Miracle-dz/UniFAR-FTUs huggingface dataset:
Enabling Failure-Resilient Intermittent Systems Without Runtime Checkpointing

Self-powered intermittent systems typically adopt runtime checkpointing as a means to accumulate computation progress across power cycles and recover system status from power failures. However, existing approaches based on the checkpointing paradigm normally require system suspension and/or logging at runtime. This article presents a design which overcomes the drawbacks of checkpointing-based approaches, to enable failure-resilient intermittent systems. Our design allows accumulative execution and instant system recovery under frequent power failures while enforcing the serializability of concurrent task execution to improve computation progress and ensuring data consistency without system suspension during runtime, by leveraging the characteristics of data accessed in hybrid memory. We integrated the design into FreeRTOS running on a Texas Instruments device. The experimental results show that our design can still accumulate progress when the power source is too weak for checkpointing-based approaches to advance, and significantly improves the computation progress while reducing the recovery time.
Bright Lyman-α emitters among Spitzer SMUVS galaxies in the MUSE/COSMOS field

We search for the presence of bright Lyα emitters among Spitzer SMUVS galaxies at z > 2.9 making use of homogeneous MUSE spectroscopic data. Although these data only cover a small region of COSMOS, MUSE has the unique advantage of providing spectral information over the entire field, without the need of target pre-selection. This results in an unbiased detection of all the brightest Lyα emitters among the SMUVS sources, which by design are stellar-mass selected galaxies. Within the studied area, ∼14% of the SMUVS galaxies at z > 2.9 have Lyα fluxes Fλ ≳ 7 × 10−18 erg s−1 cm−2. These Lyα emitters are characterized by three types of emission, 47% show a single-line profile, 19% present a double peak or a blue bump, and 31% show a red tail. One object (3%) shows both a blue bump and a red tail. We also investigate the spectral energy distribution (SED) properties of the SMUVS galaxies that are MUSE detected and those that are not. After stellar mass matching the two populations, we find that the MUSE detected galaxies have generally lower extinction than SMUVS-only objects, while there is no clear intrinsic difference in the mass and age distributions of the two samples. For the MUSE-detected SMUVS galaxies, we compare the instantaneous star formation rate lower limit obtained from the Lyα line with its past average derived from SED fitting, and find evidence for rejuvenation in some of our oldest objects. In addition, we study the spectra of those Lyα emitters that are not detected in SMUVS in the same field. We find that of the emission line profiles shown 67% have a single line, 3% a blue bump, and 30% a red tail. The difference in profile distribution could be ascribed to the fainter Lyα luminosities of the MUSE sources not detected in SMUVS and an intrinsically different mass distribution. Finally, we search for the presence of galaxy associations using the spectral redshifts. The integral coverage of MUSE reveals that these associations are 20 times more likely than what is derived from all the other existing spectral data in COSMOS, which is biased by target pre-selection.
The frog model on non-amenable trees

We examine an interacting particle system on trees commonly referred to as the frog model. For its initial state, it begins with a single active particle at the root and i.i. d. $\mathrm{Poiss}(\lambda)$ many inactive particles at each non-root vertex. Active particles perform discrete time simple random walk and in the process activate any inactive particles they encounter. We show that for $\textit{every}$ non-amenable tree with bounded degree there exists a phase transition from transience to recurrence (with a non-trivial intermediate phase sometimes sandwiched in between) as $\lambda$ varies.
Improving sample diversity of a pre-trained, class-conditional GAN by changing its class embeddings

Mode collapse is a well-known issue with Generative Adversarial Networks (GANs) and is a byproduct of unstable GAN training. We propose to improve the sample diversity of a pre-trained class-conditional generator by modifying its class embeddings in the direction of maximizing the log probability outputs of a classifier pre-trained on the same dataset. We improved the sample diversity of state-of-the-art ImageNet BigGANs at both 128x128 and 256x256 resolutions. By replacing the embeddings, we can also synthesize plausible images for Places365 using a BigGAN pre-trained on ImageNet.
Addressing Failure Prediction by Learning Model Confidence

Assessing reliably the confidence of a deep neural net and predicting its failures is of primary importance for the practical deployment of these models. In this paper, we propose a new target criterion for model confidence, corresponding to the True Class Probability (TCP). We show how using the TCP is more suited than relying on the classic Maximum Class Probability (MCP). We provide in addition theoretical guarantees for TCP in the context of failure prediction. Since the true class is by essence unknown at test time, we propose to learn TCP criterion on the training set, introducing a specific learning scheme adapted to this context. Extensive experiments are conducted for validating the relevance of the proposed approach. We study various network architectures, small and large scale datasets for image classification and semantic segmentation. We show that our approach consistently outperforms several strong methods, from MCP to Bayesian uncertainty, as well as recent approaches specifically designed for failure prediction.
The VMC survey – XXXV. model fitting of LMC Cepheid light curves

