Miracle-dz/UniFAR-FTUs huggingface dataset:
3 D Point Cloud Segmentation Using a Fully Connected Conditional Random Field

Traditional image segmentation methods working with low level image features are usually difficult to adapt to higher level tasks, such as object recognition and scene understanding. Object segmentation emerges as a new challenge in this research field. It aims at obtaining more meaningful segments related to semantic objects in the scene by analyzing a combination of different information. 3D point cloud data obtained from consumer depth sensors has been exploited to tackle many computer vision problems due to its richer information about the geometry of 3D scenes compared to 2D images. Meanwhile, new challenges have also emerged as the depth information is usually noisy, sparse and unorganized. In this paper, we present a novel point cloud segmentation approach for segmenting interacting objects in a stream of point clouds by exploiting spatio-temporal coherence. We pose the problem as an energy minimization task in a fully connected conditional random field with the energy function defined based on both current and previous information. We compare different methods and prove the improved segmentation performance and robustness of the proposed approach in sequences with over 2k frames.
Branch prediction related Optimizations for Multithreaded Processors

Major chip manufacturers have all introduced Multithreaded processors. These processors are used for running a variety of workloads. Efficient resource utilization is an important design aspect in such processors. Depending on the workload, mis-speculated execution can severely impact resource utilization and power utilization. In general, compared to a uniprocessor, a multithreaded processor may have better tolerance towards mis-speculation. However there can still be phases where even a multi-threaded processor performance may get impacted by branch induced mis-speculation. In this paper I propose monitoring the branch predictor behavior of various hardware threads running on the multi-threaded processor and use that information as a feedback to the thread arbiter/picker which schedules the next thread to fetch instructions from. If I find that a particular thread is going through a phase where it is consistently mis-predicting its branches and its average branch misprediction stall is above a specific threshold then I temporarily reduce the priority for picking that thread. I do a qualitative comparison of various solutions to the problem of resource inefficiency caused due to mis-speculated branches in multithreaded processors. This work can be extended by doing a quantitative evaluation.
Accelerating Green Computing with Hybrid Asymmetric Multicore Architectures and Safe Parallelism

In this paper we present a novel strategy for accelarating green computing by utilizing and adopting the Hybrid Asymmetric Multicore Architectures (HAMA) model with Safe Parallelism. Most of the modern computing is serial and contributes to the global footprint of energy consumption. These impacts are often witnessed and experienced in many server farms and cloud computing platforms where the majority of the world’s information resides. Evidently in this paper we present a novel strategy that can help decelerate the global footprint of energy consumption caused by computing. Through our strategy we prove that by adopting HAMA and utilizing safe parallelism energy consumption per computation can be minimized.
ASSOCIATION FOR CONSUMER RESEARCH

We investigate consumer forgiveness when formerly stocked-out products become available. When low-involvement products become available after being unintentionally stocked out, high reactance individuals are more satisfied and low reactance individuals are less satisfied with a store than in a control condition. In high involvement situations with attractive products, all individuals forgive the firm when the product becomes available. However, if the stockout is intentional, high reactance individuals do not forgive the firm, regardless of involvement. We argue these responses are driven by a balance of 1) desire for the restricted object and 2) negativity toward the source of the transgression.
' s response to reviews Title : Characterisation of an aerosol exposure system to evaluate the genotoxicity of whole mainstream cigarette smoke using the in vitro gammaH 2 AX assay by High Content Screening

The genotoxic effect of cigarette smoke is routinely measured by treating cells with cigarette Particulate Matter (PM) at different dose levels in submerg ed cell cultures. However, PM exposure cannot be considered as a complete exposure as it does not conta in the g s phase component of the cigarette smoke. The in vitro γH2AX assay by High Content Screening (HCS) has been suggested as a complementary tool to the standard ba ttery of genotoxicity assays as it detects DNA double strand breaks in a high-throughput fashion. The aim of this study was to further optimise the in vitro γH2AX assay by HCS to enable aerosol exposure of human bronchial epithelial BEAS-2B cells at the air-liquid inter face (ALI). Whole mainstream cigarette smoke (WMCS) from two reference cigar ettes (3R4F and M4A) were assessed for their genotoxic potential. During the study, a further c haracterisation of the Borgwaldt RM20S aerosol exposure system to include single dilution assessment wi th a reference gas was also carried out. The results of the optimisation showed that both reference cigarett es produced a positive genotoxic response at all dilutions tested. However, the correlation between dose and response was low for both 3R4F and M4A (Pearson coefficient, r = -0.53 and -0.44 respectively). During the additional characterisation of the exposur e system, it was observed that several pre-programmed dilutions did not perform as expected. Overall, the in vitro γH2AX assay by HCS could be used to evaluate WMCS in cell cul tures at the ALI. Additionally, the extended characterisation of the expos ure ystem indicates that assessing the performance of the dilutions could improve the existing routine QC che cks.
T
-dualizing de Sitter no-go scenarios

