Miracle-dz/UniFAR-FTUs huggingface dataset:
Bitcoin Trading is Irrational! An Analysis of the Disposition Effect in Bitcoin

Investors tend to sell their winning investments and hold onto their losers. This phenomenon, known as the \emph{disposition effect} in the field of behavioural finance, is well-known and its prevalence has been shown in a number of existing markets. But what about new atypical markets like cryptocurrencies? Do investors act as irrationally as in traditional markets? One might suspect this and hypothesise that cryptocurrency sells occur more frequently in positive market conditions and less frequently in negative market conditions. However, there is still no empirical evidence to support this. In this paper, we expand on existing research and empirically investigate the prevalence of the disposition effect in Bitcoin by testing this hypothesis. Our results show that investors are indeed subject to the disposition effect, tending to sell their winning positions too soon and holding on to their losing position for too long. This effect is very prominently evident from the boom and bust year 2017 onwards, confirmed via most of the applied technical indicators. In this study, we show that Bitcoin traders act just as irrationally as traders in other, more established markets.
Zoom on the Keystrokes: Exploiting Video Calls for Keystroke Inference Attacks

Due to recent world events, video calls have become the new norm for both personal and professional remote communication. However, if a participant in a video call is not careful, he/she can reveal his/her private information to others in the call. In this paper, we design and evaluate an attack framework to infer one type of such private information from the video stream of a call -- keystrokes, i.e., text typed during the call. We evaluate our video-based keystroke inference framework using different experimental settings and parameters, including different webcams, video resolutions, keyboards, clothing, and backgrounds. Our relatively high keystroke inference accuracies under commonly occurring and realistic settings highlight the need for awareness and countermeasures against such attacks. Consequently, we also propose and evaluate effective mitigation techniques that can automatically protect users when they type during a video call.
On the subalgebra lattice of a Leibniz algebra

Abstract In this paper, we begin to study the subalgebra lattice of a Leibniz algebra. In particular, we deal with Leibniz algebras whose subalgebra lattice is modular, upper semi-modular, lower semi-modular, distributive, or dually atomistic. The fact that a non-Lie Leibniz algebra has fewer one-dimensional subalgebras in general results in a number of lattice conditions being weaker than in the Lie case.
Reducing Bias in Modeling Real-world Password Strength via Deep Learning and Dynamic Dictionaries

Password security hinges on an accurate understanding of the techniques adopted by attackers. However, current studies mostly rely on probabilistic password models that are imperfect proxies of real-world guessing strategies. The main reason is that attackers rely on very pragmatic approaches such as dictionary attacks. Unfortunately, it is inherently difficult to correctly model those methods. To be representative, dictionary attacks must be thoughtfully configured according to a process that requires an expertise that cannot be easily replicated in password studies. The consequence of inaccurately calibrating those attacks is the unreliability of password security estimates, impaired by measurement bias. In the present work, we introduce new guessing techniques that make dictionary attacks consistently more resilient to inadequate configurations. Our framework allows dictionary attacks to self-heal and converge towards optimal attacks' performance, requiring no supervision or domain-knowledge. To achieve this: (1) We use a deep neural network to model and then simulate the proficiency of expert adversaries. (2) Then, we introduce automatic dynamic strategies within dictionary attacks to mimic experts' ability to adapt their guessing strategies on the fly by incorporating knowledge on their targets. Our techniques enable robust and sound password strength estimates, eventually reducing bias in modeling real-world threats in password security.
A Differentially Private Text Perturbation Method Using Regularized Mahalanobis Metric

Balancing the privacy-utility tradeoff is a crucial requirement of many practical machine learning systems that deal with sensitive customer data. A popular approach for privacy- preserving text analysis is noise injection, in which text data is first mapped into a continuous embedding space, perturbed by sampling a spherical noise from an appropriate distribution, and then projected back to the discrete vocabulary space. While this allows the perturbation to admit the required metric differential privacy, often the utility of downstream tasks modeled on this perturbed data is low because the spherical noise does not account for the variability in the density around different words in the embedding space. In particular, words in a sparse region are likely unchanged even when the noise scale is large. In this paper, we propose a text perturbation mechanism based on a carefully designed regularized variant of the Mahalanobis metric to overcome this problem. For any given noise scale, this metric adds an elliptical noise to account for the covariance structure in the embedding space. This heterogeneity in the noise scale along different directions helps ensure that the words in the sparse region have sufficient likelihood of replacement without sacrificing the overall utility. We provide a text-perturbation algorithm based on this metric and formally prove its privacy guarantees. Additionally, we empirically show that our mechanism improves the privacy statistics to achieve the same level of utility as compared to the state-of-the-art Laplace mechanism.
Love Thy Neighbour? Perceived Community Abidance and Private Compliance to COVID-19 Norms in India