We present the results of the light curve model fitting technique applied to optical and near-infrared photometric data for a sample of 18 Classical Cepheids (11 fundamentals and 7 first overtones) in the Large Magellanic Cloud (LMC). We use optical photometry from the OGLE III data base and near-infrared photometry obtained by the European Southern Observatory public survey ‘VISTA near-infrared survey of the Magellanic Clouds system’. Iso-periodic non-linear convective model sequences have been computed for each selected Cepheid in order to reproduce the multifilter light-curve amplitudes and shape details. The inferred individual distances provide an intrinsic weighted mean value for the LMC distance modulus of μ0 = 18.56 mag with a standard deviation of 0.13 mag. We derive also the Period–Radius, the Period–Luminosity, and the Period–Wesenheit relations that are consistent with similar relations in the literature. The intrinsic masses and luminosities of the best-fitting models show that all the investigated pulsators are brighter than the predictions of the canonical evolutionary mass–luminosity relation, suggesting a significant efficiency of non-canonical phenomena, such as overshooting, mass-loss, and/or rotation.
Non-Uniform Time-Step Deep Q-Network for Carrier-Sense Multiple Access in Heterogeneous Wireless Networks

This paper investigates a new class of carrier-sense multiple access (CSMA) protocols that employ deep reinforcement learning (DRL) techniques, referred to as carrier-sense deep-reinforcement learning multiple access (CS-DLMA). The goal of CS-DLMA is to enable efficient and equitable spectrum sharing among a group of co-located heterogeneous wireless networks. Existing CSMA protocols, such as the medium access control (MAC) protocol of WiFi, are designed for a homogeneous network in which all nodes adopt the same protocol. Such protocols suffer from severe performance degradation in a heterogeneous environment where there are nodes adopting other MAC protocols. CS-DLMA aims to circumvent this problem by making use of DRL. In particular, this paper adopts <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math><alternatives><mml:math><mml:mi>α</mml:mi></mml:math><inline-graphic xlink:href="liew-ieq1-2990399.gif"/></alternatives></inline-formula>-fairness as the general objective of CS-DLMA. With <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math><alternatives><mml:math><mml:mi>α</mml:mi></mml:math><inline-graphic xlink:href="liew-ieq2-2990399.gif"/></alternatives></inline-formula>-fairness, CS-DLMA can achieve a range of different objectives (e.g., maximizing sum throughput, achieving proportional fairness, or achieving max-min fairness) when coexisting with other MACs by changing the value of <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math><alternatives><mml:math><mml:mi>α</mml:mi></mml:math><inline-graphic xlink:href="liew-ieq3-2990399.gif"/></alternatives></inline-formula>. A salient feature of CS-DLMA is that it can achieve these objectives without knowing the coexisting MACs through a learning process based on DRL. The underpinning DRL technique in CS-DLMA is deep Q-network (DQN). However, the conventional DQN algorithms are not suitable for CS-DLMA due to their uniform time-step assumption. In CSMA protocols, time steps are non-uniform in that the time duration required for carrier sensing is smaller than the duration of data transmission. This paper introduces a non-uniform time-step formulation of DQN to address this issue. Our simulation results show that CS-DLMA can achieve the general <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math><alternatives><mml:math><mml:mi>α</mml:mi></mml:math><inline-graphic xlink:href="liew-ieq4-2990399.gif"/></alternatives></inline-formula>-fairness objective when coexisting with TDMA, ALOHA, and WiFi protocols by adjusting its own transmission strategy. Interestingly, we also find that CS-DLMA is more Pareto efficient than other CSMA protocols, e.g., p-persistent CSMA, when coexisting with WiFi. Although this paper focuses on the use of our non-uniform time-step DQN formulation in wireless networking, we believe this new DQN formulation can also find use in other domains.
Revealing the cosmic evolution of boxy/peanut-shaped bulges from HST COSMOS and SDSS

Vertically thickened bars, observed in the form of boxy/peanut (B/P) bulges, are found in the majority of massive barred disc galaxies in the local Universe, including our own. B/P bulges indicate that their host bars have suffered violent bending instabilities driven by anisotropic velocity distributions. We investigate for the first time how the frequency of B/P bulges in barred galaxies evolves from z = 1 to z ≈ 0, using a large sample of non-edge-on galaxies with masses M⋆ > 1010 M⊙, selected from the HST COSMOS survey. We find the observed fraction increases from $0^{+3.6}_{-0.0}{{\ \rm per\ cent}}$ at z = 1 to $37.8^{+5.4}_{-5.1}$ per cent at z = 0.2. We account for problems identifying B/P bulges in galaxies with low inclinations and unfavourable bar orientations, and due to redshift-dependent observational biases with the help of a sample from the Sloan Digital Sky Survey, matched in resolution, rest-frame band, signal-to-noise ratio and stellar mass and analysed in the same fashion. From this, we estimate that the true fraction of barred galaxies with B/P bulges increases from ∼10 per cent at z ≈ 1 to $\sim 70{{\ \rm per\ cent}}$ at z = 0. In agreement with previous results for nearby galaxies, we find a strong dependence of the presence of a B/P bulge on galaxy stellar mass. This trend is observed in both local and high-redshift galaxies, indicating that it is an important indicator of vertical instabilities across a large fraction of the age of the Universe. We propose that galaxy formation processes regulate the thickness of galaxy discs, which in turn affect which galaxies experience violent bending instabilities of the bar.
Working principle and demonstrator of microwave-multiplexing for the HOLMES experiment microcalorimeters

