Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic Factors Associated with Longevity in Humans

Introduction: Life expectancy and the rate of survival into old age have risen dramatically throughout the past century. The positive ageing outcomes may be due to a variety of factors including healthy lifestyle behaviors, but it is clear that longevity has a genetic basis, with heritability estimate of 20–35%. In this contest, it was emerged that human longevity seems strongly influenced by gender defined as the combination between biological sexual characteristics and factors related to behavior, social role, lifestyle and life experiences. Body—research methods: Successful ageing seems to be related to gene involved in different pathways of regulation, such as immune-inflammatory responses and oxida tive stress. The aims of the present review are to discuss recent findings and highlight the genetic basis of longevity. For these reasons we are aimed to describe the most important underpinning which is the gender differences in longevity between males and females. Conclusion—key results: It appears clear that longevity may represent a complex poly-genic trait that is influenced by the interaction of multiple genetic variants, as was dem - onstrated by several genetic studies conducted in the last years. Furthermore, epigenetic and environmental factors actin on the longevity phenotype. Data deriving from different studies indicate that stress response pathway is an integrated network of molecular activities, ranging from immunity to inflammatory regulation and acti -vation, to glucose homeostasis and mitochondrial metabolism. Therefore, ageing seems to be associated with a loss of complexity in the dynamics of multiple control systems that may reduce the ability to adapt to stress, leading to a state of impaired homeostasis and vulnerabil-ity to internal and external stressors. On the contrary, the healthy oldest old seems be able to maintain a higher level of integration among the different physiological pathways operating within the cell, thus interacting more successfully with stressors.
Position paper: A systematic framework for categorising IoT device fingerprinting mechanisms

The popularity of the Internet of Things (IoT) devices makes it increasingly important to be able to fingerprint them, for example in order to detect if there are misbehaving or even malicious IoT devices in one's network. However, there are many challenges faced in the task of fingerprinting IoT devices, mainly due to the huge variety of the devices involved. At the same time, the task can potentially be improved by applying machine learning techniques for better accuracy and efficiency. The aim of this paper is to provide a systematic categorisation of machine learning augmented techniques that can be used for fingerprinting IoT devices. This can serve as a baseline for comparing various IoT fingerprinting mechanisms, so that network administrators can choose one or more mechanisms that are appropriate for monitoring and maintaining their network. We carried out an extensive literature review of existing papers on fingerprinting IoT devices -- paying close attention to those with machine learning features. This is followed by an extraction of important and comparable features among the mechanisms outlined in those papers. As a result, we came up with a key set of terminologies that are relevant both in the fingerprinting context and in the IoT domain. This enabled us to construct a framework called IDWork, which can be used for categorising existing IoT fingerprinting mechanisms in a way that will facilitate a coherent and fair comparison of these mechanisms. We found that the majority of the IoT fingerprinting mechanisms take a passive approach -- mainly through network sniffing -- instead of being intrusive and interactive with the device of interest. Additionally, a significant number of the surveyed mechanisms employ both static and dynamic approaches, in order to benefit from complementary features that can be more robust against certain attacks such as spoofing and replay attacks.
When Virtual Therapy and Art Meet: A Case Study of Creative Drawing Game in Virtual Environments

There have been a resurge lately on virtual therapy and other virtual- and tele-medicine services due to the new normal of practicing 'shelter at home'. In this paper, we propose a creative drawing game for virtual therapy and investigate user's comfort and movement freedom in a pilot study. In a mixed-design study, healthy participants (N=16, 8 females) completed one of the easy or hard trajectories of the virtual therapy game in standing and seated arrangements using a virtual-reality headset. The results from participants' movement accuracy, task completion time, and usability questionnaires indicate that participants had significant performance differences on two levels of the game based on its difficulty (between-subjects factor), but no difference in seated and standing configurations (within-subjects factor). Also, the hard mode was more favorable among participants. This work offers implications on virtual reality and 3D-interactive systems, with specific contributions to virtual therapy, and serious games for healthcare applications.
Lexicon-constrained Copying Network for Chinese Abstractive Summarization