To arrest the spread of COVID-19 infection, strict adherence to frequent hand washing and respiratory hygiene protocols have been recommended. While these measures involve private effort, they provide health gains along with collective community benefits and hence are likely to be driven by pro-social motives like altruism and reciprocity. Using data from 934 respondents collected from April till May 2020 across India, we assess if changes in perceived community compliance can predict changes in individual compliance behaviour. We observe statistically significant and positive relationship between the two, even after accounting for observable and omitted variable bias allowing us to view the results from a plausible causal lens. Further, we find subsequent lockdowns having a detrimental effect on individual compliance though the gains from higher perceived community compliance seem to offset this loss. We also find positive perceptions about community can be particularly effective for people with pre-existing co-morbidities. Our findings underscore the need for multi-level behavioural interventions involving local actors and community institutions to sustain private compliance during the pandemic. We suggest these interventions need to be specially targeted for individuals with chronic ailments and emphasize on community behavioural practices in public messaging. JEL Codes: I12, I18, I19, I31
Coping with Label Shift via Distributionally Robust Optimisation

The label shift problem refers to the supervised learning setting where the train and test label distributions do not match. Existing work addressing label shift usually assumes access to an \emph{unlabelled} test sample. This sample may be used to estimate the test label distribution, and to then train a suitably re-weighted classifier. While approaches using this idea have proven effective, their scope is limited as it is not always feasible to access the target domain; further, they require repeated retraining if the model is to be deployed in \emph{multiple} test environments. Can one instead learn a \emph{single} classifier that is robust to arbitrary label shifts from a broad family? In this paper, we answer this question by proposing a model that minimises an objective based on distributionally robust optimisation (DRO). We then design and analyse a gradient descent-proximal mirror ascent algorithm tailored for large-scale problems to optimise the proposed objective. %, and establish its convergence. Finally, through experiments on CIFAR-100 and ImageNet, we show that our technique can significantly improve performance over a number of baselines in settings where label shift is present.
Unsupervised Domain Adaptation without Source Data by Casting a BAIT

Unsupervised domain adaptation (UDA) aims to transfer the knowledge learned from labeled source domain to unlabeled target domain. Existing UDA methods require access to the data from the source domain, during adaptation to the target domain, which may not be feasible in some real-world situations. In this paper, we address Source-free Unsupervised Domain Adaptation (SFUDA), where the model has no access to any source data during the adaptation period. We propose a novel framework named BAIT to tackle SFUDA. Specifically, we first train the model on source domain. With the source-specific classifier head (referred to as anchor classifier) fixed, we further introduce a new learnable classifier head (referred to as bait classifier), which is initialized by the anchor classifier. When adapting the source model to the target domain, the source data are no more accessible and the bait classifier aims to push the target features towards the right side of the decision boundary of the anchor classifier, thus achieving the feature alignment. Experiment results show that proposed BAIT achieves state-of-the-art performance compared with existing normal UDA methods and several SFUDA methods.
Wind Dispersal of Natural and Biomimetic Maple Samaras

Maple trees (genus Acer) accomplish the task of distributing objects to a wide area by producing seeds, known as samaras, which are carried by the wind as they autorotate and slowly descend to the ground. With the goal of supporting engineering applications, such as gathering environmental data over a broad area, we developed 3D-printed artificial samaras. Here, we compare the behavior of both natural and artificial samaras in both still-air laboratory experiments and wind dispersal experiments in the field. We show that the artificial samaras are able to replicate (within one standard deviation) the behavior of natural samaras in a lab setting. We further use the notion of windage to compare dispersal behavior, and show that the natural samara has the highest mean windage, corresponding to the longest flights during both high wind and low wind experimental trials. This study demonstrated a bioinspired design for the dispersed deployment of sensors and provides a better understanding of wind-dispersal of both natural and artificial samaras.
Random hyperbolic graphs in $d+1$ dimensions

We generalize random hyperbolic graphs to arbitrary dimensionality. We find the rescaling of network parameters that allows to reduce random hyperbolic graphs of arbitrary dimensionality to a single mathematical framework. Our results indicate that RHGs exhibit similar topological properties, regardless of the dimensionality of their latent hyperbolic spaces.
Susceptible host availability modulates climate effects on dengue dynamics

