Miracle-dz/UniFAR-FTUs huggingface dataset:
Terminal Brain Damage: Exposing the Graceless Degradation in Deep Neural Networks Under Hardware Fault Attacks

Deep neural networks (DNNs) have been shown to tolerate "brain damage": cumulative changes to the network's parameters (e.g., pruning, numerical perturbations) typically result in a graceful degradation of classification accuracy. However, the limits of this natural resilience are not well understood in the presence of small adversarial changes to the DNN parameters' underlying memory representation, such as bit-flips that may be induced by hardware fault attacks. We study the effects of bitwise corruptions on 19 DNN models---six architectures on three image classification tasks---and we show that most models have at least one parameter that, after a specific bit-flip in their bitwise representation, causes an accuracy loss of over 90%. We employ simple heuristics to efficiently identify the parameters likely to be vulnerable. We estimate that 40-50% of the parameters in a model might lead to an accuracy drop greater than 10% when individually subjected to such single-bit perturbations. To demonstrate how an adversary could take advantage of this vulnerability, we study the impact of an exemplary hardware fault attack, Rowhammer, on DNNs. Specifically, we show that a Rowhammer enabled attacker co-located in the same physical machine can inflict significant accuracy drops (up to 99%) even with single bit-flip corruptions and no knowledge of the model. Our results expose the limits of DNNs' resilience against parameter perturbations induced by real-world fault attacks. We conclude by discussing possible mitigations and future research directions towards fault attack-resilient DNNs.
Exploiting Sentential Context for Neural Machine Translation

In this work, we present novel approaches to exploit sentential context for neural machine translation (NMT). Specifically, we show that a shallow sentential context extracted from the top encoder layer only, can improve translation performance via contextualizing the encoding representations of individual words. Next, we introduce a deep sentential context, which aggregates the sentential context representations from all of the internal layers of the encoder to form a more comprehensive context representation. Experimental results on the WMT14 English-German and English-French benchmarks show that our model consistently improves performance over the strong Transformer model, demonstrating the necessity and effectiveness of exploiting sentential context for NMT.
Market Making and Latency

This paper studies optimal market making for large--tick assets in the presence of latency. We formulate the problem using Markov Decision Processes. We provide explicit characterizations of the order value and the structure of the value functions. We use such characterizations to study when a market maker can earn a positive net profit and investigate the effect of latency. Numerical experiments are conducted to illustrate our results.
Like a Baby: Visually Situated Neural Language Acquisition

We examine the benefits of visual context in training neural language models to perform next-word prediction. A multi-modal neural architecture is introduced that outperform its equivalent trained on language alone with a 2% decrease in perplexity, even when no visual context is available at test. Fine-tuning the embeddings of a pre-trained state-of-the-art bidirectional language model (BERT) in the language modeling framework yields a 3.5% improvement. The advantage for training with visual context when testing without is robust across different languages (English, German and Spanish) and different models (GRU, LSTM, Delta-RNN, as well as those that use BERT embeddings). Thus, language models perform better when they learn like a baby, i.e, in a multi-modal environment. This finding is compatible with the theory of situated cognition: language is inseparable from its physical context.
Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts

Emotion cause extraction (ECE), the task aimed at extracting the potential causes behind certain emotions in text, has gained much attention in recent years due to its wide applications. However, it suffers from two shortcomings: 1) the emotion must be annotated before cause extraction in ECE, which greatly limits its applications in real-world scenarios; 2) the way to first annotate emotion and then extract the cause ignores the fact that they are mutually indicative. In this work, we propose a new task: emotion-cause pair extraction (ECPE), which aims to extract the potential pairs of emotions and corresponding causes in a document. We propose a 2-step approach to address this new ECPE task, which first performs individual emotion extraction and cause extraction via multi-task learning, and then conduct emotion-cause pairing and filtering. The experimental results on a benchmark emotion cause corpus prove the feasibility of the ECPE task as well as the effectiveness of our approach.
The effective field theory of large scale structure at three loops

We study the power spectrum of dark matter density fluctuations in the framework of the Effective Field Theory of Large Scale Structures (EFTofLSS) up to three loop orders. We show that the extra coefficients in the EFT are sufficient to match numerical simulations with percent accuracy when a generic renormalization prescription is implemented (allowing for running of the individual counter-terms). We show that the level of accuracy increases with respect to the two loop results, up to k ≃ 0.4 h Mpc−1 at redshift z=0, although the overall improvement is somewhat marginal. At the same time, we argue there is evidence that the behavior of the loop expansion in the EFTofLSS is typical of an asymptotic series, already on the brink of its maximum predictive power (at z=0). Hence, the inclusion of higher orders will likely deteriorate the matching to data, even at moderate values of k. Part of the reason for this behavior is due to large contributions to the (renormalized) power spectrum at three loop order from mildly non-linear scales, even after the UV counter-terms are included. In conclusion, the EFTofLSS to three loop orders provides the best approximation to the (deterministic part of the) power spectrum in the weakly non-linear regime at z=0, and higher loops are not expected to improve our level of accuracy.
Cross-Domain Cascaded Deep Feature Translation

