Miracle-dz/UniFAR-FTUs huggingface dataset:
Vyaktitv: A Multimodal Peer-to-Peer Hindi Conversations based Dataset for Personality Assessment

Automatically detecting personality traits can aid several applications, such as mental health recognition and human resource management. Most datasets introduced for personality detection so far have analyzed these traits for each individual in isolation. However, personality is intimately linked to our social behavior. Furthermore, surprisingly little research has focused on personality analysis using low resource languages. To this end, we present a novel peer-to-peer Hindi conversation dataset, Vyaktitv1. It consists of high-quality audio and video recordings of the participants, with Hinglish2 textual transcriptions for each conversation. The dataset also contains a rich set of socio-demographic features, like income, cultural orientation, amongst several others, for all the participants. We release the dataset for public use, as well as perform preliminary statistical analysis along the different dimensions. Finally, we also discuss various other applications and tasks for which the dataset can be employed.
Continuous Color Transfer

Color transfer, which plays a key role in image editing, has attracted noticeable attention recently. It has remained a challenge to date due to various issues such as time-consuming manual adjustments and prior segmentation issues. In this paper, we propose to model color transfer under a probability framework and cast it as a parameter estimation problem. In particular, we relate the transferred image with the example image under the Gaussian Mixture Model (GMM) and regard the transferred image color as the GMM centroids. We employ the Expectation-Maximization (EM) algorithm (E-step and M-step) for optimization. To better preserve gradient information, we introduce a Laplacian based regularization term to the objective function at the M-step which is solved by deriving a gradient descent algorithm. Given the input of a source image and an example image, our method is able to generate continuous color transfer results with increasing EM iterations. Various experiments show that our approach generally outperforms other competitive color transfer methods, both visually and quantitatively.
Introduction to logistic regression

For random field theory based multiple comparison corrections In brain imaging, it is often necessary to compute the distribution of the supremum of a random field. Unfortunately, computing the distribution of the supremum of the random field is not easy and requires satisfying many distributional assumptions that may not be true in real data. Thus, there is a need to come up with a different framework that does not use the traditional statistical hypothesis testing paradigm that requires to compute p-values. With this as a motivation, we can use a different approach called the logistic regression that does not require computing the p-value and still be able to localize the regions of brain network differences. Unlike other discriminant and classification techniques that tried to classify preselected feature vectors, the method here does not require any preselected feature vectors and performs the classification at each edge level.
Off-Path TCP Exploits of the Mixed IPID Assignment

In this paper, we uncover a new off-path TCP hijacking attack that can be used to terminate victim TCP connections or inject forged data into victim TCP connections by manipulating the new mixed IPID assignment method, which is widely used in Linux kernel version 4.18 and beyond to help defend against TCP hijacking attacks. The attack has three steps. First, an off-path attacker can downgrade the IPID assignment for TCP packets from the more secure per-socket-based policy to the less secure hash-based policy, building a shared IPID counter that forms a side channel on the victim. Second, the attacker detects the presence of TCP connections by observing the shared IPID counter on the victim. Third, the attacker infers the sequence number and the acknowledgment number of the detected connection by observing the side channel of the shared IPID counter. Consequently, the attacker can completely hijack the connection, i.e., resetting the connection or poisoning the data stream. We evaluate the impacts of this off-path TCP attack in the real world. Our case studies of SSH DoS, manipulating web traffic, and poisoning BGP routing tables show its threat on a wide range of applications. Our experimental results show that our off-path TCP attack can be constructed within 215 seconds and the success rate is over 88%. Finally, we analyze the root cause of the exploit and develop a new IPID assignment method to defeat this attack. We prototype our defense in Linux 4.18 and confirm its effectiveness through extensive evaluation over real applications on the Internet.
An adaptive random experiment design method for engineering experiment

