Miracle-dz/UniFAR-FTUs huggingface dataset:
Phase-field theory of edges in an anisotropic crystal

In the presence of sufficiently strong surface energy anisotropy, the equilibrium shape of an isothermal crystal may include corners or edges. Models of edges have, to date, involved the regularization of the corresponding free-boundary problem resulting in equilibrium shapes with smoothed out edges. In this paper, we take a new approach and consider how a phase-field model, which provides a diffuse description of an interface, can be extended to the consideration of edges by an appropriate regularization of the underlying mathematical model. Using the method of matched asymptotic expansions, we develop an approximate solution which corresponds to a smoothed out edge from which we are able to determine the associated edge energy.
Adsorbate-driven reactive interfacial Pt-NiO1−x nanostructure formation on the Pt3Ni(111) alloy surface

Formation of catalytically reactive metal oxide interfaces on the Pt3Ni(111) surface is revealed using operando surface analysis. The origin of the synergistic catalytic effect between metal catalysts and reducible oxides has been debated for decades. Clarification of this effect, namely, the strong metal-support interaction (SMSI), requires an understanding of the geometric and electronic structures of metal-metal oxide interfaces under operando conditions. We show that the inherent lattice mismatch of bimetallic materials selectively creates surface segregation of subsurface metal atoms. Interfacial metal-metal oxide nanostructures are then formed under chemical reaction environments at ambient pressure, which thus increases the catalytic activity for the CO oxidation reaction. Our in situ surface characterizations using ambient-pressure scanning tunneling microscopy and ambient-pressure x-ray photoelectron spectroscopy exhibit (i) a Pt-skin layer on the Pt-Ni alloyed surface under ultrahigh vacuum, (ii) selective Ni segregation followed by the formation of NiO1−x clusters under oxygen gas, and (iii) the coexistence of NiO1−x clusters on the Pt-skin during the CO oxidation reaction. The formation of interfacial Pt-NiO1−x nanostructures is responsible for a highly efficient step in the CO oxidation reaction. Density functional theory calculations of the Pt3Ni(111) surface demonstrate that a CO molecule adsorbed on an exposed Pt atom with an interfacial oxygen from a segregated NiO1−x cluster has a low surface energy barrier of 0.37 eV, compared with 0.86 eV for the Pt(111) surface.
Impacts of Rice Combine Harvesters on Economic and Social of Farmers in a village of the Tung Kula Ronghai Region.

The Tung Kula Ronghai (TKR) in Northeastern Thailand is often regarded as the best production area of Hom Mali Rice, giving the best quality of rice. More than 3.06 million tons from 9.19 million ton of exported rice in 2007 was produced from the TKR, accounted for 33%. Labor shortage problem changes farmer practice to adopted Combine Harvesters widespread in the TKR. The objectives of this research was to study the impacts of Combine Harvesters on Hom Mali rice agroecosystem in the TKR and describe the coping strategies farmers employ to cope with those impacts, in order to better understand agroecosystem impacts of Combine Harvesters in the TKR. The study area of this research is Roi-Et province, the province is one part of the TKR in the northeast Thailand, farmers conduct rainfed rice cultivation, the area chosen for the study is village No.1, Kukasing sub-district, Kaset Wisai district. The data were collected from both primary and secondary sources. The primary source included existing maps and publications whereas the secondary source was interview with relevant officials, Combine Harvester's brokers, Combine Harvester's owners and farmers. Semi-Structured interview used in this research. Results of the study indicated the impacts of Combine Harvester on Economic and social of TKR farmers included the net benefit of combine harvesting was about 30.3% higher compared to manual harvesting and threshing. After widespread using of combine harvesters, these absent household members no need to return home to help their family to harvest the paddy and then went back to their workplace after finished harvesting like before. 48% of household's absent member continues working in the city.
Flow stimuli reveal ecologically-appropriate responses in mouse visual cortex