In the context of realizing de-Sitter vacua and the slow-roll inflation, several no-go conditions have been found in the framework of type IIA (generalized) flux compactifications. In this article, using our recently proposed $T$-dual dictionary in arXiv:1909.07391, we translate various such type IIA no-go conditions which subsequently leads to some interesting de-Sitter no-go scenarios in the presence of (non-)geometric fluxes on the dual type IIB side. We also present the relevance of using $K3/{\mathbb T}^4$-fibred Calabi Yau threefolds in order to facilitate one particular class of the de-Sitter no-go conditions. This analysis helps in refining certain corners of the vast non-geometric flux landscape for the hunt of de-Sitter vacua.
Super Resolution Using Cycle-Consistent Generative Adversarial Networks

Generative Adversarial Networks (GANs) have only recently been used for the super resolution task [6]. This method lead to perceptually pleasing photos with higher levels of details not seen in photos generated using other methods [4]. Even then, Ledig et al. used a generic implementation of the GAN architecture, called SRGAN. They only changed the cost function to optimize the perceptualloss as opposed to the mean-square-error. In the same year, [10] proposed CycleGAN, a GAN network with two generators and two discriminators where one generator-discriminator pair tries to outperform the other. This method surpassed the baseline results in many areas, such as photo enhancement. I think that combining the two, Ledig et al.’s metric of evaluation and Zhu et al.’s CycleGAN architecture, will lead to an overall better performance on the super resolution task.
Controlled Forgetting: Targeted Stimulation and Dopaminergic Plasticity Modulation for Unsupervised Lifelong Learning in Spiking Neural Networks

Stochastic gradient descent requires that training samples be drawn from a uniformly random distribution of the data. For a deployed system that must learn online from an uncontrolled and unknown environment, the ordering of input samples often fails to meet this criterion, making lifelong learning a difficult challenge. We exploit the locality of the unsupervised Spike Timing Dependent Plasticity (STDP) learning rule to target local representations in a Spiking Neural Network (SNN) to adapt to novel information while protecting essential information in the remainder of the SNN from catastrophic forgetting. In our Controlled Forgetting Networks (CFNs), novel information triggers stimulated firing and heterogeneously modulated plasticity, inspired by biological dopamine signals, to cause rapid and isolated adaptation in the synapses of neurons associated with outlier information. This targeting controls the forgetting process in a way that reduces the degradation of accuracy for older tasks while learning new tasks. Our experimental results on the MNIST dataset validate the capability of CFNs to learn successfully over time from an unknown, changing environment, achieving 95.24% accuracy, which we believe is the best unsupervised accuracy ever achieved by a fixed-size, single-layer SNN on a completely disjoint MNIST dataset.
Medical Complications of Crystal Methamphetamine

SUMMARY This article reviews available literature on the medical morbidities associated with methamphetamine abuse in HIV-infected patients. Medical complications include hypertension, hyperthermia, rhabdomyolysis, and stroke. One fatal case of ingestion of methamphetamine with HIV medication has been documented. Two fatal cases of ingestion of HIV medication with the amphetamine analogue n-methyl-3,4 methylenedioxymethamphetamine (MDMA, or “ecstasy”) have also been reported. Some molecular researchers suggest that dopaminergic systems are vulnerable to the combined neurotoxicity of HIV infection and methamphetamine. Methamphetamine is a new challenge related to treatment and prevention of HIV infection.
Can A User Anticipate What Her Followers Want?

Whenever a social media user decides to share a story, she is typically pleased to receive likes, comments, shares, or, more generally, feedback from her followers. As a result, she may feel compelled to use the feedback she receives to (re-)estimate her followers' preferences and decides which stories to share next to receive more (positive) feedback. Under which conditions can she succeed? In this work, we first look into this problem from a theoretical perspective and then provide a set of practical algorithms to identify and characterize such behavior in social media. More specifically, we address the above problem from the viewpoint of sequential decision making and utility maximization. For a wide variety of utility functions, we first show that, to succeed, a user needs to actively trade off exploitation-- sharing stories which lead to more (positive) feedback--and exploration-- sharing stories to learn about her followers' preferences. However, exploration is not necessary if a user utilizes the feedback her followers provide to other users in addition to the feedback she receives. Then, we develop a utility estimation framework for observation data, which relies on statistical hypothesis testing to determine whether a user utilizes the feedback she receives from each of her followers to decide what to post next. Experiments on synthetic data illustrate our theoretical findings and show that our estimation framework is able to accurately recover users' underlying utility functions. Experiments on several real datasets gathered from Twitter and Reddit reveal that up to 82% (43%) of the Twitter (Reddit) users in our datasets do use the feedback they receive to decide what to post next.
Real-time cortical simulation on neuromorphic hardware