This paper proposes an adaptive random experiment design (ARED) algorithm that can be applied to optimize the multiple factors and levels experiments. The algorithm takes real-time model error as the adaptive condition, and outputs a model that conforms to the error quantization standard based on the automatic process. According to the actual experimental scenario, the similar number of test cases were selected between the ARED method and the comparative experimental design method under the bimodal Gaussian function, the bimodal surface function and the peaks function, respectively. simultaneously, the support vector machine (SVM) algorithm is used to construct the model for the selected test cases, and the verification surface (or curve) is predicted. The qualitative and quantitative analysis is carried out at two-slice of applicability and precision. The results show that the ARED method can be applied to the experiment of multi-factor, and has better precision and applicability than the comparative experimental methods.
Low-Rank Covariance Function Estimation for Multidimensional Functional Data

Abstract Multidimensional function data arise from many fields nowadays. The covariance function plays an important role in the analysis of such increasingly common data. In this article, we propose a novel nonparametric covariance function estimation approach under the framework of reproducing kernel Hilbert spaces (RKHS) that can handle both sparse and dense functional data. We extend multilinear rank structures for (finite-dimensional) tensors to functions, which allow for flexible modeling of both covariance operators and marginal structures. The proposed framework can guarantee that the resulting estimator is automatically semipositive definite, and can incorporate various spectral regularizations. The trace-norm regularization in particular can promote low ranks for both covariance operator and marginal structures. Despite the lack of a closed form, under mild assumptions, the proposed estimator can achieve unified theoretical results that hold for any relative magnitudes between the sample size and the number of observations per sample field, and the rate of convergence reveals the phase-transition phenomenon from sparse to dense functional data. Based on a new representer theorem, an ADMM algorithm is developed for the trace-norm regularization. The appealing numerical performance of the proposed estimator is demonstrated by a simulation study and the analysis of a dataset from the Argo project. Supplementary materials for this article are available online.
Reinforcement Learning with Feedback-modulated TD-STDP

Spiking neuron networks have been used successfully to solve simple reinforcement learning tasks with continuous action set applying learning rules based on spike-timing-dependent plasticity (STDP). However, most of these models cannot be applied to reinforcement learning tasks with discrete action set since they assume that the selected action is a deterministic function of firing rate of neurons, which is continuous. In this paper, we propose a new STDP-based learning rule for spiking neuron networks which contains feedback modulation. We show that the STDP-based learning rule can be used to solve reinforcement learning tasks with discrete action set at a speed similar to standard reinforcement learning algorithms when applied to the CartPole and LunarLander tasks. Moreover, we demonstrate that the agent is unable to solve these tasks if feedback modulation is omitted from the learning rule. We conclude that feedback modulation allows better credit assignment when only the units contributing to the executed action and TD error participate in learning.
Continuous-variable gate teleportation and bosonic-code error correction

We examine continuous-variable gate teleportation using entangled states made from pure product states sent through a beamsplitter. We show that such states are Choi states for a (typically) non-unitary gate, and we derive the associated Kraus operator for teleportation, which can be used to realize non-Gaussian, non-unitary quantum operations on an input state. With this result, we show how gate teleportation is used to perform error correction on bosonic qubits encoded using the Gottesman-Kitaev-Preskill code. This result is presented in the context of deterministically produced macronode cluster states, generated by constant-depth linear optical networks, supplemented with a probabilistic supply of GKP states. The upshot of our technique is that state injection for both gate teleportation and error correction can be achieved without active squeezing operations---an experimental bottleneck for quantum optical implementations.
Sparse High-Order Portfolios Via Proximal Dca And Sca

In this paper, we study the cardinality constrained mean-variance-skewness-kurtosis (MVSKC) model for sparse high-order portfolio optimization. The MVSKC model is computationally challenging, as the objective function is non-convex and the cardinality constraint is discontinuous. Since the cardinality constraint has the difference-of-convex (DC) property, we transform it into a penalty term and then propose three algorithms, namely the proximal difference-of-convex algorithm (pDCA), pDCA with extrapolation (pDCAe), and the successive convex approximation (SCA), to handle the resulting penalized mean-variance-skewness-kurtosis (PM-VSK) formulation. Moreover, we establish theoretical convergence results for pDCA and SCA. Numerical experiments on a real dataset demonstrate the superiority of our proposed methods in obtaining better objective values and sparser solutions efficiently.
Fractal Carbon nanofoams by nanosecond and femtosecond pulsed-laser deposition

