Miracle-dz/UniFAR-FTUs huggingface dataset:
Tighter Bound Estimation of Sensitivity Analysis for Incremental and Decremental Data Modification

In large-scale classification problems, the data set may be faced with frequent updates, e.g., a small ratio of data is added to or removed from the original data set. In this case, incremental learning, which updates an existing classifier by explicitly modeling the data modification, is more efficient than retraining a new classifier from scratch. Conventional incremental learning algorithms try to solve the problem exactly. However, for some tasks, we are only interested in the lower and upper bound for some values relevant to the coefficient vector of the updated classifier without really solving it, e.g., determining whether we should update the classifier or performing some sensitivity analysis tasks. To deal with these such tasks, we propose an algorithm to make rational inferences about the updated classifier with low computational complexity. Specifically, we present a method to calculate tighter bounds of a general linear score for the updated classifier such that it's more accurate to estimate the range of interest than existing papers. The proposed method can be applied to any linear classifiers with differentiable convex L2 regularization loss function. Both theoretical analysis and experiment results show that the proposed approach is superior to existing methods.
Dynamical Transitions of Supercooled Water in Graphene Oxide Nanopores: Influence of Surface Hydrophilicity.

Molecular dynamics simulations are carried out to explore the dynamical crossover phenomenon in strongly confined and mildly supercooled water in graphene oxide nanopores. Since the extent of hydrophilicity can be varied on graphene oxide surfaces, they offer energetically heterogeneous environments that can potentially modulate the rotational and translational relaxation dynamics of confined water. The influence of the physicochemical nature of the graphene oxide surface on the dynamical transitions is investigated by varying the extent of hydrophobicity on the confining surfaces placed at an inter-surface separation of 10 Angstroms. Water forms two distinct layers in contact with the graphene oxide surface at this separation. All dynamical quantities show a typical slowing down as the temperature is lowered from 298 to 200 K; however, the nature of the transition is a distinct function of the surface type. Water confined between surfaces consisting of alternating hydrophilic and hydrophobic regions exhibit a strong-to-strong dynamical transition in the diffusion coefficients and rotational relaxation times at a crossover temperature of 237 K and show a fragile-to-strong transition in the α-relaxation time at 238 K. The observed crossover temperature is much higher than the freezing point of the SPC/E water model used in this study, indicating that these dynamical transitions can occur with mild supercooling under strong confinement in the absence of bulk-like water. In contrast, water confined in a hydrophilic nanopore shows a single Arrhenius energy barrier over the entire temperature range. Our results indicate that in addition to confinement, the nature of the surface can play a critical role in determining the dynamical transitions for water upon supercooling.
Lowest energy states of an 
O(N)
 fermionic chain

A quite general finite-size chain of fermions with $N$ internal degrees of freedom (flavors) and $O(N)$ symmetry is considered. In case of the free boundary condition, we prove that the ground state in the invariant sector with $m$ odd flavors is represented by a single rank-$m$ antisymmetric multiplet. For the even-length chains, its particle-hole quantum number (if is a good one) is given by the parity of the $m$. For the odd-length chains, the particle-hole symmetry leads to the twofold degeneracy among the conjugate multiplets. Similar statements are proven for the $O(N)$ mixed-spin chains in antisymmetric representations. The results are extended to the long-range interacting fermions and (partially) to the translation invariant chains.
Half-solution to the two-body problem in general relativity

We show that the introduction of two worldline parameters allows to drastically simplify computations in the effective field theory approach to the two-body problem in General Relativity, effectively removing half of the complexity in Feynman diagrams. These parameters obey a polynomial equation whose perturbative expansion recovers an infinite series of diagrams. Futhermore, we show that our equations define an effective two-body horizon for interacting black holes in General Relativity; in the circular orbit case, it corresponds to the smallest conceivable separation up to which the orbit can remain circular. We expect our results to simplify higher-order computations in the two-body problem, as well as to give insights on the nonperturbative properties of interacting binaries.
Statistical thermodynamics of dislocations in solids

The thermodynamic dislocation theory (TDT) is based on two fundamental but unconventional assumptions: first, that the dislocations in a persistently deforming crystalline solid must obey the second law of thermodynamics and thus be described by an effective temperature; and second, that the controlling time scale for deformation of these systems is the inverse of the thermally activated rate at which entangled dislocation lines become unpinned from each other. By use of these first-principles concepts and comparisons with experimental data, I show that this theory achieves new, usefully predictive understandings of strain hardening, yield stresses, shear banding, and brittle and ductile fracture. I argue that it opens new directions for research.
Parsing Thai Social Data: A New Challenge for Thai NLP