Assessments of the mouse visual system based on spatial frequency analysis imply that its visual capacity is low, with few neurons responding to spatial frequencies greater than 0.5 cycles/deg. However, visuallymediated behaviors, such as prey capture, suggest that the mouse visual system is more precise. We introduce a new stimulus class—visual flow patterns—that is more like what the mouse would encounter in the natural world than are sine-wave gratings but is more tractable for analysis than are natural images. We used 128-site silicon microelectrodes to measure the simultaneous responses of single neurons in the primary visual cortex (V1) of alert mice. While holding temporal-frequency content fixed, we explored a class of drifting patterns of black or white dots that have energy only at higher spatial frequencies. These flow stimuli evoke strong visually-mediated responses well beyond those predicted by spatial frequency analysis. Flow responses predominate in higher spatial-frequency ranges (0.15–1.6 cycles/degree); many are orientationor direction-selective; and flow responses of many neurons depend strongly on sign of contrast. Many cells exhibit distributed responses across our stimulus ensemble. Together, these results challenge conventional linear approaches to visual processing and extend the mouse’s visual capacity to behaviorally-relevant ranges. Significance Statement The visual system of the mouse is now widely studied as a model for development and disease in humans. Studies of its primary visual cortex (V1) using conventional grating stimuli to construct linear-nonlinear receptive fields suggest that the mouse must have very poor vision. Using novel stimuli resembling the flow of images across the retina as the mouse moves through the grass, we find that most V1 neurons respond reliably to very much finer details of the visual scene than previously believed. Our findings suggest that the conventional notion of a unique receptive field does not capture the operation of the neural network in mouse V1.
Scaling Gaussian Process Regression with Derivatives

Gaussian processes (GPs) with derivatives are useful in many applications, including Bayesian optimization, implicit surface reconstruction, and terrain reconstruction. Fitting a GP to function values and derivatives at $n$ points in $d$ dimensions requires linear solves and log determinants with an ${n(d+1) \times n(d+1)}$ positive definite matrix -- leading to prohibitive $\mathcal{O}(n^3d^3)$ computations for standard direct methods. We propose iterative solvers using fast $\mathcal{O}(nd)$ matrix-vector multiplications (MVMs), together with pivoted Cholesky preconditioning that cuts the iterations to convergence by several orders of magnitude, allowing for fast kernel learning and prediction. Our approaches, together with dimensionality reduction, enables Bayesian optimization with derivatives to scale to high-dimensional problems and large evaluation budgets.
Optimal design of deterministic lateral displacement device for viscosity-contrast-based cell sorting

We solve a design optimization problem for deterministic lateral displacement (DLD) device to sort same-size biological cells by their deformability, in particular to sort red blood cells (RBCs) by their viscosity contrast between the fluid in the interior and the exterior of the cells. A DLD device optimized for efficient cell sorting enables rapid medical diagnoses of several diseases such as malaria since infected cells are stiffer than their healthy counterparts. The device consists of pillar arrays in which pillar rows are tilted and hence are not orthogonal to the columns. This arrangement leads cells to have different final vertical displacements depending on their deformability, therefore, it vertically separates the cells. Pillar cross section, tilt angle of the pillar rows and center-to-center distances between pillars define a unique device. For a given pair of viscosity contrast values of the cells we seek optimal DLD designs by fixing the tilt angle and the center-to-center distances. So the only design parameter is the pillar cross section which we parameterize with uniform 5th order B-splines. We propose an objective function to try to capture efficient cell sorting. The objective function is evaluated by simulating the cell flows through a device using our 2D model based on a boundary integral method (Kabacaoglu et al. Journal of Computational Physics, 357:43-77, 2018). We solve the optimization problem using the covariance matrix adaptation evolution strategy (CMA-ES), which is a stochastic, derivative-free algorithm. We present several scenarios where solving the optimization problem finds designs that can separate cells with similar viscosity contrast values. To the best of our knowledge, this is the first study which poses designing a DLD device as a constrained optimization problem and shows that solving this problem systematically discovers optimal designs.
Interventions to Support Caregivers or Families of Patients with TBI, PTSD, or Polytrauma: A Systematic Review [Internet]

