OpenRaiser/Intern-Atlas huggingface dataset:
Performance Comparison of Python Translators for a Multi-threaded CPU-bound Application
Venue: arXiv cs.DC 2022
Year: 2022

Currently, Python is one of the most widely used languages in various application areas. However, it has limitations when it comes to optimizing and parallelizing applications due to the nature of its official CPython interpreter, especially for CPU-bound applications. To solve this problem, several alternative translators have emerged, each with a different approach and its own cost-performance ratio. Due to the absence of comparative studies, we have carried out a performance comparison of these translators using N-Body as a case study (a well-known problem with high computational demand). The results obtained show that CPython and PyPy presented poor performance due to their limitations when it comes to parallelizing algorithms; while Numba and Cython achieved significantly higher performance, proving to be viable options to speed up numerical algorithms.
Pinning Fault Mode Modeling for DWM Shifting
Venue: arXiv cs.ET 2022
Year: 2022

Extreme scaling for purposes of achieving higher density and lower energy continues to increase the probability of memory faults. For domain wall (DW) memories, misalignment faults arise when aligning domains with access points. A previously understudied type of shifting fault, a pinning fault may occur due to non-uniform pinning potential distribution caused by notches with fabrication imperfections. This non-uniformity can pin a wall during current-induced DW motion. This paper provides a model of geometric variations varying width, depth, and curvature variations of a notch, their impacts on the critical shift current, and a study of the resulting impact on fault rates of DW memory systems. An increase in the effective critical shift current due to 5% variation predicts a pinning fault rate on the order of $10^{-8}$ per shift, which results in a mean-time-to-failure of circa 2s for a DW memory system.
Spot the Difference: A Cooperative Object-Referring Game in Non-Perfectly Co-Observable Scene
Venue: arXiv cs.CV 2022
Year: 2022

Visual dialog has witnessed great progress after introducing various vision-oriented goals into the conversation, especially such as GuessWhich and GuessWhat, where the only image is visible by either and both of the questioner and the answerer, respectively. Researchers explore more on visual dialog tasks in such kind of single- or perfectly co-observable visual scene, while somewhat neglect the exploration on tasks of non perfectly co-observable visual scene, where the images accessed by two agents may not be exactly the same, often occurred in practice. Although building common ground in non-perfectly co-observable visual scene through conversation is significant for advanced dialog agents, the lack of such dialog task and corresponding large-scale dataset makes it impossible to carry out in-depth research. To break this limitation, we propose an object-referring game in non-perfectly co-observable visual scene, where the goal is to spot the difference between the similar visual scenes through conversing in natural language. The task addresses challenges of the dialog strategy in non-perfectly co-observable visual scene and the ability of categorizing objects. Correspondingly, we construct a large-scale multimodal dataset, named SpotDiff, which contains 87k Virtual Reality images and 97k dialogs generated by self-play. Finally, we give benchmark models for this task, and conduct extensive experiments to evaluate its performance as well as analyze its main challenges.
Are you aware of what you are watching? Role of machine heuristic in online content recommendations
Venue: arXiv cs.HC 2022
Year: 2022

Since recommender systems have been created and developed to automate the recommendation process, users can easily consume their desired video content on online platforms. In this line, several content recommendation algorithms are introduced and employed to allow users to encounter content of their interests, effectively. However, the recommendation systems sometimes regrettably recommend inappropriate content, including misinformation or fake news. To make it worse, people would unreservedly accept such content due to their cognitive heuristic, machine heuristic, which is the rule of thumb that machines are more accurate and trustworthy than humans. In this study, we designed and conducted a web-based experiment where the participants are invoked machine heuristic by experiencing the whole process of machine or human recommendation system. The results demonstrated that participants (N = 89) showed a more positive attitude toward a machine recommender than a human recommender, even the recommended videos contain inappropriate content. While participants who have a high level of trust in machines exhibited a negative attitude toward recommendations. Based on these results, we suggest that a phenomenon known as algorithm aversion might be simultaneously considered with machine heuristic in investigating human interaction with a machine.
Multi-Scale Context-Guided Lumbar Spine Disease Identification with Coarse-to-fine Localization and Classification
Venue: arXiv cs.CV 2022
Year: 2022

Accurate and efficient lumbar spine disease identification is crucial for clinical diagnosis. However, existing deep learning models with millions of parameters often fail to learn with only hundreds or dozens of medical images. These models also ignore the contextual relationship between adjacent objects, such as between vertebras and intervertebral discs. This work introduces a multi-scale context-guided network with coarse-to-fine localization and classification, named CCF-Net, for lumbar spine disease identification. Specifically, in learning, we divide the localization objective into two parallel tasks, coarse and fine, which are more straightforward and effectively reduce the number of parameters and computational cost. The experimental results show that the coarse-to-fine design presents the potential to achieve high performance with fewer parameters and data requirements. Moreover, the multi-scale context-guided module can significantly improve the performance by 6.45% and 5.51% with ResNet18 and ResNet50, respectively. Our code is available at https://github.com/czifan/CCFNet.pytorch.
WegFormer: Transformers for Weakly Supervised Semantic Segmentation
Venue: arXiv cs.CV 2022
Year: 2022

