Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of sensory dominance and gender differences on impaired emotion perception in schizophrenic patients

Patients with schizophrenia have been repeatedly reported to show dysfunctions in emotion perception. However, it remains unclear how contextual (e.g., communication channels) and individual (e.g., gender differences) factors interact to modulate their perceptual performances. The present study examined how emotions conveyed through three different sensory channels (i.e., face, prosody, and semantics) were perceived by schizophrenic patients and healthy controls of the two genders. Fourteen (seven women and seven men) schizophrenic patients and sixteen healthy controls (eight women and eight men) were asked to identify the emotion displayed visually through facial expressions, or auditorily through prosody or semantics in a fixed-choice format. The integration of accuracy and reaction time data revealed that schizophrenic patients showed impaired emotion perception compared to healthy controls. For both patients and controls, emotional faces gained most perceptual salience among the three communicative channels, but patients were more biased towards emotional semantics whereas controls were more dominated by emotional prosody. Additionally, the preserved abilities in semantic processing were more pronounced for male patients compared with their female counterparts. To sum up, schizophrenic patients demonstrate impaired nonverbal emotion perception irrespective of perceivers’ gender, while their intact abilities in verbal emotion perception are restrictive to males.
Eﬃcient Markets Hypothesis in Canada: a comparative study between Islamic and Conventional stock markets

In this paper we test the weak form of the Efficient-Market Hypothesis (EMH) using monthly data from 2004M08 to 2018M04 of stock prices by using linear and nonlinear (KSS 3 type, Sollis and Kruse) unit root tests. The informational market efficiency is examined in the Islamic and conventional markets in Canada. It aims to investigate whether Islamic market would be more or less efficient than the conventional one. Findings indicate that both Conventional Canadian Stock Index (CCSI) and Dow Jones Islamic Canadian Price Index (DJICPI) show characteristics of random walk indicating that the stock markets are efficient. The major policy implications is that in this country ( Canada ), fund managers and investors cannot enjoy excess returns to their investment.
A Two-stage Segmentation Neural Network for MICCAI FLARE22 Challenge

Abdominal organ segmentation plays an important role in medical image processing. In this work, our goal is to segment thirteen organs of the abdomen in a semi-supervised way. We apply the attention block to the DMFNet, and propose a new Attention DMFNet for medical imaging, which can automatically learn and focus target structures of diﬀerent shapes and sizes. The DMFNet is a highly eﬃcient 3D CNN, which can realize real-time dense volume segmentation. It uses 3D multi ﬁber units composed of lightweight 3D convolution network to sig-niﬁcantly reduce the computational cost. Models trained with attention blocks implicitly learn to suppress irrelevant regions in an input image while highlighting salient features useful for a speciﬁc task. Integrating attention blocks into the DMFNet can improve model sensitivity and prediction accuracy with minimal computational overhead. And we adopt a two-stage approach. In the ﬁrst stage, the foreground containing all organs is segmented. In the second stage, thirteen organs are segmented on the basis of the ﬁrst stage. We use labeled data to train the teacher model, use the teacher model to predict the unlabeled data, and take the segmentation result as pseudo labels for the following training. And then the data with true labels and the data with pseudo labels are used to train student models with the help of robust loss functions, namely beta cross-entropy, symmetric cross-entropy, and generalized cross-entropy. Finally, the trained student model is used to predict the data.
DSiD: a Delphes Detector for ILC Physics Studies

We describe DSiD, a fast simulation Delphes detector for the International Linear Collider (ILC) based on the full simulation performance of the SiD detector. SiD is one of two detectors described in the ILC Technical Design Report (TDR). The tracking efficiency, tracking momentum resolution, electromagnetic and hadronic calorimeter energy resolution, particle identification and flavor tagging efficiencies are taken from the Detailed Baseline Design (DBD) study as described in ILC TDR Volume 4: Detectors. In a cross-check study with $4 \times 10^6$ $e^+ e^- \rightarrow b \bar{b}$ events generated at $\sqrt{s}=500$ GeV and simulated by Delphes with the DSiD detector card, these performance characteristics are measured and found to be commensurate with the DBD results. For a new physics use case example, we describe a study of Next-to-Minimal Supersymmetric $e^+ e^- \rightarrow 2 \chi_3 \rightarrow 2\chi_1 2 h_1$ with $h_1 \rightarrow 2a_1$ or $h_1 \rightarrow b\bar{b}$ at the $\sqrt{s}=500$ GeV ILC. The card is available on HepForge.
Causal hydrodynamics and the membrane paradigm