Family members perform a significant service caring for Veterans with severe physical, mental, and cognitive impairments. A family caregiver may be defined as “any relative, partner, friend or neighbor who has a significant personal relationship with, and provides a broad range of assistance for, an older person or an adult with a chronic or disabling condition. ” Depending on the injuries and health conditions, for some families, the need for intensive family caregiving support can last for decades. Further, caregiving can have negative implications for the caregiver’s physical and mental health, employment, and financial security.Other systematic reviews have shown that some caregiver supportive services can reduce caregiver burden and mental distress and improve care recipient function; however, this research has focused most frequently on recipients with cognitive or memory disorders and illnesses such as cancer. There is a need to better understand the impact of interventions that support caregivers or families of patients with disabling conditions common among Veterans. This evidence synthesis describes the volume of published literature evaluating the effects of family caregiving support programs for patients with traumatic brain injury (TBI), posttraumatic stress disorder (PTSD), or polytrauma.
Proposed MAPS-Sponsored Israeli Pilot Study : MDMA-assisted Psychotherapy in Twelve People with War and Terrorism-related Posttraumatic Stress Disorder ( PTSD )

This application is for a pilot study of 3,4-methylenedioxymethamphetamine (MDMA)assisted therapy in people with posttraumatic stress disorder (PTSD) arising from combat or terrorism. This research builds on findings from Phase I studies conducted in the US and Europe (Cami et al. 2000; Forsling et al. 2001; Farre et al. 2004; Gamma et al. 2000; Grob et al. 1996; Grob et al., Unpublished; Harris et al. 2002; Lamers et al. 2003; Lester et al. 2000; Liechti et al. 2001; Mas et al. 1999; Pacifici et al. 2001; Pacifici et al. 2004; Tancer and Johanson 2001; Tancer and Johanson 2003; Vollenweider et al. 1998), and on one sponsor-conducted randomized, placebo-controlled Phase II study of MDMAassisted psychotherapy in people with crime-related PTSD (Mithoefer 2004). This study first only enrolled people with crime-related PTSD but now may enroll people with war or terrorism-related PTSD.
Original article Coffee consumption and propensity to experience aggressive feelings in provoking and frustrating situations

The present study focused on examining whether coffee intake is related to the intensity of aggressive feelings in reaction to various provoking or frustrating situations, along with consideration of sex and culture. Previous studies focused mainly on the dose of caffeine (not the habit of coffee drinking) and aggressive behavior. Many of these studies showed a positive relation between the amount of caffeine consumed and aggression, so it was expected that also habitual coffee consumption would be positively related to propensity for aggressive feelings in situations of provocation and frustration. Participants from Greece (n = 299) and Poland (n = 300) declared whether they were coffee drinkers (at least 1 mug of coffee per day) and completed the Situational Triggers of Aggressive Responses questionnaire. The results showed that in females, coffee drinkers had higher scores in Sensitivity to Provocation (SP) and Frustration (SF) than nondrinkers, while no differences between drinkers and nondrinkers appeared in males. Moreover, amongst coffee drinkers, females had higher scores in SP and SF than males, while no sex differences were found in nondrinkers. Furthermore, in Poland coffee drinkers had higher SP scores than nondrinkers. Poles also had higher SP scores than Greeks, but only when coffee drinkers were compared.
High-energy brain dynamics during anesthesia-induced unconsciousness

