Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Memristive Deep Learning Systems for Real-Time Mobile Epileptic Seizure Prediction

The unpredictability of seizures continues to distress many people with drug-resistant epilepsy. On account of recent technological advances, considerable efforts have been made using different hardware technologies to realize smart devices for the real-time detection and prediction of seizures. In this paper, we investigate the feasibility of using Memristive Deep Learning Systems (MDLSs) to perform real-time epileptic seizure prediction on the edge. Using the MemTorch simulation framework and the Children's Hospital Boston (CHB)-Massachusetts Institute of Technology (MIT) dataset we determine the performance of various simulated MDLS configurations. An average sensitivity of 77.4% and a Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.85 are reported for the optimal configuration that can process Electroencephalogram (EEG) spectrograms with 7,680 samples in 1.408ms while consuming 0.0133W and occupying an area of 0.1269mm2 in a 65nm Complementary Metal-Oxide-Semiconductor (CMOS) process.
Computation of categorical entropy via spherical functors

We study the relationship between the categorical entropy of the twist and cotwist functors along a spherical functor. In particular, we prove the categorical entropy of the twist functor coincides with that of the cotwist functor if the essential image of the right adjoint functor of the spherical functor contains a split‐generator. We also see our results to generalize the computations of the categorical entropy of spherical twists and P$\mathbb {P}$ ‐twists by Ouchi and Fan. As an application, we apply our results to the Gromov–Yomdin‐type conjecture by Kikuta–Takahashi.
Multi-Agent Multi-Armed Bandits with Limited Communication

We consider the problem where $N$ agents collaboratively interact with an instance of a stochastic $K$ arm bandit problem for $K \gg N$. The agents aim to simultaneously minimize the cumulative regret over all the agents for a total of $T$ time steps, the number of communication rounds, and the number of bits in each communication round. We present Limited Communication Collaboration - Upper Confidence Bound (LCC-UCB), a doubling-epoch based algorithm where each agent communicates only after the end of the epoch and shares the index of the best arm it knows. With our algorithm, LCC-UCB, each agent enjoys a regret of $\tilde{O}\left(\sqrt{({K/N}+ N)T}\right)$, communicates for $O(\log T)$ steps and broadcasts $O(\log K)$ bits in each communication step. We extend the work to sparse graphs with maximum degree $K_G$, and diameter $D$ and propose LCC-UCB-GRAPH which enjoys a regret bound of $\tilde{O}\left(D\sqrt{(K/N+ K_G)DT}\right)$. Finally, we empirically show that the LCC-UCB and the LCC-UCB-GRAPH algorithm perform well and outperform strategies that communicate through a central node
The Linearized Hellinger-Kantorovich Distance

In this paper we study the local linearization of the Hellinger--Kantorovich distance via its Riemannian structure. We give explicit expressions for the logarithmic and exponential map and identify a suitable notion of a Riemannian inner product. Samples can thus be represented as vectors in the tangent space of a suitable reference measure where the norm locally approximates the original metric. Working with the local linearization and the corresponding embeddings allows for the advantages of the Euclidean setting, such as faster computations and a plethora of data analysis tools, whilst still enjoying approximately the descriptive power of the Hellinger--Kantorovich metric.
Improved dependency management for issue trackers in large collaborative projects

—Issue trackers, such as Jira, have become the prevalent collaborative tools in software engineering for managing issues, such as requirements, development tasks, and software bugs. However, issue trackers inherently focus on the life-cycle of single issues although issues have and express dependencies on other issues that constitute an issue dependency network in a large complex collaborative projects. The objective of this study is to develop supportive solutions for the improved management of dependent issues in an issue tracker. This study follows Design Science methodology, consisting of elicitation of drawbacks, and construction and evaluation of a solution and system. The study was carried out in the context of The Qt Company’s Jira, which exempliﬁes an actively used, almost two decade old issue tracker with over 100,000 issues. The drawbacks capture how users operate with issue trackers to handle issue information in large, collaborative and long-lived projects. The basis of the solution is to keep issues and dependencies as separate objects and automatically construct an issue graph. Dependency detection complements the issue graph by proposing missing dependencies, and consistency check and diagnosis identify incompatible issue priorities and release assignments. Jira’s plugin and service-based system architecture realizes the functional and quality concerns of the system implementation. We show how to adopt the supporting intelligent techniques of an issue tracker in a complex use context and a large data-set. The solution takes into account integrated and holistic system-view, practical applicability and utility, and the practical characteristics, such as inherent incompleteness, of issue data.
Client-Based Intelligence for Resource Efficient Vehicular Big Data Transfer in Future 6G Networks