Experiments and models suggest that climate affects mosquito-borne disease transmission. However, disease transmission involves complex nonlinear interactions between climate and population dynamics, which makes detecting climate drivers at the population level challenging. By analyzing incidence data, estimated susceptible population size, and climate data with methods based on nonlinear time series analysis (collectively referred to as empirical dynamic modeling), we identified drivers and their interactive effects on dengue dynamics in San Juan, Puerto Rico. Climatic forcing arose only when susceptible availability was high: temperature and rainfall had net positive and negative effects, respectively. By capturing mechanistic, nonlinear, and context-dependent effects of population susceptibility, temperature, and rainfall on dengue transmission empirically, our model improves forecast skill over recent, state-of-the-art models for dengue incidence. Together, these results provide empirical evidence that the interdependence of host population susceptibility and climate drive dengue dynamics in a nonlinear and complex, yet predictable way.
Surface electrostatics govern the emulsion stability of biomolecular condensates

Liquid–liquid phase separation underlies the formation of biological condensates. Physically, such systems are microemulsions which have a general propensity to fuse and coalesce; however, many condensates persist as independent droplets inside cells. This stability is crucial for their functioning, but the physicochemical mechanisms that control the emulsion stability of condensates remain poorly understood. Here, by combining single-condensate zeta potential measurements, optical microscopy, tweezer experiments, and multiscale molecular modelling, we investigate how the forces that sustain condensates impact their stability against fusion. By comparing PR25:PolyU and FUS condensates, we show that a higher condensate surface charge correlates with a lower fusion propensity, and that this behavior can be inferred from their zeta potentials. We reveal that overall stabilization against fusion stems from a combination of repulsive forces between condensates and the effects that surface electrostatics have on lowering surface tension, thus shedding light on the molecular determinants of condensate coalescence.
Atom-by-atom analysis of sintering dynamics and stability of Pt nanoparticle catalysts in chemical reactions

Supported Pt nanoparticles are used extensively in chemical processes, including for fuel cells, fuels, pollution control and hydrogenation reactions. Atomic-level deactivation mechanisms play a critical role in the loss of performance. In this original research paper, we introduce real-time in-situ visualization and quantitative analysis of dynamic atom-by-atom sintering and stability of model Pt nanoparticles on a carbon support, under controlled chemical reaction conditions of temperature and continuously flowing gas. We use a novel environmental scanning transmission electron microscope with single-atom resolution, to understand the mechanisms. Our results track the areal density of dynamic single atoms on the support between nanoparticles and attached to them; both as migrating species in performance degradation and as potential new independent active species. We demonstrate that the decay of smaller nanoparticles is initiated by a local lack of single atoms; while a post decay increase in single-atom density suggests anchoring sites on the substrate before aggregation to larger particles. The analyses reveal a relationship between the density and mobility of single atoms, particle sizes and their nature in the immediate neighbourhood. The results are combined with practical catalysts important in technological processes. The findings illustrate the complex nature of sintering and deactivation. They are used to generate new fundamental insights into nanoparticle sintering dynamics at the single-atom level, important in the development of efficient supported nanoparticle systems for improved chemical processes and novel single-atom catalysis. This article is part of a discussion meeting issue ‘Dynamic in situ microscopy relating structure and function’.
Psychometric Properties of Achievement Goal Constructs for Chinese Students

In Chinese schools, classes are organized with special monitors and teachers contributing to the achievement goal structure for students. This study aimed to examine the psychometric properties of perception of teachers’ achievement goal structure constructs with 3,149 Chinese students from grades 3–8. The results showed that the internal consistencies of the whole scale and subscales were low to marginal. Eight models were examined to check the constructs of the achievement goal structure (mastery, performance, and performance avoidance). Two-factor structures proved to be the best fit. Additionally, a multilevel confirmatory factor analysis proved that the achievement goal structure existed at the same time in the class student levels. Our findings supported the hypothesis that achievement goal structures are different for students with different cultures, which implies that teaching approaches should be adapted in consideration of culturally distinct learning.
Ocular vs. Cervical Vestibular Evoked Myogenic Potentials in Benign Paroxysmal Positional Vertigo: A Systematic Review and Meta-Analysis