Although convolutional neural networks (CNNs) have achieved remarkable progress in weakly supervised semantic segmentation (WSSS), the effective receptive field of CNN is insufficient to capture global context information, leading to sub-optimal results. Inspired by the great success of Transformers in fundamental vision areas, this work for the first time introduces Transformer to build a simple and effective WSSS framework, termed WegFormer. Unlike existing CNN-based methods, WegFormer uses Vision Transformer (ViT) as a classifier to produce high-quality pseudo segmentation masks. To this end, we introduce three tailored components in our Transformer-based framework, which are (1) a Deep Taylor Decomposition (DTD) to generate attention maps, (2) a soft erasing module to smooth the attention maps, and (3) an efficient potential object mining (EPOM) to filter noisy activation in the background. Without any bells and whistles, WegFormer achieves state-of-the-art 70.5% mIoU on the PASCAL VOC dataset, significantly outperforming the previous best method. We hope WegFormer provides a new perspective to tap the potential of Transformer in weakly supervised semantic segmentation. Code will be released.
Instance-level loss based multiple-instance learning framework for acoustic scene classification
Venue: arXiv cs.SD 2022
Year: 2022

In the acoustic scene classification (ASC) task, an acoustic scene consists of diverse sounds and is inferred by identifying combinations of distinct attributes among them. This study aims to extract and cluster these attributes effectively using an improved multiple-instance learning (MIL) framework for ASC. MIL, known as a weakly supervised learning method, is a strategy for extracting an instance from a bundle of frames composing an input audio clip and inferring a scene corresponding to the input data using these unlabeled instances. However, many studies pointed out an underestimation problem of MIL. In this study, we develop a MIL framework more suitable for ASC systems by defining instance-level labels and loss to extract and cluster instances effectively. Furthermore, we design a fully separated convolutional module, which is a lightweight neural network comprising pointwise, frequency-sided depthwise, and temporal-sided depthwise convolutional filters. As a result, compared to vanilla MIL, the confidence and proportion of positive instances increase significantly, overcoming the underestimation problem and improving the classification accuracy up to 11%. The proposed system achieved a performance of 81.1% and 72.3% on the TAU urban acoustic scenes 2019 and 2020 mobile datasets with 139 K parameters, respectively. Especially, it achieves the highest performance among the systems having under the 1 M parameters on the TAU urban acoustic scenes 2019 dataset.
Semantics-Aware Source Coding in Status Update Systems
Venue: arXiv cs.IT 2022
Year: 2022

We consider a communication system in which the destination receives status updates from an information source that observes a physical process. The transmitter performs semantics-empowered filtering as a means to send only the most "important" samples to the receiver in a timely manner. As a first step, we explore a simple policy where the transmitter selects to encode only a fraction of the least frequent realizations of the observed random phenomenon, treating the remaining ones as not informative. For this timely source coding problem, we derive the optimal codeword lengths in the sense of maximizing a semantics-aware utility function and minimizing a quadratic average length cost. Our numerical results show the optimal number of updates to transmit for different arrival rates and encoding costs and corroborate that semantic filtering results in higher performance in terms of timely delivery of important updates.
Differentiable DAG Sampling
Venue: arXiv cs.LG 2022
Year: 2022

We propose a new differentiable probabilistic model over DAGs (DP-DAG). DP-DAG allows fast and differentiable DAG sampling suited to continuous optimization. To this end, DP-DAG samples a DAG by successively (1) sampling a linear ordering of the node and (2) sampling edges consistent with the sampled linear ordering. We further propose VI-DP-DAG, a new method for DAG learning from observational data which combines DP-DAG with variational inference. Hence,VI-DP-DAG approximates the posterior probability over DAG edges given the observed data. VI-DP-DAG is guaranteed to output a valid DAG at any time during training and does not require any complex augmented Lagrangian optimization scheme in contrast to existing differentiable DAG learning approaches. In our extensive experiments, we compare VI-DP-DAG to other differentiable DAG learning baselines on synthetic and real datasets. VI-DP-DAG significantly improves DAG structure and causal mechanism learning while training faster than competitors.
Lazy-MDPs: Towards Interpretable Reinforcement Learning by Learning When to Act
Venue: arXiv cs.LG 2022
Year: 2022

