OpenRaiser/Intern-Atlas huggingface dataset:
Assessing Dataset Quality Through Decision Tree Characteristics in Autoencoder-Processed Spaces
Venue: arXiv cs.LG 2023
Year: 2023

In this paper, we delve into the critical aspect of dataset quality assessment in machine learning classification tasks. Leveraging a variety of nine distinct datasets, each crafted for classification tasks with varying complexity levels, we illustrate the profound impact of dataset quality on model training and performance. We further introduce two additional datasets designed to represent specific data conditions - one maximizing entropy and the other demonstrating high redundancy. Our findings underscore the importance of appropriate feature selection, adequate data volume, and data quality in achieving high-performing machine learning models. To aid researchers and practitioners, we propose a comprehensive framework for dataset quality assessment, which can help evaluate if the dataset at hand is sufficient and of the required quality for specific tasks. This research offers valuable insights into data assessment practices, contributing to the development of more accurate and robust machine learning models.
Freestyle 3D-Aware Portrait Synthesis Based on Compositional Generative Priors
Venue: arXiv cs.CV 2023
Year: 2023

Efficiently generating a freestyle 3D portrait with high quality and 3D-consistency is a promising yet challenging task. The portrait styles generated by most existing methods are usually restricted by their 3D generators, which are learned in specific facial datasets, such as FFHQ. To get the diverse 3D portraits, one can build a large-scale multi-style database to retrain a 3D-aware generator, or use a off-the-shelf tool to do the style translation. However, the former is time-consuming due to data collection and training process, the latter may destroy the multi-view consistency. To tackle this problem, we propose a novel text-driven 3D-aware portrait synthesis framework that can generate out-of-distribution portrait styles. Specifically, for a given portrait style prompt, we first composite two generative priors, a 3D-aware GAN generator and a text-guided image editor, to quickly construct a few-shot stylized portrait set. Then we map the special style domain of this set to our proposed 3D latent feature generator and obtain a 3D representation containing the given style information. Finally we use a pre-trained 3D renderer to generate view-consistent stylized portraits from the 3D representation. Extensive experimental results show that our method is capable of synthesizing high-quality 3D portraits with specified styles in a few minutes, outperforming the state-of-the-art.
Efficiently Using Polar Codes in 5G Base Stations to Enhance Rural Connectivity
Venue: arXiv cs.NI 2023
Year: 2023

5G connectivity has become essential to integrate rural communities into the broader digital economy and support critical applications like remote education and remote surgery. A major hindrance to expanding rural broadband coverage, especially in developing countries, is the high cost of installing 5G base stations. Hence, there is a need to reduce the cost of a 5G base station without degrading its performance. Our work proposes a novel approach to efficiently utilize the polar code encoders in a 5G base station. The idea is to use the idle time of the polar encoders during downlink transmission for error correction in the 5G data plane. Polar codes have conventionally been used in the 5G control plane, while LDPC codes are used in the data plane. We perform detailed characterization experiments to show the advantages of using polar codes in the data plane as well. Further, to intelligently distribute the user data packets among the available compute nodes, we propose a set of novel resource allocation algorithms and compare their performance with other algorithms in the literature. Using our proposed optimization techniques, we achieve a 17% reduction in the cost of a 5G base station. Simultaneously, we are able to improve the performance by 24% compared to a conventional base station.
Identifying Practical Challenges in the Implementation of Technical Measures for Data Privacy Compliance
Venue: arXiv cs.CR 2023
Year: 2023

Modern privacy regulations provide a strict mandate for data processing entities to implement appropriate technical measures to demonstrate compliance. In practice, determining what measures are indeed "appropriate" is not trivial, particularly in light of vague guidelines provided by privacy regulations. To exacerbate the issue, challenges arise not only in the implementation of the technical measures themselves, but also in a variety of factors involving the roles, processes, decisions, and culture surrounding the pursuit of privacy compliance. In this paper, we present 33 challenges faced in the implementation of technical measures for privacy compliance, derived from a qualitative analysis of 16 interviews with privacy professionals. In addition, we evaluate the interview findings in a survey study, which gives way to a discussion of the identified challenges and their implications.
An information theoretic necessary condition for perfect reconstruction
Venue: arXiv cs.IT 2023
Year: 2023