Objective: To compare utricular dysfunction with saccular dysfunction in benign paroxysmal positional vertigo (BPPV), based on ocular vestibular evoked myogenic potentials (oVEMP) and cervical VEMP (cVEMP), respectively. Materials and Methods: We performed a literature search exploring utricular and saccular dysfunction in BPPV patients through June 2020 using oVEMP and cVEMP, respectively. The databases included Pubmed, Embase, CENTRAL, CNKI, Wan Fang Data, and CBM. The literatures were limited to Chinese and English. Inclusion criteria and exclusion criteria were defined. We adopted abnormal rate as the outcome. All statistical processes were conducted through software Review Manager. Considering the air-conducted sound (ACS) and bone conducted vibration (BCV) may have different mechanisms, and three types of diagnostic criteria for abnormal VEMP were available, sub-group analysis was performed simultaneously according to the sound stimuli and the diagnostic criteria of abnormal VEMP. Results: We retrieved 828 potentially relevant literatures, and finally 12 studies were included for meta-analysis of abnormal rate after duplication removal, titles and abstracts screening, and full-text reading. The abnormal rate of oVEMP was not significantly different from cVEMP (OR = 1.59, 95% CI = 0.99–2.57). But the abnormal rate was obviously different between the subgroups adopting ACS oVEMP and BCV oVEMP. In studies adopting ACS oVEMP, the abnormal rate of oVEMP was higher than cVEMP (OR = 1.85, 95% CI = 1.38–2.49). The abnormal rate of oVEMP was also higher than cVEMP when adopting asymmetry ratio (AR) and no response (NR) as diagnostic criteria (OR = 2.16, 95% CI = 1.61–2.89). Conclusion: The meta-analysis reveals that utricular dysfunction may be more predominant in BPPV compared with saccular dysfunction.
The Two Faces of Janus: Why Thyrotropin as a Cardiovascular Risk Factor May Be an Ambiguous Target

Elevated concentrations of free thyroid hormones are established cardiovascular risk factors, but the association of thyrotropin (TSH) levels to hard endpoints is less clear. This may, at least in part, ensue from the fact that TSH secretion depends not only on the supply with thyroid hormones but on multiple confounders including genetic traits, medication and allostatic load. Especially psychosocial stress is a still underappreciated factor that is able to adjust the set point of thyroid function. In order to improve our understanding of thyroid allostasis, we undertook a systematic meta-analysis of published studies on thyroid function in post-traumatic stress disorder (PTSD). Studies were identified via MEDLINE/PubMed search and available references, and eligible were reports that included TSH or free thyroid hormone measurements in subjects with and without PTSD. Additionally, we re-analyzed data from the NHANES 2007/2008 cohort for a potential correlation of allostatic load and thyroid homeostasis. The available evidence from 13 included studies and 3386 euthyroid subjects supports a strong association of both PTSD and allostatic load to markers of thyroid function. Therefore, psychosocial stress may contribute to cardiovascular risk via an increased set point of thyroid homeostasis, so that TSH concentrations may be increased for reasons other than subclinical hypothyroidism. This provides a strong perspective for a previously understudied psychoendocrine axis, and future studies should address this connection by incorporating indices of allostatic load, peripheral thyroid hormones and calculated parameters of thyroid homeostasis.
Right Compared With Left Thoracic Approach Esophagectomy for Patients With Middle Esophageal Squamous Cell Carcinoma

Background: In China, open surgical approaches for esophageal cancer (EC) can be divided into two techniques, the right- and left- transthoracic esophagectomy. Although there is an increasing number of instances that use the right side, the optimal surgical technique remains unclear. Based in a large cancer center with rich experience of both transthoracic side approaches, this study compared the long-term survival of patients treated by these two surgical techniques. Methods: The patients included in this study underwent a right transthoracic esophagectomy (Right, McKeown) or left transthoracic esophagectomy (Left, Sweet, or chest neck dual-incision) for esophageal squamous cell carcinoma (ESCC) between January 2015 and October 2018. The overall survival(OS) rate and perioperative data between the two groups were then retrospectively analyzed. Results: We included 437 patients who underwent Right (n = 202) and Left (n = 235) approaches for ESCC. There was a significantly longer median operative time (250 vs. 190 min, P < 0.001) and longer median postoperative hospital stay (17 vs. 14 days, P < 0.001) in the Right side group. The OS at 5-years was 49.9% in the Right group and 52.45% in the Left group; hazard ratio (HR) (95% CI): 1.002 (0.752–1.337), p = 0.987. Conclusions: For middle thoracic ESCC without suspected lymph node metastasis in the upper mediastinum, the esophagectomy through the Left thoracic approach could achieve the same OS as the Right side, with better short-term outcomes.
The Distance Between the “Self” and the “Other” in Children’s Digital Books

This conceptual paper contributes toward our understanding of the underlying mechanisms in children’s understanding of self and the other with media. We synthesize diverse bodies of literature, concerned with children’s reading with digital and traditional (print) books, to explicate the parameters that may, in part, explain positive learning outcomes and further illuminate the patterns across various measures. We propose the “Distance Model,” which suggests that a child’s interest in a reading activity depends on its proximity to the child’s funds of identity (Esteban-Guitart and Moll, 2014). The closer the proximity, the more salient the impact on the child’s cognitive understanding and sense of belonging. The familiarity of the reading content and the relevance of the reading medium for a child’s personal life can be evoked through a number of reading strategies and design techniques, which we discuss in relation to children’s literature and the contemporary design of children’s interactive e-books. We conclude with some suggestions regarding future applications of the Distance Model in children’s media research.