Dependency parsing (DP) is a task that analyzes text for syntactic structure and relationship between words. DP is widely used to improve natural language processing (NLP) applications in many languages such as English. Previous works on DP are generally applicable to formally written languages. However, they do not apply to informal languages such as the ones used in social networks. Therefore, DP has to be researched and explored with such social network data. In this paper, we explore and identify a DP model that is suitable for Thai social network data. After that, we will identify the appropriate linguistic unit as an input. The result showed that, the transition based model called, improve Elkared dependency parser outperform the others at UAS of 81.42%.
The explosion energy of the type IIP supernova SN 2013fs with a confined dense circumstellar shell

The recent study of the SN 2013fs flash spectrum suggests an enormous explosion energy for SNe IIP, far beyond the possibilities of the neutrino mechanism. The issue of the explosion energy of SN 2013fs is revisited, making use of the effects of the early supernova interaction with the dense circumstellar shell. The velocity of the cold dense shell between reverse and forward shocks is inferred from the analysis of the broad He ii 4686 Å on day 2.4. This velocity, alongside other observables, provides us with an alternative energy estimate of ∼1.8 × 1051 erg for the preferred mass of ∼10 M⊙. The inferred value is within the range of neutrino-driven explosions.
"Other-Play" for Zero-Shot Coordination

We consider the problem of zero-shot coordination - constructing AI agents that can coordinate with novel partners they have not seen before (e.g. humans). Standard Multi-Agent Reinforcement Learning (MARL) methods typically focus on the self-play (SP) setting where agents construct strategies by playing the game with themselves repeatedly. Unfortunately, applying SP naively to the zero-shot coordination problem can produce agents that establish highly specialized conventions that do not carry over to novel partners they have not been trained with. We introduce a novel learning algorithm called other-play (OP), that enhances self-play by looking for more robust strategies, exploiting the presence of known symmetries in the underlying problem. We characterize OP theoretically as well as experimentally. We study the cooperative card game Hanabi and show that OP agents achieve higher scores when paired with independently trained agents. In preliminary results we also show that our OP agents obtains higher average scores when paired with human players, compared to state-of-the-art SP agents.
Demographic Bias in Presentation Attack Detection of Iris Recognition Systems

With the widespread use of biometric systems, the demographic bias problem raises more attention. Although many studies addressed bias issues in biometric verification, there are no works that analyze the bias in presentation attack detection (PAD) decisions. Hence, we investigate and analyze the demographic bias in iris PAD algorithms in this paper. To enable a clear discussion, we adapt the notions of differential performance and differential outcome to the PAD problem. We study the bias in iris PAD using three baselines (hand-crafted, transfer-learning, and training from scratch) using the NDCLD-2013 [18] database. The experimental results point out that female users will be significantly less protected by the PAD, in comparison to males.
SERENIoT: Collaborative Network Security Policy Management and Enforcement for Smart Homes

Network traffic whitelisting has emerged as a dominant approach for securing consumer IoT devices. However, determining what the whitelisted behavior of an IoT device should be remains an open challenge. Proposals to date have relied on manufacturers and trusted parties to provide whitelists, but these proposals require manufacturer involvement or placing trust in an additional stakeholder. Alternatively, locally monitoring devices can allow building whitelists of observed behavior, but devices may not exhaust their functionality set during the observation period, or the behavior may change following a software update which requires re-training. This paper proposes a blockchain-based system for determining whether an IoT device is behaving like other devices of the same type. Our system (SERENIoT, pronounced Serenity) overcomes the challenge of initially determining the correct behavior for a device. Nodes in the SERENIoT public blockchain submit summaries of the network behavior observed for connected IoT devices and build whitelists of behavior observed by the majority of nodes. Changes in behavior through software updates are automatically whitelisted once the update is broadly deployed. Through a proof-of-concept implementation of SERENIoT on a small Raspberry Pi IoT network and a large-scale Amazon EC2 simulation, we evaluate the security, scalability, and performance of our system.
SAT0034 Nicotine Is A Potent Driver of Netosis and Accelerates Collagen-Induced Arthritis