A new information theoretic condition is presented for reconstructing a discrete random variable $X$ based on the knowledge of a set of discrete functions of $X$. The reconstruction condition is derived from Shannon's 1953 lattice theory with two entropic metrics of Shannon and Rajski. Because such a theoretical material is relatively unknown and appears quite dispersed in different references, we first provide a synthetic description (with complete proofs) of its concepts, such as total, common and complementary informations. Definitions and properties of the two entropic metrics are also fully detailed and shown compatible with the lattice structure. A new geometric interpretation of such a lattice structure is then investigated that leads to a necessary (and sometimes sufficient) condition for reconstructing the discrete random variable $X$ given a set $\{ X_1,\ldots,X_{n} \}$ of elements in the lattice generated by $X$. Finally, this condition is illustrated in five specific examples of perfect reconstruction problems: reconstruction of a symmetric random variable from the knowledge of its sign and absolute value, reconstruction of a word from a set of linear combinations, reconstruction of an integer from its prime signature (fundamental theorem of arithmetic) and from its remainders modulo a set of coprime integers (Chinese remainder theorem), and reconstruction of the sorting permutation of a list from a minimal set of pairwise comparisons.
Geometric Ultrasound Localization Microscopy
Venue: arXiv cs.CV 2023
Year: 2023

Contrast-Enhanced Ultra-Sound (CEUS) has become a viable method for non-invasive, dynamic visualization in medical diagnostics, yet Ultrasound Localization Microscopy (ULM) has enabled a revolutionary breakthrough by offering ten times higher resolution. To date, Delay-And-Sum (DAS) beamformers are used to render ULM frames, ultimately determining the image resolution capability. To take full advantage of ULM, this study questions whether beamforming is the most effective processing step for ULM, suggesting an alternative approach that relies solely on Time-Difference-of-Arrival (TDoA) information. To this end, a novel geometric framework for micro bubble localization via ellipse intersections is proposed to overcome existing beamforming limitations. We present a benchmark comparison based on a public dataset for which our geometric ULM outperforms existing baseline methods in terms of accuracy and robustness while only utilizing a portion of the available transducer data.
Challenges and Opportunities for RISC-V Architectures towards Genomics-based Workloads
Venue: arXiv cs.DC 2023
Year: 2023

The use of large-scale supercomputing architectures is a hard requirement for scientific computing Big-Data applications. An example is genomics analytics, where millions of data transformations and tests per patient need to be done to find relevant clinical indicators. Therefore, to ensure open and broad access to high-performance technologies, governments, and academia are pushing toward the introduction of novel computing architectures in large-scale scientific environments. This is the case of RISC-V, an open-source and royalty-free instruction-set architecture. To evaluate such technologies, here we present the Variant-Interaction Analytics use case benchmarking suite and datasets. Through this use case, we search for possible genetic interactions using computational and statistical methods, providing a representative case for heavy ETL (Extract, Transform, Load) data processing. Current implementations are implemented in x86-based supercomputers (e.g. MareNostrum-IV at the Barcelona Supercomputing Center (BSC)), and future steps propose RISC-V as part of the next MareNostrum generations. Here we describe the Variant Interaction Use Case, highlighting the characteristics leveraging high-performance computing, indicating the caveats and challenges towards the next RISC-V developments and designs to come from a first comparison between x86 and RISC-V architectures on real Variant Interaction executions over real hardware implementations.
MTFS: a Moving Target Defense-Enabled File System for Malware Mitigation
Venue: arXiv cs.CR 2023
Year: 2023