The determination of the neutrino mass is an open issue in modern particle physics and astrophysics. The direct mass measurement is the only theory-unrelated experimental tool capable to probe such quantity. The HOLMES experiment aims to measure the end-point energy of the electron capture (EC) decay of 163Ho with a statistical sensitivity on the neutrino mass as low as ∼ 1 eV/c2. In order to acquire the large needed statistics, by keeping the pile-up contribution as low as possible, 1024 transition edge sensors (TESs) with high energy and time resolutions will be employed. Microcalorimeter and bolometer arrays based on transition edge sensor with thousands of pixels are under development for several space-based and ground-based applications, including astrophysics, nuclear and particle physics, and materials science. The common necessary challenge is to develop pratical multiplexing techniques in order to simplify the cryogenics and readout systems. Despite the various multiplexing variants which are being developed have been successful, new approaches are needed to enable scaling to larger pixel counts and faster sensors, as requested for HOLMES, reducing also the cost and complexity of readout. A very novel technique that meets all of these requirements is based on superconducting microwave resonators coupled to radio-frequency Superconducting Quantum Interference Devices, in which the changes in the TES input current is tranduced to a change in phase of a microwave signal. In this work we introduce the basics of this technique, the design and development of the first two-channel read out system and its performances with the first TES detectors specifically designed for HOLMES. In the last part we explain how to extend this approach scaling to 1024 pixels.
Optimal Capital Income Taxation and Redistribution

This paper studies the effects of agent heterogeneity on optimal capital income tax rates. In a two period model with arbitrarily many heterogeneous agents, we explicitly derive the welfare effects of taxation depending on the distribution of the agents' characteristics. In particular, we show that the sign of the optimal capital income tax rate depends not on the extent of inequality in goods endowments and productivities each by itself, but on a measure of inequality in their joint distribution.
The Predictive Role of Analyst Coverage Intensity: Evidence from Cross-Listing in the U.S.

This paper investigates financial analysts' predictive power of future performance and earnings quality, based on their selective coverage of firms that have recently cross-listed into the U.S. This setting is useful for examining these questions because, following cross-listing, firms often experience an increase in analyst coverage and an improvement in their information environment. We find that analyst coverage is positively related to analysts' expectation about firms' future performance and negatively related to analysts' concern over firms' earnings quality. Furthermore, country-level legal origin and disclosure index are two significant determinants of analyst coverage of cross-listed firms. In addition, the intensity of analyst coverage can predict future abnormal stock price performance. Our latter finding augments those in Das, Guo, and Zhang (2006), who investigate the predictive role of analyst coverage following IPOs. The benefit of our setting is that, unlike the post-IPO period, cross-listing firms do not appear to experience mis-pricing that might confound the results. Overall, our study further documents the substantial informational benefits to cross-listing, but suggests that these informational benefits may not be complete, since financial analysts appear to have predictive power and selectively provide coverage for firms with favorable future prospects.
Banking Globalization and Monetary Transmission

The globalization of banking in the United States is influencing the monetary transmission mechanism both domestically and in foreign markets. Using quarterly information from all U.S. banks filing call reports between 1980 and 2006, we show that globalized banks activate internal capital markets with their overseas affiliates to insulate themselves partially from changes in domestic liquidity conditions. The existence of these internal capital markets directly contributes to an international propagation of domestic liquidity shocks to lending by affiliated banks abroad. While these results imply a substantially more active lending channel than documented in Kashyap and Stein (2000), they also imply that the lending channel within the United States is declining in strength as banking becomes more globalized and monetary transmission abroad likewise increases in strength.
Online control of the familywise error rate