We report on an investigation on the properties of ultra-low density, fractal, Carbon nanofoams fabricated with the nanosecond and femtosecond pulsed-laser deposition (PLD) techniques. We measure through innovative techniques the foam mean density and the properties of the fractal aggregates composing the film (i.e. nanoparticles diameter, fractal dimension, and gyration radius) as a function of several PLD process parameters, namely the background gas pressure and the laser pulse characteristics (energy, time duration, repetition rate). We discuss the experimental observation on the basis of the existing literature, and we propose an analytical equation, based on the fractal scaling law, to predict the foam density from the aggregates properties. Finally, we use analytical arguments to explain the observed trends in the nanofoam density with respect to the process parameters, useful to gain new insights on the nanofoam growth and to guide experimental work in fabricating ultra-low density films with precisely controlled properties.
Position- and momentum-space two-body correlations in a weakly interacting trapped condensate

We investigate the position- and momentum-space two--body correlations in a weakly interacting, harmonically trapped atomic Bose-Einstein condensed gas at low temperatures. The two-body correlations are computed within the Bogoliubov approximation and the peculiarities of the trapped gas are highlighted in contrast to the spatially homogeneous case. In the position space, we recover the anti-bunching induced by the repulsive inter-atomic interaction in the condensed fraction localized around the trap center and the bunching in the outer thermal cloud. In the momentum space, bunching signatures appear for either equal or opposite values of the momentum and display peculiar features as a function of the momentum and the temperature. In analogy to the optical Hanbury Brown and Twiss effect, the amplitude of the bunching signal at close-by momenta is fixed by the chaotic nature of the matter field state and its linewidth is shown to be set by the (inverse of the) finite spatial size of the associated in-trap momentum components. In contrast, the linewidth of the bunching signal at opposite-momenta is only determined by the condensate size.
Double null coordinates for Kerr-Newman-de Sitter spacetimes

We construct double null coordinates for subextremal Kerr-Newman-de Sitter spacetimes. This was already done for Kerr spacetimes by Pretorius and Israel and by Dafermos and Luk. These coordinates allow one to immediately extend some results previously proven for Kerr. As an example, we illustrate how Sbierski's result, for the wave equation on the black hole interior, for Reissner-Nordstrom and Kerr spacetimes, applies to Kerr-Newman-de Sitter spacetimes.
Beyond Next Item Recommendation: Recommending and Evaluating List of Sequences

Recommender systems (RS) suggest items-based on the estimated preferences of users. Recent RS methods utilise vector space embeddings and deep learning methods to make efficient recommendations. However, most of these methods overlook the sequentiality feature and consider each interaction, e.g., check-in, independent from each other. The proposed method considers the sequentiality of the interactions of users with items and uses them to make recommendations of a list of multi-item sequences. The proposed method uses FastText \cite{bojanowski2016enriching}, a well-known technique in natural language processing (NLP), to model the relationship among the subunits of sequences, e.g., tracks, playlists, and utilises the trained representation as an input to a traditional recommendation method. The recommended lists of multi-item sequences are evaluated by the ROUGE \cite{lin2003automatic,lin2004rouge} metric, which is also commonly used in the NLP literature. The current experimental results reveal that it is possible to recommend a list of multi-item sequences, in addition to the traditional next item recommendation. Also, the usage of FastText, which utilise sub-units of the input sequences, helps to overcome cold-start user problem.
Optimal Quantization for Batch Normalization in Neural Network Deployments and Beyond