Characterizing anesthesia-induced alterations to brain network dynamics provides a powerful framework to understand the neural mechanisms of unconsciousness. To this end, increased attention has been directed at how anesthetic drugs alter the functional connectivity between brain regions as defined through neuroimaging. However, the effects of anesthesia on temporal dynamics at functional network scales is less well understood. Here, we examine such dynamics in view of the free-energy principle, which postulates that brain dynamics tend to promote lower energy (more organized) states. We specifically engaged the hypothesis that such low-energy states play an important role in maintaining conscious awareness. To investigate this hypothesis, we analyzed resting-state BOLD fMRI data from human volunteers during wakefulness and under sevoflurane general anesthesia. Our approach, which extends an idea previously used in the characterization of neuron-scale populations, involves thresholding the BOLD time series and using a normalized Hamiltonian energy function derived from the Ising model. Our major finding is that the brain spends significantly more time in lower energy states during eyes-closed wakefulness than during general anesthesia. This effect is especially pronounced in networks thought to be critical for maintaining awareness, suggesting a crucial cognitive role for both the structure and the dynamical landscape of these networks. Author Summary We show that activity in the human brain, as captured by functional magnetic resonance imaging (fMRI), is more organized during wakefulness than during general anesthesia. This increased organization corresponds to a decrease in a statistical-physics-inspired energy measure among brain regions of shared functional specialization (resting-state networks) that have putative roles in conscious awareness and attention. Characterizing the energy distributions in this way reveals significant changes in the dynamics of brain activity in different states of consciousness, insights that are not observable in the average functional connectivity data alone. Our results indicate that the ability of brain networks to sustain stable representations, via their dynamics, may be crucial for consciousness and cognition.
Metric Semantics for Probabilistic Relational Reasoning

The Fuzz programming language [Reed and Pierce, 2010] uses an elegant linear type system to express and reason about function sensitivity properties, most notably \epsilon-differential privacy. We show how to extend Fuzz to encompass more general relational properties of probabilistic programs, with our motivating example being the (\epsilon, {\delta})-variant of differential privacy. Our technical contributions are threefold. First, we introduce the categorical notion of assignment on a monad to model composition properties of probabilistic divergences. Then, we show how to express relational properties as sensitivity properties via an adjunction we call the path construction, reminiscent of Benton's linear and non-linear models of linear logic. Finally, we instantiate our semantics to model the terminating fragment of Fuzz, and extend the language with types carrying information about richer divergences between distributions.
The Upward Bias in Measures of Information Derived from Limited Data Samples

Extracting information measures from limited experimental samples, such as those normally available when using data recorded in vivo from mammalian cortical neurons, is known to be plagued by a systematic error, which tends to bias the estimate upward. We calculate here the average of the bias, under certain conditions, as an asymptotic expansion in the inverse of the size of the data sample. The result agrees with numerical simulations, and is applicable, as an additive correction term, to measurements obtained under such conditions. Moreover, we discuss the implications for measurements obtained through other usual procedures.
Computing Recommendations via a Knowledge Graph-aware Autoencoder

In the last years, deep learning has shown to be a game-changing technology in artificial intelligence thanks to the numerous successes it reached in diverse application fields. Among others, the use of deep learning for the recommendation problem, although new, looks quite promising due to its positive performances in terms of accuracy of recommendation results. In a recommendation setting, in order to predict user ratings on unknown items a possible configuration of a deep neural network is that of autoencoders tipically used to produce a lower dimensionality representation of the original data. In this paper we present KG-AUTOENCODER, an autoencoder that bases the structure of its neural network on the semanticsaware topology of a knowledge graph thus providing a label for neurons in the hidden layer that are eventually used to build a user profile and then compute recommendations. We show the effectiveness of KG-AUTOENCODER in terms of accuracy, diversity and novelty by comparing with state of the art recommendation algorithms.
On the Number of Circuits in Regular Matroids (with Connections to Lattices and Codes)

We show that for any regular matroid on $m$ elements and any $\alpha \geq 1$, the number of $\alpha$-minimum circuits, or circuits whose size is at most an $\alpha$-multiple of the minimum size of a circuit in the matroid is bounded by $m^{O(\alpha^2)}$. This generalizes a result of Karger for the number of $\alpha$-minimum cuts in a graph. As a consequence, we obtain similar bounds on the number of $\alpha$-shortest vectors in "totally unimodular" lattices and on the number of $\alpha$-minimum weight codewords in "regular" codes.
Physically-Based Simulation of Cosmetics via Intrinsic Image Decomposition with Facial Priors