Copy mechanism allows sequence-to-sequence models to choose words from the input and put them directly into the output, which is finding increasing use in abstractive summarization. However, since there is no explicit delimiter in Chinese sentences, most existing models for Chinese abstractive summarization can only perform character copy, resulting in inefficient. To solve this problem, we propose a lexicon-constrained copying network that models multi-granularity in both encoder and decoder. On the source side, words and characters are aggregated into the same input memory using a Transformerbased encoder. On the target side, the decoder can copy either a character or a multi-character word at each time step, and the decoding process is guided by a word-enhanced search algorithm that facilitates the parallel computation and encourages the model to copy more words. Moreover, we adopt a word selector to integrate keyword information. Experiments results on a Chinese social media dataset show that our model can work standalone or with the word selector. Both forms can outperform previous character-based models and achieve competitive performances.
For self-supervised learning, Rationality implies generalization, provably

We prove a new upper bound on the generalization gap of classifiers that are obtained by first using self-supervision to learn a representation $r$ of the training data, and then fitting a simple (e.g., linear) classifier $g$ to the labels. Specifically, we show that (under the assumptions described below) the generalization gap of such classifiers tends to zero if $\mathsf{C}(g) \ll n$, where $\mathsf{C}(g)$ is an appropriately-defined measure of the simple classifier $g$'s complexity, and $n$ is the number of training samples. We stress that our bound is independent of the complexity of the representation $r$. We do not make any structural or conditional-independence assumptions on the representation-learning task, which can use the same training dataset that is later used for classification. Rather, we assume that the training procedure satisfies certain natural noise-robustness (adding small amount of label noise causes small degradation in performance) and rationality (getting the wrong label is not better than getting no label at all) conditions that widely hold across many standard architectures. We show that our bound is non-vacuous for many popular representation-learning based classifiers on CIFAR-10 and ImageNet, including SimCLR, AMDIM and MoCo.
Migration and brackish environment use of Prochilodus lineatus (Characiformes: Prochilodontidae) inferred by Sr:Ca ratio transects of otolith

otolith sections were analyzed for Sr:Ca with laser ablation-inductively coupled plasma-mass spectrometry (LA ICP-MS) to infer habitat use of fish. To the interpretation of transects, a threshold that represents the transition between freshwater and brackish environments was calculated using the Sr:Ca ratio of the otolith edge of specimens captured in the first section of the La Plata Estuary (salinity ≥ 0.5 PSU). The percentage of fish using the estuary was higher in the Parana (37%) than the Uruguay River (5%). Change-point analysis showed that fish entered the estuary between 1 and 3 times throughout life at a wide range of ages (0-15 years). These incursions had no obvious periodicity. This information should be integrated into future management actions, which should also be specific to each area since migration patterns differ between the major rivers of the basin.
Cu(II) pollution affects fecundity of the mangrove degrader community, the Labyrinthulomycetes