Vehicular big data is anticipated to become the “new oil” of the automotive industry which fuels the development of novel crowdsensing-enabled services. However, the tremendous amount of transmitted vehicular sensor data represents a massive challenge for the cellular network. A promising method for achieving relief which allows to utilize the existing network resources in a more efficient way is the utilization of intelligence on the end-edge-cloud devices. Through machine learning-based identification and exploitation of highly resource efficient data transmission opportunities, the client devices are able to participate in overall network resource optimization process. In this work, we present a novel client-based opportunistic data transmission method for delay-tolerant applications which is based on a hybrid machine learning approach: Supervised learning is applied to forecast the currently achievable data rate which serves as the metric for the reinforcement learning-based data transfer scheduling process. In addition, unsupervised learning is applied to uncover geospatially-dependent uncertainties within the prediction model. In a comprehensive real world evaluation in the public cellular networks of three German Mobile Network Operator (MNO), we show that the average data rate can be improved by up to 223% while simultaneously reducing the amount of occupied network resources by up to 89%. As a side-effect of preferring more robust network conditions for the data transfer, the transmission-related power consumption is reduced by up to 73%. The price to pay is an increased Age of Information (AoI) of the sensor data.
Political Bias and Factualness in News Sharing Across more then 100, 000 Online Communities

As civil discourse increasingly takes place online, misinformation and the polarization of news shared in online communities have become ever more relevant concerns with real world harms across our society. Studying online news sharing at scale is challenging due to the massive volume of content which is shared by millions of users across thousands of communities. Therefore, existing research has largely focused on specific communities or specific interventions, such as bans. However, understanding the prevalence and spread of misinformation and polarization more broadly, across thousands of online communities, is critical for the development of governance strategies, interventions, and community design. Here, we conduct the largest study of news sharing on reddit to date, analyzing more than 550 million links spanning 4 years. We use non-partisan news source ratings from Media Bias/Fact Check to annotate links to news sources with their political bias and factualness. We find that, compared to left-leaning communities, right-leaning communities have 105% more variance in the political bias of their news sources, and more links to relatively-more biased sources, on average. We observe that reddit users’ voting and re-sharing behaviors generally decrease the visibility of extremely biased and low factual content, which receives 20% fewer upvotes and 30% fewer exposures from crossposts than more neutral or more factual content. This suggests that reddit is more resilient to low factual content than Twitter. We show that extremely biased and low factual content is very concentrated, with 99% of such content being shared in only 0.5% of communities, giving credence to the recent strategy of community-wide bans and quarantines.
Provably Efficient Policy Gradient Methods for Two-Player Zero-Sum Markov Games

Policy gradient methods are widely used in solving two-player zero-sum games to achieve superhuman performance in practice. However, it remains elusive when they can provably ﬁnd a near-optimal solution and how many samples and iterations are needed. The current paper studies natural extensions of Natural Policy Gradient algorithm for solving two-player zero-sum games where function approximation is used for generalization across states. We thoroughly characterize the algorithms’ performance in terms of the number of samples, number of iterations, concentrability coeﬃcients, and approximation error. To our knowledge, this is the ﬁrst quantitative analysis of policy gradient methods with function approximation for two-player zero-sum Markov games.
Supporting More Active Users for Massive Access via Data-assisted Activity Detection

Massive machine-type communication (mMTC) has been regarded as one of the most important use scenarios in the fifth generation (5G) and beyond wireless networks, which demands scalable access for a large number of devices. While grant-free random access has emerged as a promising mechanism for massive access, its potential has not been fully unleashed. Particularly, the two key tasks in massive access systems, namely, user activity detection and data detection, were handled separately in most existing studies, which ignored the common sparsity pattern in the received pilot and data signal. Moreover, error detection and correction in the payload data provide additional mechanisms for performance improvement. In this paper, we propose a data-assisted activity detection framework, which aims at supporting more active users by reducing the activity detection error, consisting of false alarm and missed detection errors. Specifically, after an initial activity detection step based on the pilot symbols, the false alarm users are filtered by applying energy detection for the data symbols; once data symbols of some active users have been successfully decoded, their effect in activity detection will be resolved via successive pilot interference cancellation, which reduces the missed detection error. Simulation results show that the proposed algorithm effectively increases the activity detection accuracy, and it is able to support ∼20% more active users compared to a conventional method in some sample scenarios.
Prioritizing Original News on Facebook

This work outlines how we prioritize original news, a critical indicator of news quality. By examining the landscape and lifecycle of news posts on our social media platform, we identify challenges of building and deploying an originality score. We pursue an approach based on normalized PageRank values and three-step clustering, and refresh the score on an hourly basis to capture the dynamics of online news. We describe a near real-time system architecture, evaluate our methodology, and deploy it to production. Our empirical results validate individual components and show that prioritizing original news increases user engagement with news and improves proprietary cumulative metrics.
Surveying the Landscape of Ethics-Focused Design Methods