Ransomware has remained one of the most notorious threats in the cybersecurity field. Moving Target Defense (MTD) has been proposed as a novel paradigm for proactive defense. Although various approaches leverage MTD, few of them rely on the operating system and, specifically, the file system, thereby making them dependent on other computing devices. Furthermore, existing ransomware defense techniques merely replicate or detect attacks, without preventing them. Thus, this paper introduces the MTFS overlay file system and the design and implementation of three novel MTD techniques implemented on top of it. One delaying attackers, one trapping recursive directory traversal, and another one hiding file types. The effectiveness of the techniques are shown in two experiments. First, it is shown that the techniques can delay and mitigate ransomware on real IoT devices. Secondly, in a broader scope, the solution was confronted with 14 ransomware samples, highlighting that it can save 97% of the files.
Learning to Sail Dynamic Networks: The MARLIN Reinforcement Learning Framework for Congestion Control in Tactical Environments
Venue: arXiv cs.LG 2023
Year: 2023

Conventional Congestion Control (CC) algorithms,such as TCP Cubic, struggle in tactical environments as they misinterpret packet loss and fluctuating network performance as congestion symptoms. Recent efforts, including our own MARLIN, have explored the use of Reinforcement Learning (RL) for CC, but they often fall short of generalization, particularly in competitive, unstable, and unforeseen scenarios. To address these challenges, this paper proposes an RL framework that leverages an accurate and parallelizable emulation environment to reenact the conditions of a tactical network. We also introduce refined RL formulation and performance evaluation methods tailored for agents operating in such intricate scenarios. We evaluate our RL learning framework by training a MARLIN agent in conditions replicating a bottleneck link transition between a Satellite Communication (SATCOM) and an UHF Wide Band (UHF) radio link. Finally, we compared its performance in file transfer tasks against Transmission Control Protocol (TCP) Cubic and the default strategy implemented in the Mockets tactical communication middleware. The results demonstrate that the MARLIN RL agent outperforms both TCP and Mockets under different perspectives and highlight the effectiveness of specialized RL solutions in optimizing CC for tactical network environments.
Symphonize 3D Semantic Scene Completion with Contextual Instance Queries
Venue: arXiv cs.CV 2023
Year: 2023

`3D Semantic Scene Completion (SSC) has emerged as a nascent and pivotal undertaking in autonomous driving, aiming to predict voxel occupancy within volumetric scenes. However, prevailing methodologies primarily focus on voxel-wise feature aggregation, while neglecting instance semantics and scene context. In this paper, we present a novel paradigm termed Symphonies (Scene-from-Insts), that delves into the integration of instance queries to orchestrate 2D-to-3D reconstruction and 3D scene modeling. Leveraging our proposed Serial Instance-Propagated Attentions, Symphonies dynamically encodes instance-centric semantics, facilitating intricate interactions between image-based and volumetric domains. Simultaneously, Symphonies enables holistic scene comprehension by capturing context through the efficient fusion of instance queries, alleviating geometric ambiguity such as occlusion and perspective errors through contextual scene reasoning. Experimental results demonstrate that Symphonies achieves state-of-the-art performance on challenging benchmarks SemanticKITTI and SSCBench-KITTI-360, yielding remarkable mIoU scores of 15.04 and 18.58, respectively. These results showcase the paradigm's promising advancements. The code is available at https://github.com/hustvl/Symphonies.
Understanding (Ir)rational Herding Online
Venue: arXiv cs.SI 2023
Year: 2023