Abstract Thraustochytrids, a group of fungus-like organisms belonging to the protist class Labyrinthulomycetes, are common colonisers and degraders of fallen leaves in mangroves and, thus, are actively involved in nutrient cycling. Mangroves, often located at the river mouth, constantly receive freshwater runoff, which contains organic and inorganic pollutants, including metals. Metals are known to cause cellular damage, which may affect the survival of thraustochytrids in the mangrove environment. A previous study suggested that Cu(II), one of the major metal ions in coastal water and sediment, retards the growth of and causes cellular damage to mangrove thraustochytrids. We hypothesize that increased concentrations of Cu(II) negatively affect the fecundity of mangrove thraustochytrids. In a laboratory study, we assessed the sporulation success (number of zoospores produced per colony) and the growth response (biomass) of 11 isolates of Schizochytrium limacinum collected from four mangrove stands in Taiwan exposed to increasing concentrations of Cu(II). Tolerance to Cu(II) varied among the tested isolates of S. limacinum. In general, a negative dose-response relationship was exhibited between growth response/sporulation success and increasing concentrations of Cu(II). However, exposure to low concentrations of Cu(II) had a stimulating effect on growth (2 mg l-1) and sporulation (2–64 mg l-1) for some isolates. A sharp decline in growth was observed at 32 mg l-1 Cu(II), and sporulation success was more tolerant to increasing concentrations of Cu(II). The IC10 and IC50 values for growth were 1.0–16.5 mg l-1 and 9.1–23.9 mg l-1, respectively, whereas those for sporulation success were 0.6–40.7 mg l-1 and 10.5–108.4 mg l-1, respectively. In conclusion, Cu(II) interfered with both the growth and sporulation success of S. limacinum, which may affect its abundance and distribution in mangrove environments.
Self-consistent calculation of localized orbital scaling correction for correct electron densities and energy level alignment in density functional theory

The recently developed localized orbital scaling correction (LOSC) method shows the ability to systematically and size-consistently reduce the delocalization error existing in conventional density functional approximations (DFAs). Applying LOSC to conventional DFAs (LOSC-DFAs) gives much improvement for the description of related properties, including band gaps, total energies and photoemission spectra. However, concern and issue remain: the application of LOSC to DFAs is mainly through a post self-consistent field (SCF) manner, and few results from applying LOSC to DFAs with a SCF manner have been reported. The reason is the originally proposed SCF approach for SCF-LOSC calculation uses an approximate Hamiltonian and encounters convergence problems easily in practice. In this work, we develop a new SCF approach with a correct Hamiltonian and achieve reliable SCF-LOSC calculations. We demonstrate the capability of the new SCF approach for SCF-LOSC to correctly describe the electron densities, total energies and energy level alignment for the molecular dissociation process, while conventional DFAs or LOSC-DFAs with post-SCF calculations show large errors. This work demonstrates that the new SCF approach for SCF-LOSC would be a promising method to study problems for correct electron densities and energy level alignment in large systems.
Consistent Feature Selection for Analytic Deep Neural Networks

One of the most important steps toward interpretability and explainability of neural network models is feature selection, which aims to identify the subset of relevant features. Theoretical results in the field have mostly focused on the prediction aspect of the problem with virtually no work on feature selection consistency for deep neural networks due to the model's severe nonlinearity and unidentifiability. This lack of theoretical foundation casts doubt on the applicability of deep learning to contexts where correct interpretations of the features play a central role. In this work, we investigate the problem of feature selection for analytic deep networks. We prove that for a wide class of networks, including deep feed-forward neural networks, convolutional neural networks, and a major sub-class of residual neural networks, the Adaptive Group Lasso selection procedure with Group Lasso as the base estimator is selection-consistent. The work provides further evidence that Group Lasso might be inefficient for feature selection with neural networks and advocates the use of Adaptive Group Lasso over the popular Group Lasso.
Mischief: A Simple Black-Box Attack Against Transformer Architectures

We introduce Mischief, a simple and lightweight method to produce a class of human-readable, realistic adversarial examples for language models. We perform exhaustive experimentations of our algorithm on four transformer-based architectures, across a variety of downstream tasks, as well as under varying concentrations of said examples. Our findings show that the presence of Mischief-generated adversarial samples in the test set significantly degrades (by up to $20\%$) the performance of these models with respect to their reported baselines. Nonetheless, we also demonstrate that, by including similar examples in the training set, it is possible to restore the baseline scores on the adversarial test set. Moreover, for certain tasks, the models trained with Mischief set show a modest increase on performance with respect to their original, non-adversarial baseline.
Adaptive Feature Selection for End-to-End Speech Translation