We obtain the relaxation time for the shear viscous stress for various geometries using the 'membrane paradigm' formula proposed recently. We consider the generic Schwarzschild-anti-de Sitter black holes (SAdS), the generic Dp-brane, the Klebanov-Tseytlin (KT) geometry, and the N=2* theory. The formula is the 'shear mode' result and is not fully trustable, but it may be helpful to learn some generic behaviors about the relaxation time. For example, a simple formula summarizes all known results for SAdS, and a single expression summarizes the results for the Dp-brane and the KT geometry.
Flowing dusty plasma experiments: generation of flow and measurement techniques

A variety of experimental techniques for the generation of subsonic/supersonic dust fluid flows and means of measuring such flow velocities are presented. The experiments have been carried out in a Π-shaped dusty plasma experimental device with micron size kaolin/melamine formaldehyde particles embedded in a background of argon plasma created by a direct current glow discharge. A stationary dust cloud is formed over the cathode region by precisely balancing the pumping speed and gas flow rate. A flow of dust particles/fluid is generated by additional gas injection from a single or dual locations or by altering the dust confining potential. The flow velocity is then estimated by three different techniques, namely, by super particle identification code, particle image velocimetry analysis and the excitation of dust acoustic waves. The results obtained from these three different techniques along with their merits and demerits are discussed. An estimation of the neutral drag force responsible for the generation as well as the attenuation of the dust fluid flow is made. These techniques can be usefully employed in laboratory devices to investigate linear and non-linear collective excitations in a flowing dusty plasma.
The State-Dependent Eﬀects of Monetary Policy

This paper studies state-dependent eﬀects of monetary policy shocks. I ﬁrst consider state-dependence of policy actions in a simple static model. The model predicts that eﬀectiveness of monetary policy is positively related to the level of output. I next use an estimated DSGE model to quantitatively assess asymmetries in policy transmission mechanism. Consistent with the intuition from the simple model, I ﬁnd that the eﬀects of monetary policy on output are less powerful in recessions compared to expansions. By contrast, inﬂation is more sensitive in recessionary states. The latter implies that the aggregate price ﬂexibility is varying across the business cycle. In particular, prices are more ﬂexible when the economy is in a recessionary state. Conversely, prices become more rigid in expansionary states.
Investigating Metformin & Syrosingopine's Synthetic Lethality in PC3 & THP-1 Cancer Cell Lines

The oral antidiabetic, metformin, has been documented to have antineoplastic activity in various cancer cell lines, associated with the inhibition of mitochondrial complex I. However, pre-clinical studies have struggled to demonstrate such antineoplastic activity utilising metformin concentrations attainable with standard antidiabetic dosing. Research has revealed that metformin combined with the anti-hypertensive drug, syrosingopine, lowers metformin’s therapeutic threshold and sensitizes cancer cells to killing, by inhibiting monocarboxylate transporters (MCT)1/MCT4. The potent interaction between these drugs elicits a synthetic lethality, specific to transformed cells. This preliminary study aimed to investigate metformin’s antineoplastic effects and its synergistic relationship with syrosingopine by measuring cell viability and extracellular lactate in PC3 (adherent) and THP-1 (suspension) cancer cell lines. Overall, the PC3 cell line responded better to treatment with combined doses of metformin and syrosingopine, or either drug alone; however, synthetic lethality was not observed in either cell line. In both cell lines, the interaction between metformin and syrosingopine was not statistically significant in the cell viability assays (p>0.05). Analysis of MCT1/MCT4 inhibition through the measurement of extracellular lactate was not statistically significant (p>0.05) and proved inconclusive. Additionally, the nature of the cell line, adherent or suspension, was statistically significant (p<0.05) in some treatment groups, suggesting that this may play a role in the efficacy of the drug treatments. Further research is necessary to better understand the underlying cellular mechanisms of metformin and syrosingopine’s synthetic lethality, and syrosingopine’s MCT1/MCT4 inhibition. Future research should focus on achieving metformin doses capable of exhibiting antineoplastic effects in vivo
Leveraging generative models to characterize the failure conditions of image classifiers

We address in this work the question of identifying the failure conditions of a given image classifier. To do so, we exploit the capacity of producing controllable distributions of high quality image data made available by recent Generative Adversarial Networks (StyleGAN2): the failure conditions are expressed as directions of strong performance degradation in the generative model latent space. This strategy of analysis is used to discover corner cases that combine multiple sources of corruption, and to compare in more details the behavior of different classifiers. The directions of degradation can also be rendered visually by generating data for better interpretability. Some degradations such as image quality can affect all classes, whereas other ones such as shape are more class-specific. The approach is demonstrated on the MNIST dataset that has been completed by two sources of corruption: noise and blur, and shows a promising way to better understand and control the risks of exploiting Artificial Intelligence components for safety-critical applications.
Special Issue: Nonlinear Effects and Dynamics in Close Relationships