In recent years we have witnessed tremendous progress in unpaired image-to-image translation methods, propelled by the emergence of DNNs and adversarial training strategies. However, most existing methods focus on transfer of style and appearance, rather than on shape translation. The latter task is challenging, due to its intricate non-local nature, which calls for additional supervision. We mitigate this by descending the deep layers of a pre-trained network, where the deep features contain more semantics, and applying the translation from and between these deep features. Specifically, we leverage VGG, which is a classification network, pre-trained with large-scale semantic supervision. Our translation is performed in a cascaded, deep-to-shallow, fashion, along the deep feature hierarchy: we first translate between the deepest layers that encode the higher-level semantic content of the image, proceeding to translate the shallower layers, conditioned on the deeper ones. We show that our method is able to translate between different domains, which exhibit significantly different shapes. We evaluate our method both qualitatively and quantitatively and compare it to state-of-the-art image-to-image translation methods. Our code and trained models will be made available.
Convergence of Learning Dynamics in Stackelberg Games

This paper investigates the convergence of learning dynamics in Stackelberg games. In the class of games we consider, there is a hierarchical game being played between a leader and a follower with continuous action spaces. We establish a number of connections between the Nash and Stackelberg equilibrium concepts and characterize conditions under which attracting critical points of simultaneous gradient descent are Stackelberg equilibria in zero-sum games. Moreover, we show that the only stable critical points of the Stackelberg gradient dynamics are Stackelberg equilibria in zero-sum games. Using this insight, we develop a gradient-based update for the leader while the follower employs a best response strategy for which each stable critical point is guaranteed to be a Stackelberg equilibrium in zero-sum games. As a result, the learning rule provably converges to a Stackelberg equilibria given an initialization in the region of attraction of a stable critical point. We then consider a follower employing a gradient-play update rule instead of a best response strategy and propose a two-timescale algorithm with similar asymptotic convergence guarantees. For this algorithm, we also provide finite-time high probability bounds for local convergence to a neighborhood of a stable Stackelberg equilibrium in general-sum games. Finally, we present extensive numerical results that validate our theory, provide insights into the optimization landscape of generative adversarial networks, and demonstrate that the learning dynamics we propose can effectively train generative adversarial networks.
The One or the Many

This article aims to identify and critically assess qualitative intervention studies of rehabilitation processes that target young adults. By applying a meta-epistemological approach inspired by the works of Michel Foucault and Julia Kristeva, we examine how the included studies present qualitative knowledge and whether they adhere to their own stated principles of qualitative knowledge. Through their stated aims and theoretical framing, the articles draw attention to individual processes of meaning making. Nonetheless, we find that the articles to a great extent emphasize frequencies of the qualitative data they present. Individual processes and experiences are subject to subdivisions and categorization and transformed into manageable objects of knowledge. In conclusion, these studies, with one important exception, contribute to self-marginalization of the knowledge they themselves promote: They undermine the uniqueness of the qualitative knowledge they proclaim by focusing on frequency and the general patterns and categories encompassing the unique.
Confidence Regions in Wasserstein Distributionally Robust Estimation

Estimators based on Wasserstein distributionally robust optimization are obtained as solutions of min-max problems in which the statistician selects a parameter minimizing the worst-case loss among all probability models within a certain distance from the underlying empirical measure in a Wasserstein sense. While motivated by the need to identify optimal model parameters or decision choices that are robust to model misspecification, these distributionally robust estimators recover a wide range of regularized estimators, including square-root lasso and support vector machines, among others. This paper studies the asymptotic normality of these distributionally robust estimators as well as the properties of an optimal confidence region induced by the Wasserstein distributionally robust optimization formulation. In addition, key properties of min-max distributionally robust optimization problems are also studied, for example, we show that distributionally robust estimators regularize the loss based on its derivative and we also derive general sufficient conditions which show the equivalence between the min-max distributionally robust optimization problem and the corresponding max-min formulation.
Exploring the effects of galaxy formation on matter clustering through a library of simulation power spectra