Information in speech signals is not evenly distributed, making it an additional challenge for end-to-end (E2E) speech translation (ST) to learn to focus on informative features. In this paper, we propose adaptive feature selection (AFS) for encoder-decoder based E2E ST. We first pre-train an ASR encoder and apply AFS to dynamically estimate the importance of each encoded speech feature to ASR. A ST encoder, stacked on top of the ASR encoder, then receives the filtered features from the (frozen) ASR encoder. We take L0DROP (Zhang et al., 2020) as the backbone for AFS, and adapt it to sparsify speech features with respect to both temporal and feature dimensions. Results on LibriSpeech EnFr and MuST-C benchmarks show that AFS facilitates learning of ST by pruning out ~84% temporal features, yielding an average translation gain of ~1.3-1.6 BLEU and a decoding speedup of ~1.4x. In particular, AFS reduces the performance gap compared to the cascade baseline, and outperforms it on LibriSpeech En-Fr with a BLEU score of 18.56 (without data augmentation).
On Automatic Feasibility Study for Machine Learning Application Development with ease.ml/snoopy

In our experience working with domain experts who are using today's AutoML systems, a common problem we encountered is what we call Unrealistic Expectation: When users have access to very noisy or challenging datasets, whilst being expected to achieve startlingly high accuracy with ML. Consequently, many computationally expensive AutoML runs and labour-intensive ML development processes are predestined to fail from the beginning. In traditional software engineering, this problem is addressed via a feasibility study, an indispensable step before developing any software system. In this paper we present this http URL with the goal of preforming an automatic feasibility study before building ML applications. A user provides inputs in the form of a dataset and a quality target (e.g., expected accuracy $>$ 0.8) and the system returns its deduction on whether this target is achievable using ML given the input data. We formulate this problem as estimating the irreducible error of the underlying task, also known as the Bayes error. The key contribution of this work is the study of this problem from a system's and empirical perspective -- we (1) propose practical "compromises" that enable the application of Bayes error estimators and (2) develop an evaluation framework that compares different estimators empirically on real-world data. We then systematically explore the design space by evaluating a range of estimators, reporting not only the improvements of our proposed estimator but also limitations of both our method and existing estimators.
General Semantic Construction of Dependent Refinement Type Systems, Categorically

Refinement types are types equipped with predicates that specify preconditions and postconditions of underlying functional languages. We propose a general semantic construction of dependent refinement type systems from underlying type systems and predicate logic, that is, a construction of liftings of closed comprehension categories from given (underlying) closed comprehension categories and posetal fibrations for predicate logic. We give sufficient conditions to lift structures such as dependent products, dependent sums, computational effects, and recursion from the underlying type systems to refinement type systems. We demonstrate the usage of our construction by giving semantics to a refinement type system and proving soundness.
Orbital MCMC

Markov Chain Monte Carlo (MCMC) is a computational approach to fundamental problems such as inference, integration, optimization, and simulation. Recently, the framework (Involutive MCMC) was proposed describing a large body of MCMC algorithms via two components: a stochastic acceptance test and an involutive deterministic function. This paper demonstrates that this framework is a special case of a larger family of algorithms operating on orbits of continuous deterministic bijections. We describe this family by deriving a novel MCMC kernel, which we call orbital MCMC (oMCMC). We provide a theoretical analysis and illustrate its utility using simple examples.
Chirality induced Giant Unidirectional Magnetoresistance in the Twisted Bilayers

The twisted bilayer graphene (TBG) exhibits a chiral atomic structure and flat electronic bands. We find that the chirality leads to the giant unidirectional magnetoresistance (UMR) in TBG by quantum transport calculations. The UMR refers to the resistance change as reversing the direction of the current or the magnetic field. We point out that flat bands significantly enhance the UMR effect. The UMR increases quickly as reducing the twisted angle. It reaches about 20\% for the angle 1.5$^\circ$ in a 10 T in-plane magnetic field. We propose the band structure topology (asymmetry), which leads to direction-sensitive mean free path, as a useful way to anticipate the UMR effect. The UMR provides a probe for the chirality and symmetry-breaking in the twisted bilayers.
Partial discharge direction‐of‐arrival estimation in air‐insulated substation by UHF wireless array and RSSI maximum likelihood estimator