Real-time simulation of a large-scale biologically representative spiking neural network is presented, through the use of a heterogeneous parallelization scheme and SpiNNaker neuromorphic hardware. A published cortical microcircuit model is used as a benchmark test case, representing ≈1 mm2 of early sensory cortex, containing 77 k neurons and 0.3 billion synapses. This is the first hard real-time simulation of this model, with 10 s of biological simulation time executed in 10 s wall-clock time. This surpasses best-published efforts on HPC neural simulators (3 × slowdown) and GPUs running optimized spiking neural network (SNN) libraries (2 × slowdown). Furthermore, the presented approach indicates that real-time processing can be maintained with increasing SNN size, breaking the communication barrier incurred by traditional computing machinery. Model results are compared to an established HPC simulator baseline to verify simulation correctness, comparing well across a range of statistical measures. Energy to solution and energy per synaptic event are also reported, demonstrating that the relatively low-tech SpiNNaker processors achieve a 10 × reduction in energy relative to modern HPC systems, and comparable energy consumption to modern GPUs. Finally, system robustness is demonstrated through multiple 12 h simulations of the cortical microcircuit, each simulating 12 h of biological time, and demonstrating the potential of neuromorphic hardware as a neuroscience research tool for studying complex spiking neural networks over extended time periods. This article is part of the theme issue ‘Harmonizing energy-autonomous computing and intelligence’.
Effect of “ OM Meditation ’ ’ On Blood Pressure in Young Healthy Adults of B . G .

DOI: 10.21276/sjams.2018. 6.7. 38 Abstract: This study was planned to find out the effect of OM Meditation on blood pressure in young healthy adults of B.G. Nagara. This study was conducted to determine the effect of OM meditation on blood pressure in healthy young individuals. The present study was a case-control study consisting of 50 healthy individuals in the age group of 20-40 years. This study was conducted in the Department of Physiology; Adichunchanagiri institute of medical sciences, B.G. Nagara, Nagamangala Taluk, Mandya district, after the institutional ethical clearance and written consent from each participant blood pressure was recorded before & after practicing deep OM Meditation daily for three months. The parameters thus recorded were analyzed for statistical significance using Students„t‟ test and p<0.05 was considered the level of significance. The results of this study indicate that practicing OM meditation daily as indicated in the method, decreases the blood pressure significantly even in the absence of any other form of physical exercise.
Infant Behavior and Development others who are securely attached in pregnancy show more ttuned infant mirroring 7 months postpartum

This study contrasted two forms of mother–infant mirroring: the mother’s imitation of the infant’s facial, gestural, or vocal behavior (i.e., “direct mirroring”) and the mother’s ostensive verbalization of the infant’s internal state, marked as distinct from the infant’s own experience (i.e., “intention mirroring”). Fifty mothers completed the Adult Attachment Interview (Dynamic Maturational Model) during the third trimester of pregnancy. Mothers returned with their infants 7 months postpartum and completed a modified still-face procedure. While direct mirroring did not distinguish between secure and insecure/dismissing mothers, secure mothers were observed to engage in intention mirroring more than twice as frequently as did insecure/dismissing mothers. Infants of the two mother groups also demonstrated differences, with infants of secure mothers directing their attention toward their mothers at a higher frequency than did infants of insecure/dismissing mothers. The findings underscore marked and ostensive verbalization as a distinguishing feature of secure mothers’ well-attuned, affect-mirroring communication with their infants. © 2014 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/).
Scene Person Structure Inference Machine Structure Inference Machine Structure Inference Machine Walking ? Waiting ? Waiting Waiting Waiting Walking Waiting Waiting Walking Waiting

Rich semantic relations are important in a variety of visual recognition problems. As a concrete example, group activity recognition involves the interactions and relative spatial relations of a set of people in a scene. State of the art recognition methods center on deep learning approaches for training highly effective, complex classifiers for interpreting images. However, bridging the relatively low-level concepts output by these methods to interpret higher-level compositional scenes remains a challenge. Graphical models are a standard tool for this task. In this paper, we propose a method to integrate graphical models and deep neural networks into a joint framework. Instead of using a traditional inference method, we use a sequential inference modeled by a recurrent neural network. Beyond this, the appropriate structure for inference can be learned by imposing gates on edges between nodes. Empirical results on group activity recognition demonstrate the potential of this model to handle highly structured learning tasks.
A I ] 3 O ct 2 01 8 Algorithms for Destructive Shift Bribery ∗