Biological research often involves testing a growing number of null hypotheses as new data are accumulated over time. We study the problem of online control of the familywise error rate, that is testing an a priori unbounded sequence of hypotheses (p-values) one by one over time without knowing the future, such that with high probability there are no false discoveries in the entire sequence. This paper unifies algorithmic concepts developed for offline (single batch) familywise error rate control and online false discovery rate control to develop novel online familywise error rate control methods. Though many offline familywise error rate methods (e.g., Bonferroni, fallback procedures and Sidak’s method) can trivially be extended to the online setting, our main contribution is the design of new, powerful, adaptive online algorithms that control the familywise error rate when the p-values are independent or locally dependent in time. Our numerical experiments demonstrate substantial gains in power, that are also formally proved in an idealized Gaussian sequence model. A promising application to the International Mouse Phenotyping Consortium is described.
Psilocybin based therapy for cancer related distress, a systematic review and meta analysis

Background : depression and anxiety are common in patients with cancer, classical antidepressant has no proven efficacy on this type of distress compared to placebo. A Psilocybin (serotoninergic hallucinogen) based therapy appear to give promising results among recent studies. Aims : to examine if a Psilocybin based therapy could be considered for patients with cancer related depression and anxiety and to assume it's safety. To sum up Heffter institute work, as the main institute working on this topic. Method : following PRISMA (Preferred Reporting Items for Systematic reviews and Meta Analyses) guidelines, a systematic review was conducted, for quantitative and qualitative studies about psilocybin for treating cancer related depression and anxiety. Pubmed and the Heffter institute databases have been reached for this purpose, separating studies in types : qualitative or quantitative. We studied the effects on cancer related depression and anxiety separately and investigated the psychological and neurobiological mechanisms. Results : the four studies included a total of 105 randomized patients, meta analysis on depression and anxiety with pooled Peto odds ratio showed a significant superiority of Psilocybin over placebo. The substance appeared to be safe for this type of patients. Surprising psychological mechanisms hypothesis have been found out. Conclusion : psilocybin appear to be potentially useful as a treatment for cancer related depression and anxiety. Future research should verify these findings on wider population and eventually seek a way to apply therapy to non hospitalized (ambulatory) patients. Keywords : psilocybin, depression, anxiety, review, meta-analysis
Sampling The “Inverse Set” of a Neuron

With the recent success of deep neural networks in computer vision, it is important to understand the internal working of these networks. What does a given neuron represent? The concepts captured by a neuron may be hard to understand or express in simple terms. The approach we propose in this paper is to characterize the region of input space that excites a given neuron to a certain level; we call this the inverse set. This inverse set is a complicated high dimensional object that we explore by an optimization-based sampling approach. Inspection of samples of this set by a human can reveal regularities that help to understand the neuron. This goes beyond approaches which were limited to finding an image which maximally activates the neuron [1] or using Markov chain Monte Carlo to sample images [2], but this is very slow, generates samples with little diversity and lacks control over the activation value of the generated samples. Our approach also allows us to explore the intersection of inverse sets of several neurons and other variations.
The collective effect of finite-sized inhomogeneities on the spatial spread of populations in two dimensions

The dynamics of a population expanding into unoccupied habitat has been primarily studied for situations in which growth and dispersal parameters are uniform in space or vary in one dimension. Here, we study the influence of finite-sized individual inhomogeneities and their collective effect on front speed if randomly placed in a two-dimensional habitat. We use an individual-based model to investigate the front dynamics for a region in which dispersal or growth of individuals is reduced to zero (obstacles) or increased above the background (hotspots), respectively. In a regime where front dynamics is determined by a local front speed only, a principle of least time can be employed to predict front speed and shape. The resulting analytical solutions motivate an event-based algorithm illustrating the effects of several obstacles or hotspots. We finally apply the principle of least time to large heterogeneous environments by solving the Eikonal equation numerically. Obstacles lead to a slow-down that is dominated by the number density and width of obstacles, but not by their precise shape. Hotspots result in a speed-up, which we characterize as function of hotspot strength and density. Our findings emphasize the importance of taking the dimensionality of the environment into account.
Defence Against the Modern Arts: the Curse of Statistics "Score-based likelihood ratios".

For several decades, legal and scientific scholars have argued that conclusions from forensic examinations should be supported by statistical data and reported within a probabilistic framework. Multiple models have been proposed to quantify the probative value of forensic evidence. Unfortunately, several of these models rely on ad-hoc strategies that are not scientifically sound. The opacity of the technical jargon used to present these models and their results, and the complexity of the techniques involved make it very difficult for the untrained user to separate the wheat from the chaff. This series of papers is intended to help forensic scientists and lawyers recognise limitations and issues in tools proposed to interpret the results of forensic examinations. This paper focuses on tools that have been proposed to leverage the use of similarity scores to assess the probative value of forensic findings. We call this family of tools "score-based likelihood ratios". In this paper, we present the fundamental concepts on which these tools are built, we describe some specific members of this family of tools, and we explore their convergence to the Bayes factor through an intuitive geometrical approach and through simulations. Finally, we discuss their validation and their potential usefulness as a decision-making tool in forensic science.