The quick detection and localization of partial discharge (PD) in an air-insulated substation (AIS) based on ultrahigh-frequency (UHF) sensor arrays are efficient for power equipment monitoring. The adopted UHF PD time difference of arrival (TDOA) methods mainly use the time difference of electromagnetic wave signals. Thus, the system requires both a high sampling rate and time synchronization accuracy, leading to a high cost and large size. In this study, the feasibility and accuracy of PD DOA in an AIS were investigated using a UHF wireless sensor array and the received signal strength indicator. First, the power pattern of the designed UHF wireless sensor array was obtained via an offline experiment. Then, a statistical approach to the PD DOA method based on the maximum likelihood estimator was employed to obtain the preliminary DOA result. Finally, interpolation and clustering algorithms were used to improve the accuracy of the DOA. A laboratory test was conducted. The average error of the PD DOA was less than 6°, and the cost-effectiveness and portability were clearly improved.
Measuring the Dynamic Impact of High-Speed Railways on Urban Interactions in China

High-speed rail (HSR) has become an important mode of inter-city transportation between large cities. Inter-city interaction facilitated by HSR tends to play a more prominent role in promoting urban and regional economic integration and development. Quantifying the impact of HSR's interaction on cities and people is therefore crucial for long-term urban and regional development planning and policy making. We develop an evaluation framework using toponym information from social media as a proxy to estimate the dynamics of such interactions. This paper adopts two types of spatial information: toponyms from social media posts, and the geographical location information embedded in social media posts. The framework highlights the asymmetric nature of social interaction among cities, and proposes a series of metrics to quantify such impact from multiple perspectives, including interaction strength, spatial decay, and channel effect. The results show that HSRs not only greatly expand the uneven distribution of inter-city connections, but also significantly reshape the interactions that occur along HSR routes through the channel effect.
A Combinatorial Description of the Knot Concordance Invariant Epsilon

In this paper, we give a combinatorial description of the concordance invariant $\varepsilon$ defined by Hom in \cite{hom2011knot}, prove some properties of this invariant using grid homology techniques. We also compute $\varepsilon$ of $(p,q)$ torus knots and prove that $\varepsilon(\mathbb{G}_+)=1$ if $\mathbb{G}_+$ is a grid diagram for a positive braid. Furthermore, we show how $\varepsilon$ behaves under $(p,q)$-cabling of negative torus knots.
Data Analytics-enabled Intrusion Detection: Evaluations of ToN IoT Linux Datasets

With the widespread of Artificial Intelligence (AI)-enabled security applications, there is a need for collecting heterogeneous and scalable data sources for effectively evaluating the performances of security applications. This paper presents the description of new datasets, named ToN_IoT datasets that include distributed data sources collected from Telemetry datasets of Internet of Things (IoT) services, Operating systems datasets of Windows and Linux, and datasets of Network traffic. The paper aims to describe the new testbed architecture used to collect Linux datasets from audit traces of hard disk, memory and process. The architecture was designed in three distributed layers of edge, fog, and cloud. The edge layer comprises IoT and network systems, the fog layer includes virtual machines and gateways, and the cloud layer includes data analytics and visualization tools connected with the other two layers. The layers were programmatically controlled using Software-Defined Network (SDN) and Network-Function Virtualization (NFV) using the VMware NSX and vCloud NFV platform. The Linux ToN_IoT datasets would be used to train and validate various new federated and distributed AI-enabled security solutions such as intrusion detection, threat intelligence, privacy preservation and digital forensics. Various Data analytical and machine learning methods are employed to determine the fidelity of the datasets in terms of examining feature engineering, statistics of legitimate and security events, and reliability of security events. The datasets can be publicly accessed from [1].