Traditionally, Reinforcement Learning (RL) aims at deciding how to act optimally for an artificial agent. We argue that deciding when to act is equally important. As humans, we drift from default, instinctive or memorized behaviors to focused, thought-out behaviors when required by the situation. To enhance RL agents with this aptitude, we propose to augment the standard Markov Decision Process and make a new mode of action available: being lazy, which defers decision-making to a default policy. In addition, we penalize non-lazy actions in order to encourage minimal effort and have agents focus on critical decisions only. We name the resulting formalism lazy-MDPs. We study the theoretical properties of lazy-MDPs, expressing value functions and characterizing optimal solutions. Then we empirically demonstrate that policies learned in lazy-MDPs generally come with a form of interpretability: by construction, they show us the states where the agent takes control over the default policy. We deem those states and corresponding actions important since they explain the difference in performance between the default and the new, lazy policy. With suboptimal policies as default (pretrained or random), we observe that agents are able to get competitive performance in Atari games while only taking control in a limited subset of states.
Learning to Generate Synthetic Training Data using Gradient Matching and Implicit Differentiation
Venue: arXiv cs.LG 2022
Year: 2022

Using huge training datasets can be costly and inconvenient. This article explores various data distillation techniques that can reduce the amount of data required to successfully train deep networks. Inspired by recent ideas, we suggest new data distillation techniques based on generative teaching networks, gradient matching, and the Implicit Function Theorem. Experiments with the MNIST image classification problem show that the new methods are computationally more efficient than previous ones and allow to increase the performance of models trained on distilled data.
Sparsification Lower Bound for Linear Spanners in Directed Graphs
Venue: arXiv cs.DM 2022
Year: 2022

For $α\ge 1$, $β\ge 0$, and a graph $G$, a spanning subgraph $H$ of $G$ is said to be an $(α, β)$-spanner if $\dist(u, v, H) \le α\cdot \dist(u, v, G) + β$ holds for any pair of vertices $u$ and $v$. These type of spanners, called \emph{linear spanners}, generalizes \emph{additive spanners} and \emph{multiplicative spanners}. Recently, Fomin, Golovach, Lochet, Misra, Saurabh, and Sharma initiated the study of additive and multiplicative spanners for directed graphs (IPEC $2020$). In this article, we continue this line of research and prove that \textsc{Directed Linear Spanner} parameterized by the number of vertices $n$ admits no polynomial compression of size $\calO(n^{2 - ε})$ for any $ε> 0$ unless $\NP \subseteq \coNP/poly$. We show that similar results hold for \textsc{Directed Additive Spanner} and \textsc{Directed Multiplicative Spanner} problems. This sparsification lower bound holds even when the input is a directed acyclic graph and $α, β$ are \emph{any} computable functions of the distance being approximated.
Conditional Measurement Density Estimation in Sequential Monte Carlo via Normalizing Flow
Venue: arXiv cs.AI 2022
Year: 2022

Tuning of measurement models is challenging in real-world applications of sequential Monte Carlo methods. Recent advances in differentiable particle filters have led to various efforts to learn measurement models through neural networks. But existing approaches in the differentiable particle filter framework do not admit valid probability densities in constructing measurement models, leading to incorrect quantification of the measurement uncertainty given state information. We propose to learn expressive and valid probability densities in measurement models through conditional normalizing flows, to capture the complex likelihood of measurements given states. We show that the proposed approach leads to improved estimation performance and faster training convergence in a visual tracking experiment.
Artificial Intelligence Enables Real-Time and Intuitive Control of Prostheses via Nerve Interface
Venue: arXiv cs.RO 2022
Year: 2022

Objective: The next generation prosthetic hand that moves and feels like a real hand requires a robust neural interconnection between the human minds and machines. Methods: Here we present a neuroprosthetic system to demonstrate that principle by employing an artificial intelligence (AI) agent to translate the amputee's movement intent through a peripheral nerve interface. The AI agent is designed based on the recurrent neural network (RNN) and could simultaneously decode six degree-of-freedom (DOF) from multichannel nerve data in real-time. The decoder's performance is characterized in motor decoding experiments with three human amputees. Results: First, we show the AI agent enables amputees to intuitively control a prosthetic hand with individual finger and wrist movements up to 97-98% accuracy. Second, we demonstrate the AI agent's real-time performance by measuring the reaction time and information throughput in a hand gesture matching task. Third, we investigate the AI agent's long-term uses and show the decoder's robust predictive performance over a 16-month implant duration. Conclusion & significance: Our study demonstrates the potential of AI-enabled nerve technology, underling the next generation of dexterous and intuitive prosthetic hands.
Know your sensORs -- A Modality Study For Surgical Action Classification
Venue: arXiv cs.CV 2022
Year: 2022

