proposed STAR-RIS-assisted scheme greatly surpasses that of the conventional RIS scheme, which validates the superiority of STAR-RIS in simultaneously implementing PLS and CCs.
Development of a Blockchain-Based On-Demand Lightweight Commodity Delivery System
Venue: arXiv cs.DC 2023
Year: 2023

The COVID-19 pandemic has caused a surge in the use of online delivery services, which rely on user-generated content to promote collaborative consumption. Although Online Food Delivery (OFD) is a popular delivery system in Bangladesh, it has yet to ensure item authenticity, especially with the increasing demand for lightweight commodity delivery services across the country. The authenticity of products, involvement of multiple parties, and fair exchange are all challenging aspects of coast-to-coast services. Therefore, it is necessary for the three entities involved in the supply chain transaction - Seller, Carrier, and Buyer - to establish at least two peer-to-peer operations to ensure reliability and efficiency. To address these limitations and meet consumer expectations, the study proposes a framework for a nationwide on-demand marketplace for lightweight commodity items and a delivery system. Furthermore, transaction details are stored in a Blockchain to ensure the transparency and reliability of the proposed system.
Scaling Limits of Memristor-Based Routers for Asynchronous Neuromorphic Systems
Venue: arXiv cs.ET 2023
Year: 2023

Multi-core neuromorphic systems typically use on-chip routers to transmit spikes among cores. These routers require significant memory resources and consume a large part of the overall system's energy budget. A promising alternative approach to using standard CMOS and SRAM-based routers is to exploit the features of memristive crossbar arrays and use them as programmable switch-matrices that route spikes. However, the scaling of these crossbar arrays presents physical challenges, such as "IR drop" on the metal lines due to the parasitic resistance, and leakage current accumulation on multiple active memristors in their "off" state. While reliability challenges of this type have been extensively studied in synchronous systems for compute-in-memory matrix-vector multiplication (MVM) accelerators and storage class memory, little effort has been devoted so far to characterizing the scaling limits of memristor-based crossbar routers. Here, we study the challenges of memristive crossbar arrays, when used as routing channels to transmit spikes in asynchronous Spiking Neural Network (SNN) hardware. We validate our analytical findings with experimental results obtained from a 4K-ReRAM chip which demonstrates its functionality as a routing crossbar. We determine the functionality bounds on the routing due to the IR drop and leak problem, based on theoretical modeling, circuit simulations for a 22nm FDSOI technology, and experimental measurements. This work highlights the limitations of this approach and provides useful guidelines for engineering the memristor device properties in memristive crossbar routers for multi-core asynchronous neuromorphic systems.
Heterogeneous graphs model spatial relationships between biological entities for breast cancer diagnosis
Venue: arXiv cs.CV 2023
Year: 2023

The heterogeneity of breast cancer presents considerable challenges for its early detection, prognosis, and treatment selection. Convolutional neural networks often neglect the spatial relationships within histopathological images, which can limit their accuracy. Graph neural networks (GNNs) offer a promising solution by coding the spatial relationships within images. Prior studies have investigated the modeling of histopathological images as cell and tissue graphs, but they have not fully tapped into the potential of extracting interrelationships between these biological entities. In this paper, we present a novel approach using a heterogeneous GNN that captures the spatial and hierarchical relations between cell and tissue graphs to enhance the extraction of useful information from histopathological images. We also compare the performance of a cross-attention-based network and a transformer architecture for modeling the intricate relationships within tissue and cell graphs. Our model demonstrates superior efficiency in terms of parameter count and achieves higher accuracy compared to the transformer-based state-of-the-art approach on three publicly available breast cancer datasets -- BRIGHT, BreakHis, and BACH.
PrestigeBFT: Revolutionizing View Changes in BFT Consensus Algorithms with Reputation Mechanisms
Venue: arXiv cs.DC 2023
Year: 2023

This paper proposes PrestigeBFT, a novel leader-based BFT consensus algorithm that addresses the weaknesses of passive view-change protocols. Passive protocols blindly rotate leadership among servers on a predefined schedule, potentially selecting unavailable or slow servers as leaders. PrestigeBFT proposes an active view-change protocol using reputation mechanisms that calculate a server's potential correctness based on historic behavior. The active protocol enables servers to campaign for leadership by performing reputation-associated work. As such, up-to-date and correct servers with good reputations are more likely to be elected as leaders as they perform less work, whereas faulty servers with bad reputations are suppressed from becoming leaders by being required to perform more work. Under normal operation, PrestigeBFT achieves 5X higher throughput than the baseline that uses passive view-change protocols. In addition, PrestigeBFT remains unaffected under benign faults and experiences only a 24% drop in throughput under a variety of Byzantine faults, while the baseline throughput drops by 62% and 69%, respectively.
Reducing Trust in Automated Certificate Authorities via Proofs-of-Authentication
Venue: arXiv cs.CR 2023
Year: 2023

Automated certificate authorities (CAs) have expanded the reach of public key infrastructure on the web and for software signing. The certificates that these CAs issue attest to proof of control of some digital identity. Some of these automated CAs issue certificates in response to client authentication using OpenID Connect (OIDC, an extension of OAuth 2.0). This places these CAs in a position to impersonate any identity. Mitigations for this risk, like certificate transparency and signature thresholds, have emerged, but these mitigations only detect or raise the difficulty of compromise. Researchers have proposed alternatives to CAs in this setting, but many of these alternatives would require prohibitive changes to deployed authentication protocols. In this work, we propose a cryptographic technique for reducing trust in these automated CAs. When issuing a certificate, the CAs embed a proof of authentication from the subject of the certificate -- but without enabling replay attacks. We explain multiple methods for achieving this with tradeoffs between user privacy, performance, and changes to existing infrastructure. We implement a proof of concept for a method using Guillou-Quisquater signatures that works out-of-the-box with existing OIDC deployments for the open-source Sigstore CA, finding that minimal modifications are required.