Over the past decade, HCI researchers, design researchers, and practitioners have increasingly addressed ethics-focused issues through a range of theoretical, methodological and pragmatic contributions to the field. While many forms of design knowledge have been proposed and described, we focus explicitly on knowledge that has been codified as “methods,” which we define as any supports for everyday work practices of designers. In this paper, we identify, analyze, and map a collection of 63 existing ethics-focused methods intentionally designed for ethical impact. We present a content analysis, providing a descriptive record of how they operationalize ethics, their intended audience or context of use, their “core” or “script,” and the means by which these methods are formulated, articulated, and languaged. Building on these results, we provide an initial definition of ethics-focused methods identifying potential opportunities for the development of future methods to support design practice and research.
Sparsely Factored Neural Machine Translation

The standard approach to incorporate linguistic information to neural machine translation systems consists in maintaining separate vocabularies for each of the annotated features to be incorporated (e.g. POS tags, dependency relation label), embed them, and then aggregate them with each subword in the word they belong to. This approach, however, cannot easily accommodate annotation schemes that are not dense for every word. We propose a method suited for such a case, showing large improvements in out-of-domain data, and comparable quality for the in-domain data. Experiments are performed in morphologically-rich languages like Basque and German, for the case of low-resource scenarios.
Domain Generalization Needs Stochastic Weight Averaging for Robustness on Domain Shifts

Domain generalization aims to learn a generalizable model to unseen target domains from multiple source domains. Various approaches have been proposed to address this problem. However, recent benchmarks show that most of them do not provide signiﬁcant improvements compared to the simple empirical risk minimization (ERM) in practical cases. In this paper, we analyze how ERM works in views of domain-invariant feature learning and domain-speciﬁc gradient normalization. In addition, we observe that ERM converges to a loss valley shared over multiple training domains and obtain an insight that a center of the valley generalizes better. To estimate the center, we employ stochastic weight averaging (SWA) and provide theoretical analysis describing how SWA supports the generalization bound for an un-seen domain. As a result, we achieve state-of-the-art performances over all of widely used domain generalization benchmarks, namely PACS , VLCS , OfficeHome , TerraIncognita , and Do-mainNet with large margins. Further analysis reveals how SWA operates on domain generalization tasks.
The Planck Submillimeter Properties of Galactic High-mass Star-forming Regions: Dust Temperatures, Luminosities, Masses, and Star Formation Efficiency

Massive star formation occurs in the interior of giant molecular clouds and proceeds through many stages. In this work, we focus on massive young stellar objects (MYSOs) and ultracompact H ii regions (UCH ii), where the former are enshrouded in dense envelopes of dust and gas, the latter of which has begun dispersing. By selecting a complete sample of MYSOs and UCH ii from the Red MSX Source (RMS) survey database, we combine Planck and IRAS data and build their spectral energy distributions. With these, we estimate the physical properties (dust temperatures, mass, luminosity) of the sample. Because the RMS database provides unique solar distances, it also allows the instantaneous star formation efficiency (SFE) to be investigated as a function of Galactocentric radius. We find that the SFE increases between 2 and 4.5 kpc, where it reaches a peak, likely in correspondence with the accumulation of molecular material at the end of the Galactic bar. It then stays approximately constant up to 9 kpc, after which it linearly declines, in agreement with predictions from extragalactic studies. This behavior suggests the presence of a significant amount of undetected molecular gas at R G > 8 kpc. Finally, we present diagnostic colors that can be used to identify sites of massive star formation.
Dual process in the two-parameter Poisson-Dirichlet diffusion

The two-parameter Poisson-Dirichlet diffusion takes values in the infinite ordered simplex and extends the celebrated infinitely-many-neutral-alleles model, with a two-parameter Poisson-Dirichlet stationary distribution. Here we identify a dual process for this diffusion and obtain its transition probabilities. The dual is shown to be given by Kingman's coalescent with mutation, conditional on a given configuration of leaves. Interestingly, the dual does not depend on the additional parameter of the stationary distribution. After discussing the sampling probabilities of a two-parameter Poisson-Dirichlet partition drawn conditionally on another partition, we use these together with the dual process to derive the transition density of the diffusion. Our derivation provides a new probabilistic proof of this result, leveraging on an extension of Pitman's P\'olya urn scheme whereby the urn is split after a finite sequence and two urns are run independently onwards. The proof strategy exemplifies the power of duality and could be exported to other models where a dual is available.
Robust Factorization of Real-world Tensor Streams with Patterns, Missing Values, and Outliers