We present a physically-based approach for simulating makeup in face images. The key idea is to decompose the face image into intrinsic image layers – namely albedo, diffuse shading, and specular highlights – which are each differently affected by cosmetics, and then manipulate each layer according to corresponding models of reflectance. Accurate intrinsic image decompositions for faces are obtained with the help of human face priors, including statistics on skin reflectance and facial geometry. The intrinsic image layers are then transformed in appearance according to measured optical properties of cosmetics and proposed adaptations of physically-based reflectance models. With this approach, realistic results are generated in a manner that preserves the personal appearance features and lighting conditions of the target face while not requiring detailed geometric and reflectance measurements. We demonstrate this technique on various forms of cosmetics including foundation, blush, lipstick, and eye shadow. Results on both images and videos exhibit a close approximation to ground truth and compare favorably to existing techniques.
An HMM/MLP Architecture for Sequence Recognition

This paper presents a hybrid architecture of hidden Markov models (HMMs) and a multilayer perceptron (MLP). This exploits the discriminative capability of a neural network classifier while using HMM formalism to capture the dynamics of input patterns. The main purpose is to improve the discriminative power of the HMM-based recognizer by additionally classifying the likelihood values inside them with an MLP classifier. To appreciate the performance of the presented method, we apply it to the recognition problem of on-line handwritten characters. Simulations show that the proposed architecture leads to a significant improvement in generalization performance over conventional approaches to sequential pattern recognition.
Analysis Acoustic Features for Acoustic Scene Classification and Score fusion of multi-classification systems applied to DCASE 2016 challenge

This paper describes an acoustic scene classification method which achieved the 4th ranking result in the IEEE AASP challenge of Detection and Classification of Acoustic Scenes and Events 2016. In order to accomplish the ensuing task, several methods are explored in three aspects: feature extraction, feature transformation, and score fusion for final decision. In the part of feature extraction, several features are investigated for effective acoustic scene classification. For resolving the issue that the same sound can be heard in different places, a feature transformation is applied for better separation for classification. From these, several systems based on different feature sets are devised for classification. The final result is determined by fusing the individual systems. The method is demonstrated and validated by the experiment conducted using the Challenge database.
Cloning and Characterization of Two Toll Receptors (PcToll5 and PcToll6) in Response to White Spot Syndrome Virus in the Red Swamp Crayfish Procambarus clarkii

Toll/Toll-like receptors are key components in the innate immune responses of invertebrates. In this study, we identified two novel Toll receptors (PcToll5 and PcToll6) from the red swamp crayfish Procambarus clarkii. The complete cDNA sequence of PcToll5 is 4247 bp, encoding a 1293 amino acid polypeptide. The full-length 4688 bp PcToll6 encodes a putative protein of 1195 amino acids. Quantitative RT-PCR analysis indicated that PcToll5 and PcToll6 were constitutively expressed in all tissues studied. The highest expression levels of PcToll5 and PcToll6 were found in the intestine and gills, respectively, and were significantly upregulated from 24 to 48 h during white spot syndrome virus (WSSV) challenge. siRNA-mediated RNA interference results showed that PcToll5 and PcToll6 might regulate the expression of anti-lipopolysaccharide factors (PcALF2 and PcALF3) in vivo. Overexpression of PcToll5 and PcToll6 in Drosophila Schneider 2 (S2) cells activated the transcription of Drosophila antimicrobial peptides, including drosomycin (Drs), metchnikowin (Mtk), and attacin A (AttA), and shrimp Penaeidin-4 (Pen4). These findings provide significant information that PcToll5 and PcToll6 may contribute to host immune defense against WSSV in P. clarkii.