Upcoming weak lensing surveys require a detailed theoretical understanding of the matter power spectrum in order to derive accurate and precise cosmological parameter values. While galaxy formation is known to play an important role, its precise effects are currently unknown. We present a set of 92 matter power spectra from the OWLS, cosmo-OWLS, and BAryons and HAloes of MAssive Systems simulation suites, including different ΛCDM cosmologies, neutrino masses, subgrid prescriptions, and AGN feedback strengths. We conduct a detailed investigation of the dependence of the relative difference between the total matter power spectra in hydrodynamical and collisionless simulations on the effectiveness of stellar and AGN feedback, cosmology, and redshift. The strength of AGN feedback can greatly affect the power on a range of scales, while a lack of stellar feedback can greatly increase the effectiveness of AGN feedback on large scales. We also examine differences in the initial conditions of hydrodynamic and N-body simulations that can lead to an $\sim 1{{\ \rm per\ cent}}$ discrepancy in the large-scale power, and furthermore show our results to be insensitive to cosmic variance. We present an empirical model capable of predicting the effect of galaxy formation on the matter power spectrum at z = 0 to within $1{{\ \rm per\ cent}}$ for $k\lt 1\, h\, \mathrm{Mpc}^{-1}$, given only the mean baryon fraction in galaxy groups. Differences in group baryon fractions can also explain the quantitative disagreement between predictions from the literature. All total and dark matter only power spectra in this library will be made publicly available at powerlib.strw. leidenuniv.nl.
How Large Are Lions? Inducing Distributions over Quantitative Attributes

Most current NLP systems have little knowledge about quantitative attributes of objects and events. We propose an unsupervised method for collecting quantitative information from large amounts of web data, and use it to create a new, very large resource consisting of distributions over physical quantities associated with objects, adjectives, and verbs which we call Distributions over Quantitative (DoQ). This contrasts with recent work in this area which has focused on making only relative comparisons such as “Is a lion bigger than a wolf? ”. Our evaluation shows that DoQ compares favorably with state of the art results on existing datasets for relative comparisons of nouns and adjectives, and on a new dataset we introduce.
Training Neural Response Selection for Task-Oriented Dialogue Systems

Despite their popularity in the chatbot literature, retrieval-based models have had modest impact on task-oriented dialogue systems, with the main obstacle to their application being the low-data regime of most task-oriented dialogue tasks. Inspired by the recent success of pretraining in language modelling, we propose an effective method for deploying response selection in task-oriented dialogue. To train response selection models for task-oriented dialogue tasks, we propose a novel method which: 1) pretrains the response selection model on large general-domain conversational corpora; and then 2) fine-tunes the pretrained model for the target dialogue domain, relying only on the small in-domain dataset to capture the nuances of the given dialogue domain. Our evaluation on five diverse application domains, ranging from e-commerce to banking, demonstrates the effectiveness of the proposed training method.
Two-dimensional topological materials discovery by symmetry-indicator method

Two-dimensional (2D) topological materials (TMs) have attracted tremendous attention due to the promise of revolutionary devices with non-dissipative electric or spin currents. Unfortunately, the scarcity of 2D TMs holds back the experimental realization of such devices. In this work, based on our recently developed, highly efficient TM discovery algorithm using symmetry indicators, we explore the possible 2D TMs in all non-magnetic compounds in four recently proposed materials databases for possible 2D materials. We identify hundreds of 2D TM candidates, including 205 topological (crystalline) insulators and 299 topological semimetals. In particular, we highlight MoS, with a mirror Chern number of -4, as a possible experimental platform for studying the interaction-induced modification to the topological classification of materials. Our results winnow out the topologically interesting 2D materials from these databases and provide a TM gene pool which for further experimental studies.
Generalized No Free Lunch Theorem for Adversarial Robustness

This manuscript presents some new impossibility results on adversarial robustness in machine learning, a very important yet largely open problem. We show that if conditioned on a class label the data distribution satisfies the $W_2$ Talagrand transportation-cost inequality (for example, this condition is satisfied if the conditional distribution has density which is log-concave; is the uniform measure on a compact Riemannian manifold with positive Ricci curvature, any classifier can be adversarially fooled with high probability once the perturbations are slightly greater than the natural noise level in the problem. We call this result The Strong "No Free Lunch" Theorem as some recent results (Tsipras et al. 2018, Fawzi et al. 2018, etc.) on the subject can be immediately recovered as very particular cases. Our theoretical bounds are demonstrated on both simulated and real data (MNIST). We conclude the manuscript with some speculation on possible future research directions.
Both aldosterone and spironolactone can modulate the intracellular ACE/ANG II/AT1 and ACE2/ANG (1‐7)/MAS receptor axes in human mesangial cells