Quantized Neural Networks (QNNs) use low bit-width fixed-point numbers for representing weight parameters and activations, and are often used in real-world applications due to their saving of computation resources and reproducibility of results. Batch Normalization (BN) poses a challenge for QNNs for requiring floating points in reciprocal operations, and previous QNNs either require computing BN at high precision or revise BN to some variants in heuristic ways. In this work, we propose a novel method to quantize BN by converting an affine transformation of two floating points to a fixed-point operation with shared quantized scale, which is friendly for hardware acceleration and model deployment. We confirm that our method maintains same outputs through rigorous theoretical analysis and numerical analysis. Accuracy and efficiency of our quantization method are verified by experiments at layer level on CIFAR and ImageNet datasets. We also believe that our method is potentially useful in other problems involving quantization.
Structural and Geomechanical Analysis of Fractured Cambrian-Ordovician Reservoirs in the Northern Appalachian Basin; #10793 (2015)

The Cambrian-Ordovician Conasauga and Knox Groups constitute a regional succession of carbonates punctuated by brief periods of clastic deposition. Diagenesis and a history of multiple orogenic events resulted in the development of a complex fracture system. Understanding the orientation and distribution of the natural fracture sets within the Conasauga and Knox Groups is of significance in seeking potential CO2 storage zones and building reservoir models with fracture networks. Preliminary fracture studies were carried out to determine natural fracture orientation and distribution within the Conasauga and Knox Groups on the western flank of northern Appalachian Basin. Over 700 observations of fractures were interpreted on newly acquired resistivity and acoustic image logs collected at multiple well locations ranging in depth from 730 to 4150 meters. We evaluated structural parameters of the fractures using statistical analysis. Additionally, we evaluated the likelihood of observed fractures to slip under current stress conditions using 3D Mohr diagram for critically stressed fracture analysis. Analysis and interpretation of fracture orientation clusters shows the regional fracture systems are highly complex with possibly systematic and non-systematic fractures within the evaluated lithologic units. Fracture density is observed to increase up-dip within the studied area. Overall, a high percentage of fractures with varying dip direction were observed to strike sub-parallel to the contemporary maximum horizontal stress direction (SHmax) determined from wellbore failure, while a lower percentage strikes perpendicular to the SHmax direction. Critically stressed fracture analysis shows the natural fractures are not critically stressed in the current state. This project was supported by U.S. Department of Energy's National Energy Technology Laboratory (Award DE-FE0023330) and Ohio Development Services Agency (Grant OER-CDO/D-14-16).
Bounds for the Remainder in Simpson’s Inequality vian-Polynomial Convex Functions of Higher Order Using Katugampola Fractional Integrals

The goal of this paper is to derive some new variants of Simpson’s inequality using the class of - polynomial convex functions of higher order. To obtain the main results of the paper, we first derive a new generalized fractional integral identity utilizing the concepts of Katugampola fractional integrals. This new fractional integral identity will serve as an auxiliary result in the development of the main results of this paper.
Propofol protects against oxygen/glucose deprivation-induced cell injury via gap junction inhibition in astrocytes

Stroke is one of the leading causes of mortality and disability worldwide with limited clinical therapies available. The present study isolated primary astrocytes from the brains of rats and treated them with oxygen-glucose deprivation and re-oxygenation (OGD/R) to mimic hypoxia/reperfusion (H/R) injury in vitro to investigate stroke. It was revealed that propofol (2,6-diisopropylphenol), an intravenous sedative and anesthetic agent, protected against oxygen/glucose-deprivation (OGD) and induced cell injury. Furthermore, propofol exerted a protective effect by inhibiting gap junction function, which was also revealed to promote cell death in astrocytes. The present study further identified that propofol suppressed gap junction function by downregulating the protein expression levels of connexin43 (Cx43), which is one of the most essential components of gap junctions in astrocytes. In addition, when the expression levels of Cx43 were downregulated using small interfering RNA, OGD/R-induced cell death was decreased. Conversely, cell death was enhanced when Cx43 was overexpressed, which was reversed following propofol treatment. In summary, propofol protects against OGD-induced injury in astrocytes by decreasing the protein expression levels of Cx43 and suppressing gap junction function. The present study improved our understanding of how propofol protects astrocytes from OGD/R-induced injury.
Association of Frailty with Mortality in Older Inpatients with Covid-19: A Cohort Study