Background Cigarette smoking is a major risk factor for rheumatoid arthritis (RA). The contribution of nicotine, the most ubiquitous and addictive component in the traditional as well as electronic cigarettes (e-cigarettes), to the development of RA remains unclear. Recent evidences implicate aberrant Neutrophil Extracellular Trap (NET) formation in the pathogenesis of RA. Objectives This study was undertaken to investigate the effect of nicotine on NETosis and its ability to potentiate arthritis in a murine model of RA. Methods Human neutrophils were incubated with nicotine in the presence of DNA-binding dye (Sytox) to quantify NETosis. In parallel, levels of NET-associated myeloperoxidase (MPO)/DNA complexes were measured using a novel NET-specific ELISA. Immunofluorescent staining was used to perform microscopic kinetic analysis of NETosis and to assess the presence of nicotinic acetylcholine receptors (nAChRs) in these neutrophils. NETosis were measured after stimulating neutrophils with GTS-21 (α7-specific nAChR agonist) or mecamylamine (an AChR antagonist). Finally, the effect of systemic nicotine exposure on arthritis development and progression was investigated using subcutaneous osmotic nicotine pumps in the collagen-induced arthritis (CIA) mouse model. Results Neutrophils derived from current smokers displayed significantly higher level of spontaneous- and PMA-induced NETosis as measured by Sytox assay and by NET-specific ELISA. In line with this, nicotine enhanced NETosis in a dose-dependent manner in vitro. Furthermore, incubation with APCA-immune complexs or TNF-α facilitated priming of neutrophils to undergo nicotine-induced NETosis. Immunofluorescent staining revealed that nicotine induced robust formation of NET-like structure in these neutrophils. Cell surface staining with an α7-specific agonist (α-bungarotoxin) and anti-α7-specfic antibody demonstrated co-localization on the plasma membrane. Nicotine-induced NETosis was reproduced when stimulated with GTS-21 and was abrogated when neutrophils were pretreated with mecamylamine. A significantly increased arthritis score as measured by paw swelling and thickness was observed in CIA mice treated with nicotine compared with vehicle treated controls. Conclusions Our data indicate that nicotine is a potent inducer of NETosis and may play an important role in the initiation and propagation of RA. Furthermore, our study provides molecular evidence for public awareness and clinical guidance against the use of nicotine-containing products including e-cigarettes in patients with RA. Disclosure of Interest None declared
Active Model Estimation in Markov Decision Processes

We study the problem of efficient exploration in order to learn an accurate model of an environment, modeled as a Markov decision process (MDP). Efficient exploration in this problem requires the agent to identify the regions in which estimating the model is more difficult and then exploit this knowledge to collect more samples there. In this paper, we formalize this problem, introduce the first algorithm to learn an $\epsilon$-accurate estimate of the dynamics, and provide its sample complexity analysis. While this algorithm enjoys strong guarantees in the large-sample regime, it tends to have a poor performance in early stages of exploration. To address this issue, we propose an algorithm that is based on maximum weighted entropy, a heuristic that stems from common sense and our theoretical analysis. The main idea here is to cover the entire state-action space with the weight proportional to the noise in the transitions. Using a number of simple domains with heterogeneous noise in their transitions, we show that our heuristic-based algorithm outperforms both our original algorithm and the maximum entropy algorithm in the small sample regime, while achieving similar asymptotic performance as that of the original algorithm.
Heteroscedasticity-Adjusted Ranking and Thresholding for Large-Scale Multiple Testing

Abstract Standardization has been a widely adopted practice in multiple testing, for it takes into account the variability in sampling and makes the test statistics comparable across different study units. However, despite conventional wisdom to the contrary, we show that there can be a significant loss in information from basing hypothesis tests on standardized statistics rather than the full data. We develop a new class of heteroscedasticity-adjusted ranking and thresholding (HART) rules that aim to improve existing methods by simultaneously exploiting commonalities and adjusting heterogeneities among the study units. The main idea of HART is to bypass standardization by directly incorporating both the summary statistic and its variance into the testing procedure. A key message is that the variance structure of the alternative distribution, which is subsumed under standardized statistics, is highly informative and can be exploited to achieve higher power. The proposed HART procedure is shown to be asymptotically valid and optimal for false discovery rate (FDR) control. Our simulation results demonstrate that HART achieves substantial power gain over existing methods at the same FDR level. We illustrate the implementation through a microarray analysis of myeloma.
Sensitive Data Detection and Classification in Spanish Clinical Text: Experiments with BERT