The kidney is an important target of the renin‐ANG‐aldosterone system (RAAS). To date, several studies have demonstrated the existence of a local RAAS in various tissues, including the renal tissue. The mineralocorticoid aldosterone is known to play a critical role in the classical RAAS; however, its effect on mesangial cells (MCs) remains to be elucidated. Based on this, our aim was to investigate whether aldosterone stimulation can modulate the intracellular RAAS of immortalized human MCs by evaluating ANG‐converting enzyme (ACE)/ANG II/ANG II receptor type 1 (AT1) and ANG‐converting enzyme 2 (ACE2)/ANG (1‐7)/MAS receptor axes. To realise this, protein expression, enzyme activity, and immunofluorescence were performed under aldosterone stimulation and in the presence of the mineralocorticoid receptor (MR) antagonist spironolactone (SPI). We observed that high doses of aldosterone increase ACE activity. The effect of aldosterone on the catalytic activity of ACE was completely abolished with the pretreatment of SPI suggesting that the aldosterone‐induced cell injuries through ANG II release were attenuated. Aldosterone treatment also decreased the expression of MAS receptor, but did not alter the expression or the catalytic activity of ACE 2 and ANG (1‐7) levels. Spironolactone modulated the localization of ANG II and AT1 receptor and decreased ANG (1‐7) and MAS receptor levels. Our data suggest that both aldosterone and the MR receptor antagonist can modulate both of these axes and that spironolactone can protect MCs from the damage induced by aldosterone.
Community, facility and individual level impact of integrating mental health screening and treatment into the primary healthcare system in Sehore district, Madhya Pradesh, India

Introduction Programme for Improving Mental Health Care (PRIME) designed a comprehensive mental healthcare plan (MHCP) for Sehore district, Madhya Pradesh, India. The objective of this paper is to describe the findings of the district-level impact evaluation of the MHCP. Methods Repeat community-based CS were conducted to measure change in population-level contact coverage for depression and alcohol use disorders (AUD), repeat FDS were conducted to assess change in detection and initiation of treatment for depression and AUD, and the effect of treatment on patient outcomes was assessed using disorder-specific prospective cohort studies. Results PRIME MHCP did not have any impact on contact coverage/treatment seeking for depression (14.8% at the baseline and 10.5% at the follow-up) and AUD (7.7% at the baseline and 7.3% at the follow-up) and had a small impact on detection and initiation of treatment for depression and AUD (9.7% for depression and 17.8% for AUD compared with 0% for both at the baseline) in the health facilities. Patients with depression who received care as part of the MHCP had higher rates of response (52.2% in the treatment group vs 26.9% in the comparison/usual care group), early remission (70.2% in the treatment group vs 44.8% in the comparison/usual care group) and recovery (56.1% in the treatment group vs 28.5% in the comparison/usual care group), but there was no impact of treatment on their functioning. Conclusions While dedicated human resources (eg, Case Managers) and dedicated space for mental health clinics (eg, Mann-Kaksha) strengthen the ‘formal’ healthcare platform, without substantial additional investments in staff, such as Community Health Workers/Accredited Social Health Activists to improve community level processes and provision of community-based continuing care to patients, we are unlikely to see major changes in coverage or clinical outcomes.
Screening and exchange in the theory of the femtosecond kinetics of the electron-hole plasma.

This work deals with the dynamics of the nonthermalized electron-hole plasma in the subpicosecond regime. We computed different collision rates (electron-electron, electron-hole, electron-phonon) and discussed the relative efficiency of the different processes for the cases of static or dynamic screening of the interaction. We also considered how the inclusion of the exchange affected the scattering amplitude and the collision rates. Calculations were based upon GaAs. We conclude that dynamic screening is crucial for electrons in the conduction band at a high plasma density [say, at densities above (1\char21{}2)\ifmmode\times\else\texttimes\fi{}${10}^{16}$/${\mathrm{cm}}^{3}$], whereas it generally plays a minor role at low density and when descibing hole dynamics. The excitation of heavy holes into the light-hole band by collisions with the conduction electrons is discussed in heavily p-doped GaAs.
Remote Management of Heart Failure: An Overview of Telemonitoring Technologies

Technological advances have enabled increasingly sophisticated attempts to remotely monitor heart failure. This should allow earlier identification of decompensation, better adherence to lifestyle changes and medication and interventions (such as diuretic dosage changes) that reduce the need for hospitalisation. This review discusses telemonitoring approaches in heart failure, and the evidence for their impact. It is not difficult to collect data remotely, but converting more data into better decision-making that improves the outcome of care is challenging. Policy-makers and technology companies are enthusiastic about the potential of digital technologies to transform healthcare and bring expertise to the patient, rather than the other way round, but guideline writers are not yet convinced, due to the lack of consistent findings in randomised trials.