Consider multiple seasonal time series being collected in real-time, in the form of a tensor stream. Real-world tensor streams often include missing entries (e.g., due to network disconnection) and at the same time unexpected outliers (e.g., due to system errors). Given such a real-world tensor stream, how can we estimate missing entries and predict future evolution accurately in real-time? In this work, we answer this question by introducing SOFIA, a robust factorization method for real-world tensor streams. In a nutshell, SOFIA smoothly and tightly integrates tensor factorization, outlier removal, and temporal-pattern detection, which naturally reinforce each other. Moreover, SOFIA integrates them in linear time, in an online manner, despite the presence of missing entries. We experimentally show that SOFIA is (a) robust and accurate: yielding up to 76% lower imputation error and 71% lower forecasting error; (b) fast: up to 935× faster than the second-most accurate competitor; and (c) scalable: scaling linearly with the number of new entries per time step.
Rethinking Co-design of Neural Architectures and Hardware Accelerators

Neural architectures and hardware accelerators have been two driving forces for the progress in deep learning. Previous works typically attempt to optimize hardware given a fixed model architecture or model architecture given fixed hardware. And the dominant hardware architecture explored in this prior work is FPGAs. In our work, we target the optimization of hardware and software configurations on an industry-standard edge accelerator. We systematically study the importance and strategies of co-designing neural architectures and hardware accelerators. We make three observations: 1) the software search space has to be customized to fully leverage the targeted hardware architecture, 2) the search for the model architecture and hardware architecture should be done jointly to achieve the best of both worlds, and 3) different use cases lead to very different search outcomes. Our experiments show that the joint search method consistently outperforms previous platform-aware neural architecture search, manually crafted models, and the state-of-the-art EfficientNet on all latency targets by around 1% on ImageNet top-1 accuracy. Our method can reduce energy consumption of an edge accelerator by up to 2x under the same accuracy constraint, when co-adapting the model architecture and hardware accelerator configurations.
User Tracking in the Post-cookie Era: How Websites Bypass GDPR Consent to Track Users

During the past few years, mostly as a result of the GDPR and the CCPA, websites have started to present users with cookie consent banners. These banners are web forms where the users can state their preference and declare which cookies they would like to accept, if such option exists. Although requesting consent before storing any identifiable information is a good start towards respecting the user privacy, yet previous research has shown that websites do not always respect user choices. Furthermore, considering the ever decreasing reliance of trackers on cookies and actions browser vendors take by blocking or restricting third-party cookies, we anticipate a world where stateless tracking emerges, either because trackers or websites do not use cookies, or because users simply refuse to accept any. In this paper, we explore whether websites use more persistent and sophisticated forms of tracking in order to track users who said they do not want cookies. Such forms of tracking include first-party ID leaking, ID synchronization, and browser fingerprinting. Our results suggest that websites do use such modern forms of tracking even before users had the opportunity to register their choice with respect to cookies. To add insult to injury, when users choose to raise their voice and reject all cookies, user tracking only intensifies. As a result, users’ choices play very little role with respect to tracking: we measured that more than 75% of tracking activities happened before users had the opportunity to make a selection in the cookie consent banner, or when users chose to reject all cookies.
Loading and relaxation dynamics of a red blood cell.

We use mesoscale numerical simulations to investigate the unsteady dynamics of a single red blood cell (RBC) subjected to an external mechanical load. We carry out a detailed comparison between the loading (L) dynamics, following the imposition of the mechanical load on the RBC at rest, and the relaxation (R) dynamics, allowing the RBC to relax to its original shape after the sudden arrest of the mechanical load. Such a comparison is carried out by analyzing the characteristic times of the two corresponding dynamics, i.e., tL and tR. When the intensity of the mechanical load is small enough, the two kinds of dynamics are symmetrical (tL ≈ tR) and independent of the typology of mechanical load (intrinsic dynamics); otherwise, in marked contrast, an asymmetry is found, wherein the loading dynamics is typically faster than the relaxation one. This asymmetry manifests itself with non-universal characteristics, e.g., dependency on the applied load and/or on the viscoelastic properties of the RBC membrane. To deepen such a non-universal behaviour, we consider the viscosity of the erythrocyte membrane as a variable parameter and focus on three different typologies of mechanical load (mechanical stretching, shear flow, elongational flow): this allows to clarify how non-universality builds up in terms of the deformation and rotational contributions induced by the mechanical load on the membrane. Finally, we also investigate the effect of the elastic shear modulus on the characteristic times tL and tR. Our results provide crucial and quantitative information on the unsteady dynamics of RBC and its membrane response to the imposition/cessation of external mechanical loads.