Background: Close relationships in older adulthood are characterized by heightened interdependence, which has implications for health and well-being as partners age together. Purpose: We describe a novel method that uses partners’ spatial proximity to examine the dynamics of interpersonal relationships. Research Design: In a sample of 10 older adult couples over a 14-day study period, we linked a continuous measure of partners’ spatial proximity with partners’ heart rates—a physiological marker of arousal. Results: Cross-correlations showed that proximity was consistently associated with each partner’s heart rate, but the magnitude and sequence of the correlation varied from day-to-day, suggesting that the coupling of proximity and heart rate is a dynamic of the interaction, rather than the couple. Additionally, our predictive model showed that all three time-series were necessary for optimal prediction, demonstrating that proximity and partners’ heart rates are dynamically intertwined. Conclusion: Together, these results demonstrate meaningful and predictable variation in couple dynamics at the momentary level that consists of a complex association between physiological and spatial proximity.
The Impact of Conservatism and Supply Chain Finance on Bad Debt Expense

Standard accrual models assume a parsimonious, linear relation between accruals and changes in sales but ignore the accounting methods and real transactions that generate accruals. Hence, legitimate transactions are often modeled as earnings management. We focus on a specific accrual, bad debt expense (BDE), and develop accounting-based models. We find that accounting-based models that incorporate conservatism have much better explanatory power. This is because, due to conservatism, receivables can be written off but are rarely written up, creating an asymmetry in BDE that linear models cannot capture. We then investigate the effect of real transactions on BDE by examining supply chain finance, which reduces the risk of receivables. We predict and find that supply chain finance decreases the level and asymmetry of BDE, which would likely be misclassified as earnings management by standard models. Our study highlights the importance of modeling individual accruals using accounting methods and incorporating real transactions.
Classifiers Should Do Well Even on Their Worst Classes

The performance of a vision classifier on a given test set is usually measured by its accuracy. For reliable machine learning systems, however, it is important to avoid the existence of areas of the input space where they fail severely. To reflect this, we argue that a single number does not provide a complete enough picture even for a fixed test set, as there might be particular classes or subtasks where a model that is generally accurate performs unexpectedly poorly. Without using new data, we motivate and establish a wide selection of interesting worst-case performance metrics which can be evaluated besides accuracy on a given test set. Some of these metrics can be extended when a grouping of the original classes into superclasses is available, indicating if the model is exceptionally bad at handling inputs from one superclass.
Representation Learning as Finding Necessary and Sufﬁcient Causes

Representation learning constructs low-dimensional representations to summarize essential features of high-dimensional data. This learning problem is often approached by describing various desiderata associated with learned representations; e.g., that they be non-spurious or efﬁcient. It can be challenging, however, to turn these intuitive desiderata into formal criteria that can be measured and enhanced based on observed data. In this paper, we take a causal perspective on representation learning, formalizing non-spuriousness and efﬁciency (in supervised representation learning) using counterfactual quantities and observable consequences of causal assertions. This yields computable metrics that can be used to assess the degree to which representations satisfy the desiderata of interest and learn non-spurious representations from single observational datasets.
Mixture of experts on Riemannian manifolds for visual-servoing ﬁxtures

—Adaptive Virtual Fixtures (VFs) for teleoperation often rely on visual inputs for online adaptation. State estimation from visual detections is never perfect, and thus affects the quality and robustness of adaptation. It is therefore important to be able to quantify how uncertain an estimation from vision is. This can, for example, inform on how to modulate a ﬁxture’s stiffness to decrease the physical force a human operator has to apply. Furthermore, the target of a manipulation operation might not be known from the beginning of the task, which creates the need for a principled way to add and remove ﬁxtures when possible targets appear in the robot workspace. In this paper we propose an on-manifold Mixture of Experts (MoE) model that synthesizes visual-servoing ﬁxtures while elegantly handling full pose detection uncertainties and 6D teleoperation goals in a uniﬁed framework. An arbitration function allocating the authority between multiple vision-based ﬁxtures arises naturally from the MoE formulation. We show that this approach allows a teleoperator to insert multiple printed circuit boards (PCBs) with high precision without requiring the manual design of VFs to guide the robot motion. An exemplary video visualizing the probability distribution resulting from our model is available at: https://youtu.be/GKMQvbJ5OzA
Invariance Discovery for Systematic Generalization in Reinforcement Learning