We study the complexity of Destructive Shift Bribery. In this problem, we are given an election with a set of candidates and a set of voters (each ranking the candidates from the best to the worst), a despised candidate d, a budget B, and prices for shifting d back in the voters’ rankings. The goal is to ensure that d is not a winner of the election. We show that this problem is polynomial-time solvable for scoring protocols (encoded in unary), the Bucklin and Simplified Bucklin rules, and the Maximin rule, but is NPhard for the Copeland rule. This stands in contrast to the results for the constructive setting (known from the literature), for which the problem is polynomial-time solvable for k-Approval family of rules, but is NP-hard for the Borda, Copeland, and Maximin rules. We complement the analysis of the Copeland rule showing W-hardness for the parameterization by the budget value, and by the number of affected voters. We prove that the problem is W-hard when parameterized by the number of voters even for unit prices. From the positive perspective we provide an efficient algorithm for solving the problem parameterized by the combined parameter the number of candidates and the maximum bribery price (alternatively the number of different bribery prices).
Nonlinear Response , Semi-classical Percolation and Breakdown in the RRTN Model

In this chapter, we address the issue of nonlinearity of response functions of disordered, granular composite materials and more specifically, an extension of the classical percolation (a classical insulator to metal phase transition; concisely reviewed by the author of the first chapter) problem to a situation where the contributions of nonclassical or non-diffusive processes cannot be neglected. In the paradigm of charge transport in electrical composites, this implies that the charge carriers travel not only inside metallic phases or grains but also outside of it; e.g., in the microscopic gap between two such grains using some externally assisted hopping over the barrier potential due to the gap. The assistance may be due to the phonons (ambient thermal bath), impressed electrical and/or chemical potential differences, etc. As such, these processes are known to bring forth nonlinearity in the response functions of the macroscopic system. Further, if the imminent phase transition as a function of the external driving field gives rise to a hazardous failure of some sort (usually irreversible), we are faced with a generalised breakdown phenomenon. In the paradigm of electrical phenomena, if it is the failure of a dielectric/insulator to stop charges from flowing, one observes a spark discharge (e.g. lightning in nature) and the phenomenon is called a dielectric breakdown. If, on the other hand, the failure is due to the transformation of a good metal into an insulator due to a very high current, then it is called a fuse or simply electrical breakdown. Classical (or, Zener) breakdown has been concisely reviewed and a fully microscopic theory of quantum (or, Landau-Zener) breakdown have been presented in the later two chapters of this book. We discuss some aspects of reversible, semi-classical breakdown as well, later in this chapter.
A Study of Accessible Game Applications on Smartphone for Visually-Impaired Users

In recent year, Mobile phone has become an important communication tool for daily living of people worldwide, and most of the mobile phones currently were addressed as ‘smartphone’. Due to these such kind of phones offered more advanced computing power and connectivity than anold contemporary style of mobile phone. Smartphones therefore have been repositioned as a new information medium and become a popular tool among people worldwide. Smartphone currently has become not only a primary technology device for general people, but they have been widely used by visually-impaired people. Visually-impaired users could use their smartphone based on their own individual preference such as to download applications that meet their need. But, the key important thing to use smartphone applications like to use internet websites for visually-impaired user was they must first ensure that all applications they need to download were fully accessible for them especially game applications. However there was a prominent lack of research on smart phone accessibility for such people. Also, there were lack of research studies and academic papers presented about accessible smart phone applications for visuallyimpaired people especially the concept and overview of accessible game applications on smartphone for visually-impaired users. This paper therefore proposed to review the overall concepts and types of accessible game applications on smartphone for visually-impaired Users.
. XVI . Asembo Bay Cohort Project

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The Indirect Continuous-GMM Estimation Rachidi Kotchoni

A curse of dimensionality arises when using the Continuum-GMM procedure to estimate large dimensional models. Two solutions are proposed, both of which convert the high dimensional model into a continuum of reduced information sets. Under certain regularity conditions, each reduced information set can be used to produce a consistent estimator of the parameter of interest. An indirect CGMM estimator is obtained by optimally aggregating all such consistent estimators. The simulation results suggest that the indirect CGMM procedure makes an e¢cient use of the information content of moment restrictions.
Ex Vivo Expansion of Functional Human UCB-HSCs / HPCs by Coculture with AFT 024-hkirre Cells

1 Department of Pathology, University of Veterinary and Animal Sciences, Lahore 54000, Pakistan 2 Institute of Biotechnology and Genetic Engineering, University of Agriculture, Peshawar 25000, Pakistan 3 State Key Laboratory of Biomembrane and Membrane Biotechnology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China 4Department of Anatomy and Histology, University of Veterinary and Animal Sciences, Lahore 54000, Pakistan 5 Department of Advanced Materials & Nanotechnology, College of Engineering, Peking University, Beijing 100871, China 6Department of Pharmacology and Toxicology, University of Veterinary and Animal Sciences, Lahore 54000, Pakistan 7 Institue of Biochemistry and Biotechnology, University of Veterinary and Animal Sciences, Lahore 54000, Pakistan