Investigations of social influence in collective decision-making have become possible due to recent technologies and platforms that record interactions in far larger groups than could be studied before. Herding and its impact on decision-making are critical areas of practical interest and research study. However, despite theoretical work suggesting that it matters whether individuals choose who to imitate based on cues such as experience or whether they herd at random, there is little empirical analysis of this distinction. To demonstrate the distinction between what the literature calls "rational" and "irrational" herding, we use data on tens of thousands of loans from a well-established online peer-to-peer (p2p) lending platform. First, we employ an empirical measure of memory in complex systems to measure herding in lending. Then, we illustrate a network-based approach to visualize herding. Finally, we model the impact of herding on collective outcomes. Our study reveals that loan performance is not solely determined by whether the lenders engage in herding or not. Instead, the interplay between herding and the imitated lenders' prior success on the platform predicts loan outcomes. In short, herds led by expert lenders tend to pick loans that do not default. We discuss the implications of this under-explored aspect of herding for platform designers, borrowers, and lenders. Our study advances collective intelligence theories based on a case of high-stakes group decision-making online.
Joint Learning of Network Topology and Opinion Dynamics Based on Bandit Algorithms
Venue: arXiv cs.SI 2023
Year: 2023

We study joint learning of network topology and a mixed opinion dynamics, in which agents may have different update rules. Such a model captures the diversity of real individual interactions. We propose a learning algorithm based on multi-armed bandit algorithms to address the problem. The goal of the algorithm is to find each agent's update rule from several candidate rules and to learn the underlying network. At each iteration, the algorithm assumes that each agent has one of the updated rules and then modifies network estimates to reduce validation error. Numerical experiments show that the proposed algorithm improves initial estimates of the network and update rules, decreases prediction error, and performs better than other methods such as sparse linear regression and Gaussian process regression.
Adversarial Backdoor Attack by Naturalistic Data Poisoning on Trajectory Prediction in Autonomous Driving
Venue: arXiv cs.CV 2023
Year: 2023

In autonomous driving, behavior prediction is fundamental for safe motion planning, hence the security and robustness of prediction models against adversarial attacks are of paramount importance. We propose a novel adversarial backdoor attack against trajectory prediction models as a means of studying their potential vulnerabilities. Our attack affects the victim at training time via naturalistic, hence stealthy, poisoned samples crafted using a novel two-step approach. First, the triggers are crafted by perturbing the trajectory of attacking vehicle and then disguised by transforming the scene using a bi-level optimization technique. The proposed attack does not depend on a particular model architecture and operates in a black-box manner, thus can be effective without any knowledge of the victim model. We conduct extensive empirical studies using state-of-the-art prediction models on two benchmark datasets using metrics customized for trajectory prediction. We show that the proposed attack is highly effective, as it can significantly hinder the performance of prediction models, unnoticeable by the victims, and efficient as it forces the victim to generate malicious behavior even under constrained conditions. Via ablative studies, we analyze the impact of different attack design choices followed by an evaluation of existing defence mechanisms against the proposed attack.
On the reconstruction of bandlimited signals from random samples quantized via noise-shaping
Venue: arXiv cs.IT 2023
Year: 2023

Noise-shaping quantization techniques are widely used for converting bandlimited signals from the analog to the digital domain. They work by "shaping" the quantization noise so that it falls close to the reconstruction operator's null space. We investigate the compatibility of two such schemes, specifically $ΣΔ$ quantization and distributed noise-shaping quantization, with random samples of bandlimited functions. Let $f$ be a real-valued $π$-bandlimited function. Suppose $R>1$ is a real number and assume that $\{x_i\}_{i=1}^m$ is a sequence of i.i.d random variables uniformly distributed on $[-\tilde{R},\tilde{R}]$, where $\tilde{R}>R$ is appropriately chosen. We show that by using a noise-shaping quantizer to quantize the values of $f$ at $\{x_i\}_{i=1}^m$, a function $f^{\sharp}$ can be reconstructed from these quantized values such that $\|f-f^{\sharp}\|_{L^2[-R, R]}$ decays with high probability as $m$ and $\tilde{R}$ increase. We emphasize that the sample points $\{x_i\}_{i=1}^m$ are completely random, i.e., they have no predefined structure, which makes our findings the first of their kind.
Predicting the Impact of Batch Refactoring Code Smells on Application Resource Consumption
Venue: arXiv cs.SE 2023
Year: 2023