The surgical operating room (OR) presents many opportunities for automation and optimization. Videos from various sources in the OR are becoming increasingly available. The medical community seeks to leverage this wealth of data to develop automated methods to advance interventional care, lower costs, and improve overall patient outcomes. Existing datasets from OR room cameras are thus far limited in size or modalities acquired, leaving it unclear which sensor modalities are best suited for tasks such as recognizing surgical action from videos. This study demonstrates that surgical action recognition performance can vary depending on the image modalities used. We perform a methodical analysis on several commonly available sensor modalities, presenting two fusion approaches that improve classification performance. The analyses are carried out on a set of multi-view RGB-D video recordings of 18 laparoscopic procedures.
GPU-Accelerated Parallel Gene-pool Optimal Mixing in a Gray-Box Optimization Setting
Venue: arXiv cs.NE 2022
Year: 2022

In a Gray-Box Optimization (GBO) setting that allows for partial evaluations, the fitness of an individual can be updated efficiently after a subset of its variables has been modified. This enables more efficient evolutionary optimization with the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) due to its key strength: Gene-pool Optimal Mixing (GOM). For each solution, GOM performs variation for many (small) sets of variables. To improve efficiency even further, parallel computing can be leveraged. For EAs, typically, this comprises population-wise parallelization. However, unless population sizes are large, this offers limited gains. For large GBO problems, parallelizing GOM-based variation holds greater speed-up potential, regardless of population size. However, this potential cannot be directly exploited because of dependencies between variables. We show how graph coloring can be used to group sets of variables that can undergo variation in parallel without violating dependencies. We test the performance of a CUDA implementation of parallel GOM on a Graphics Processing Unit (GPU) for the Max-Cut problem, a well-known problem for which the dependency structure can be controlled. We find that, for sufficiently large graphs with limited connectivity, finding high-quality solutions can be achieved up to 100 times faster, showcasing the great potential of our approach.
Base-Rate Fallacy Redux and a Deep Dive Review in Cybersecurity
Venue: arXiv cs.CR 2022
Year: 2022

This paper examines the current state of the science underlying cybersecurity research with an emphasis on the non-signature-based intrusion detection domain. First, the paper re-examines the base-rate fallacy originally published by Axelsson, putting the impact of false positives into context. Given the relative high numbers of false positives, the paper argues for deeper analysis of false positives, akin to the analysis that true positives are treated to. The second section of the paper examines the metrics being used to analyze non-signature intrusion detection techniques, the current status quo of employed metrics, and the impact of the status quo on scientific advancement. Finally, the paper analyzes the use of online attack graphs and their applicability, especially in scenarios of constrained environments, such as Internet of Things devices. The use of offline attack graphs in such constrained environments is also examined. In essence, a deep dive review identified multiple areas throughout the field in which the effectiveness and validity of the scientific method can be greatly improved, e.g., through removal of logical fallacies.
Code Smells in Elixir: Early Results from a Grey Literature Review
Venue: arXiv cs.SE 2022
Year: 2022

Elixir is a new functional programming language whose popularity is rising in the industry. However, there are few works in the literature focused on studying the internal quality of systems implemented in this language. Particularly, to the best of our knowledge, there is currently no catalog of code smells for Elixir. Therefore, in this paper, through a grey literature review, we investigate whether Elixir developers discuss code smells. Our preliminary results indicate that 11 of the 22 traditional code smells cataloged by Fowler and Beck are discussed by Elixir developers. We also propose a list of 18 new smells specific for Elixir systems and investigate whether these smells are currently identified by Credo, a well-known static code analysis tool for Elixir. We conclude that only two traditional code smells and one Elixir-specific code smell are automatically detected by this tool. Thus, these early results represent an opportunity for extending tools such as Credo to detect code smells and then contribute to improving the internal quality of Elixir systems.
Explaining Preference-driven Schedules: the EXPRES Framework
Venue: arXiv cs.AI 2022
Year: 2022

Scheduling is the task of assigning a set of scarce resources distributed over time to a set of agents, who typically have preferences about the assignments they would like to get. Due to the constrained nature of these problems, satisfying all agents' preferences is often infeasible, which might lead to some agents not being happy with the resulting schedule. Providing explanations has been shown to increase satisfaction and trust in solutions produced by AI tools. However, it is particularly challenging to explain solutions that are influenced by and impact on multiple agents. In this paper we introduce the EXPRES framework, which can explain why a given preference was unsatisfied in a given optimal schedule. The EXPRES framework consists of: (i) an explanation generator that, based on a Mixed-Integer Linear Programming model, finds the best set of reasons that can explain an unsatisfied preference; and (ii) an explanation parser, which translates the generated explanations into human interpretable ones. Through simulations, we show that the explanation generator can efficiently scale to large instances. Finally, through a set of user studies within J.P. Morgan, we show that employees preferred the explanations generated by EXPRES over human-generated ones when considering workforce scheduling scenarios.