In the sequential decision making setting, an agent aims to achieve systematic generalization over a large, possibly infinite, set of environments. Such environments are modeled as discrete Markov decision processes with both states and actions represented through a feature vector. The underlying structure of the environments allows the transition dynamics to be factored into two components: one that is environment-specific and another one that is shared. Consider a set of environments that share the laws of motion as an illustrative example. In this setting, the agent can take a finite amount of reward-free interactions from a subset of these environments. The agent then must be able to approximately solve any planning task defined over any environment in the original set, relying on the above interactions only. Can we design a provably efficient algorithm that achieves this ambitious goal of systematic generalization? In this paper, we give a partially positive answer to this question. First, we provide the first tractable formulation of systematic generalization by employing a causal viewpoint. Then, under specific structural assumptions, we provide a simple learning algorithm that allows us to guarantee any desired planning error up to an unavoidable sub-optimality term, while showcasing a polynomial sample complexity.
Chemical vapor deposition-assembled graphene field-effect transistor on hexagonal boron nitride

We investigate key electrical properties of monolayer graphene assembled by chemical vapor deposition (CVD). Graphene field-effect transistors (GFETs) were fabricated with carbon channel placing directly on hexagonal boron nitride (h-BN) and SiO2. Small-signal transconductance (gm) and effective carrier mobility (μeff) are improved by 8.5 and 4 times on h-BN, respectively, as compared with that on SiO2. Compared with GFET with exfoliated graphene on SiO2, gm and μeff measured from device with CVD graphene on h-BN substrate exhibit comparable values. The experiment demonstrates the potential of employing h-BN as a platform material for large-area carbon electronics.
Organizational Justice and Health: Reviewing Two Decades of Studies

Organizational justice refers to employees’ perceptions of the fairness of decision-making rules and policies in the workplace. Lack of justice is suggested to be a signiﬁcant psychosocial risk factor that aﬀects employees’ attitudes and health. The aim of this narrative review was to compile the evidence available about the eﬀects of organizational justice on health. To this end, a literature search was carried out using the Web of Science, PubMed, and PsycINFO databases. The ﬁnal sample consisted of 103 articles that studied the eﬀects of justice on mental health (40 results), job stress (26), sickness absence (15), physical health (14), absenteeism/ presenteeism (3), safety at work (3), and health of third parties (2). The results show that perceptions of workplace justice predict employees’ mental health, stress-related health problems, and lower levels of sickness absence were relatively compelling. Future studies should focus on less-researched outcomes and on how these associations are modiﬁed by other variables for a better understanding of how justice aﬀects health, with a view to being able to carry out preventive measures more eﬃciently.
CopyCat: Leveraging American Sign Language Recognition in Educational Games for Deaf Children

Deaf children born to hearing parents lack continuous access to language, leading to weaker working memory compared to hearing children and deaf children born to deaf parents. CopyCat is a game where children communicate with the computer via American Sign Language (ASL), and it has been shown to improve language skills and working memory. Previously, CopyCat depended on unscalable hardware such as custom gloves for sign veriﬁcation, but modern 4K cameras and pose estimators present new opportunities. This thesis focuses on the current version of the CopyCat game using o ↵ -the-shelf hardware, as well as the state-of-the-art sign language recognition system we have developed to augment game play. Using Hidden Markov Models (HMMs), user independent word accuracies were 90.6%, 90.5%, and 90.4% for AlphaPose, Kinect, and MediaPipe, respectively. Transformers, a state-of-the-art model in natural language processing, performed 17.0% worse on average. Given these results, we believe our current HMM-based recognizer can be successfully adapted to verify children’s signing while playing CopyCat.
Subsidies, Entry, and Economic Growth in a Schumpeterian Model with Incumbents and Entrants

This paper explores the effects of various subsidies in a Schumpeterian model with incumbents and entrants. We find that subsidizing the production of intermediate goods or subsidizing incumbents’ in -house R&D serves to promote economic growth. However, the growth effect of the subsidy on entrants’ R&D is ambiguous. Moreover, we show that various types of subsidies have different effects on the entry of new firms and market structure. Finally, we calibrate the model and find that the subsidy on intermediate goods production is more effective than R&D subsidies in terms of promoting growth and raising welfare.
Digital Signal

The nonlinear complexity of volatility duration and volatility difference component based on voter ﬁnancial dynamics is investigated in this paper. The statistic – volatility difference component is ﬁrst introduced in this work, in an attempt to study the volatility behaviors comprehensively. The maximum change rate series and the average change rate series (both derived from the volatility difference components) are employed to characterize the volatility duration properties of ﬁnancial markets. Further, for the proposed series model and the proposed ﬁnancial statistic series (which are transformed to symbolic sequences), the permutation Lempel–Ziv complexity, a novel complexity measure, is introduced to study the corresponding randomness and complexity behaviors. Besides, Zipf analysis is also applied to investigate the corresponding Zipf distributions of the proposed series. The empirical study shows the similar complexity behaviors of volatility between the proposed price model and the real stock markets, which exhibits that the proposed model is feasible to some extent. © 2017