Automated batch refactoring has become a de-facto mechanism to restructure software that may have significant design flaws negatively impacting the code quality and maintainability. Although automated batch refactoring techniques are known to significantly improve overall software quality and maintainability, their impact on resource utilization is not well studied. This paper aims to bridge the gap between batch refactoring code smells and consumption of resources. It determines the relationship between software code smell batch refactoring, and resource consumption. Next, it aims to design algorithms to predict the impact of code smell refactoring on resource consumption. This paper investigates 16 code smell types and their joint effect on resource utilization for 31 open source applications. It provides a detailed empirical analysis of the change in application CPU and memory utilization after refactoring specific code smells in isolation and in batches. This analysis is then used to train regression algorithms to predict the impact of batch refactoring on CPU and memory utilization before making any refactoring decisions. Experimental results also show that our ANN-based regression model provides highly accurate predictions for the impact of batch refactoring on resource consumption. It allows the software developers to intelligently decide which code smells they should refactor jointly to achieve high code quality and maintainability without increasing the application resource utilization. This paper responds to the important and urgent need of software engineers across a broad range of software applications, who are looking to refactor code smells and at the same time improve resource consumption. Finally, it brings forward the concept of resource aware code smell refactoring to the most crucial software applications.
Exploring Fully Offloaded GPU Stream-Aware Message Passing
Venue: arXiv cs.DC 2023
Year: 2023

Modern heterogeneous supercomputing systems are comprised of CPUs, GPUs, and high-speed network interconnects. Communication libraries supporting efficient data transfers involving memory buffers from the GPU memory typically require the CPU to orchestrate the data transfer operations. A new offload-friendly communication strategy, stream-triggered (ST) communication, was explored to allow offloading the synchronization and data movement operations from the CPU to the GPU. A Message Passing Interface (MPI) one-sided active target synchronization based implementation was used as an exemplar to illustrate the proposed strategy. A latency-sensitive nearest neighbor microbenchmark was used to explore the various performance aspects of the implementation. The offloaded implementation shows significant on-node performance advantages over standard MPI active RMA (36%) and point-to-point (61%) communication. The current multi-node improvement is less (23% faster than standard active RMA but 11% slower than point-to-point), but plans are in progress to purse further improvements.
"Is a picture of a bird a bird": Policy recommendations for dealing with ambiguity in machine vision models
Venue: arXiv cs.CY 2023
Year: 2023

Many questions that we ask about the world do not have a single clear answer, yet typical human annotation set-ups in machine learning assume there must be a single ground truth label for all examples in every task. The divergence between reality and practice is stark, especially in cases with inherent ambiguity and where the range of different subjective judgments is wide. Here, we examine the implications of subjective human judgments in the behavioral task of labeling images used to train machine vision models. We identify three primary sources of ambiguity arising from (i) depictions of labels in the images, (ii) raters' backgrounds, and (iii) the task definition. On the basis of the empirical results, we suggest best practices for handling label ambiguity in machine learning datasets.
Structured State Space Models for Multiple Instance Learning in Digital Pathology
Venue: arXiv cs.CV 2023
Year: 2023

Multiple instance learning is an ideal mode of analysis for histopathology data, where vast whole slide images are typically annotated with a single global label. In such cases, a whole slide image is modelled as a collection of tissue patches to be aggregated and classified. Common models for performing this classification include recurrent neural networks and transformers. Although powerful compression algorithms, such as deep pre-trained neural networks, are used to reduce the dimensionality of each patch, the sequences arising from whole slide images remain excessively long, routinely containing tens of thousands of patches. Structured state space models are an emerging alternative for sequence modelling, specifically designed for the efficient modelling of long sequences. These models invoke an optimal projection of an input sequence into memory units that compress the entire sequence. In this paper, we propose the use of state space models as a multiple instance learner to a variety of problems in digital pathology. Across experiments in metastasis detection, cancer subtyping, mutation classification, and multitask learning, we demonstrate the competitiveness of this new class of models with existing state of the art approaches. Our code is available at https://github.com/MICS-Lab/s4_digital_pathology.