Massive digital data processing provides a wide range of opportunities and benefits, but at the cost of endangering personal data privacy. Anonymisation consists in removing or replacing sensitive information from data, enabling its exploitation for different purposes while preserving the privacy of individuals. Over the years, a lot of automatic anonymisation systems have been proposed; however, depending on the type of data, the target language or the availability of training documents, the task remains challenging still. The emergence of novel deep-learning models during the last two years has brought large improvements to the state of the art in the field of Natural Language Processing. These advancements have been most noticeably led by BERT, a model proposed by Google in 2018, and the shared language models pre-trained on millions of documents. In this paper, we use a BERT-based sequence labelling model to conduct a series of anonymisation experiments on several clinical datasets in Spanish. We also compare BERT with other algorithms. The experiments show that a simple BERT-based model with general-domain pre-training obtains highly competitive results without any domain specific feature engineering.
When Deep Learning Meets Data Alignment: A Review on Deep Registration Networks (DRNs)

This paper reviews recent deep learning-based registration methods. Registration is the process that computes the transformation that aligns datasets, and the accuracy of the result depends on multiple factors. The most significant factors are the size of input data; the presence of noise, outliers and occlusions; the quality of the extracted features; real-time requirements; and the type of transformation, especially those defined by multiple parameters, such as non-rigid deformations. Deep Registration Networks (DRNs) are those architectures trying to solve the alignment task using a learning algorithm. In this review, we classify these methods according to a proposed framework based on the traditional registration pipeline. This pipeline consists of four steps: target selection, feature extraction, feature matching, and transform computation for the alignment. This new paradigm introduces a higher-level understanding of registration, which makes explicit the challenging problems of traditional approaches. The main contribution of this work is to provide a comprehensive starting point to address registration problems from a learning-based perspective and to understand the new range of possibilities.
A P2P Context Lookup Service for Multiple Smart Spaces

Context information has emerged as an important resource to enable autonomy and flexibility of pervasive applications. The widespread use of context information necessitates efficient wide-area lookup services. In this paper, we present the design and implementation of a peer-to-peer context lookup system to support contextaware applications over multiple smart spaces. Our system provides a distributed repository for context storage, and a semantic peer-to-peer network for context lookup. Collaborative context-aware applications that utilize different context information in multiple smart spaces can be easily built by invoking a pull or push service provided by our system. We outline the design and implementation of our system, and validate our system through the development of cross-domain applications
Emergence of an upper bound to the electric field controlled Rashba spin splitting in InAs nanowires

The experimental assessment of the strength ($\alpha_R$) of the Rashba spin-orbit coupling is rather indirect and involves the measurement of the spin relaxation length from magnetotransport, together with a model of weak antilocalization. The analysis of the spin relaxation length in nanowires, however, clouds the experimental assessment of the $\alpha_R$ and leads to the prevailing belief that it can be tuned freely with electric field--a central tenant of spintronics. Here, we report direct theory of $\alpha_R$ leading to atomistic calculations of the spin band structure of InAs nanowires upon application of electric field-- a direct method that does not require a theory of spin relaxation. Surprisingly, we find an {\it upper bound} to the electric field tunable Rashba spin splitting and the ensuing $\alpha_R$; for InAs nanowires, $\alpha_R$ is pinned at about 170 meV{\AA} irrespective of the applied field strength. We find that this pinning is due to the quantum confined stark effect, that reduces continuously the nanowire band gap with applied electric field, leading eventually to band gap closure and a considerable increase in the density of free carriers. This results in turn in a strong screening that prevents the applied electric field inside the nanowire from increasing further beyond around 200 kV/cm for InAs nanowires. Therefore, further increase in the gate voltage will not increase $\alpha_R$. This finding clarifies the physical trends to be expected in nanowire Rashba SOC and the roles played by the nano size and electric field.
On the Role of Conceptualization in Commonsense Knowledge Graph Construction

Commonsense knowledge graphs (CKGs) like Atomic and ASER are substantially different from conventional KGs as they consist of much larger number of nodes formed by loosely-structured text, which, though, enables them to handle highly diverse queries in natural language related to commonsense, leads to unique challenges for automatic KG construction methods. Besides identifying relations absent from the KG between nodes, such methods are also expected to explore absent nodes represented by text, in which different real-world things, or entities, may appear. To deal with the innumerable entities involved with commonsense in the real world, we introduce to CKG construction methods conceptualization, i.e., to view entities mentioned in text as instances of specific concepts or vice versa. We build synthetic triples by conceptualization, and further formulate the task as triple classification, handled by a discriminatory model with knowledge transferred from pretrained language models and fine-tuned by negative sampling. Experiments demonstrate that our methods can effectively identify plausible triples and expand the KG by triples of both new nodes and edges of high diversity and novelty.