Abstract Background COVID-19 has disproportionately affected older people. Objective to investigate whether frailty is associated with all-cause mortality in older hospital inpatients, with COVID-19. Design cohort study. Setting secondary care acute hospital. Participants six hundred and seventy-seven consecutive inpatients aged 65 years and over. Methods Cox proportional hazards models were used to examine the association of frailty with mortality. Frailty was assessed at baseline, according to the Clinical Frailty Scale (CFS), where higher categories indicate worse frailty. Analyses were adjusted for age, sex, deprivation, ethnicity, previous admissions and acute illness severity. Results six hundred and sixty-four patients were classified according to CFS. Two hundred and seventy-one died, during a mean follow-up of 34.3 days. Worse frailty at baseline was associated with increased mortality risk, even after full adjustment (p = 0.004). Patients with CFS 4 and CFS 5 had non-significant increased mortality risks, compared to those with CFS 1–3. Patients with CFS 6 had a 2.13-fold (95% CI 1.34–3.38) and those with CFS 7–9 had a 1.79-fold (95% CI 1.12–2.88) increased mortality risk, compared to those with CFS 1–3 (p = 0.001 and 0.016, respectively). Older age, male sex and acute illness severity were also associated with increased mortality risk. Conclusions frailty is associated with all-cause mortality risk in older inpatients with COVID-19.
Natural HDAC‐1/8 inhibitor baicalein exerts therapeutic effect in CBF‐AML

Abstract Background Although targeting histone deacetylases (HDACs) may be an effective strategy for core binding factor‐acute myeloid leukemia (CBF‐AML) harboring t(8;21) or inv(16), HDAC inhibitors are reported to be limited by drug‐resistant characteristic. Our purpose is to evaluate the anti‐leukemia effects of Baicalein on CBF‐AML and clarify its underlying mechanism. Methods Enzyme activity assay was used to measure the activity inhibition of HDACs. Rhodamine123 and RT‐qPCR were employed to evaluate the distribution of drugs and the change of ATP‐binding cassette (ABC) transporter genes. CCK8, Annexin V/PI, and FACS staining certified the effects of Baicalein on cell growth, apoptosis, and differentiation. Duolink and IP assay assessed the interaction between HDAC‐1 and ubiquitin, HSP90 and AML1‐ETO, and Ac‐p53 and CBFβ‐MYH11. AML cell lines and primary AML cells‐bearing NOD/SCID mice models were used to evaluate the anti‐leukemic efficiency and potential mechanism of Baicalein in vivo. Results Baicalein showed HDAC‐1/8 inhibition to trigger growth suppression and differentiation induction of AML cell lines and primary AML cells. Although the inhibitory action on HDAC‐1 was mild, Baicalein could induce the degradation of HDAC‐1 via ubiquitin proteasome pathway, thereby upregulating the acetylation of Histone H3 without promoting ABC transporter genes expression. Meanwhile, Baicalein increased the acetylation of HSP90 and lessened its connection to AML1/ETO, consequently leading to degradation of AML1‐ETO in t(8;21)q(22;22) AML cells. In inv(16) AML cells, Baicalein possessed the capacity of apoptosis induction accompanied with p53‐mediated apoptosis genes expression. Moreover, CBFβ‐MYH11‐bound p53 acetylation was restored via HDAC‐8 inhibition induced by Baicalein contributing the diminishing of survival of CD34+ inv(16) AML cells. Conclusions These findings improved the understanding of the epigenetic regulation of Baicalein, and warrant therapeutic potential of Baicalein for CBF‐AML.