Miracle-dz/UniFAR-FTUs huggingface dataset:
A tutorial comparing different covariate balancing methods with an application evaluating the causal effect of exercise on the progression of Huntington's Disease

Randomized controlled trials are the gold standard for measuring the causal effects of treatments on clinical outcomes. However, randomized trials are not always feasible, and causal treatment effects must, therefore, often be inferred from observational data. Observational study designs do not allow conclusions about causal relationships to be drawn unless statistical techniques are used to account for the imbalance of confounders across groups while key assumptions hold. Propensity score (PS) and balance weighting are two useful techniques that aim to reduce the imbalances between treatment groups by weighting the groups to look alike on the observed confounders. There are many methods available to estimate PS and balancing weights. However, it is unclear a priori which will achieve the best trade-off between covariate balance and effective sample size. Weighted analyses are further complicated by small studies with limited sample sizes, which is common when studying rare diseases. To address these issues, we present a step-by-step guide to covariate balancing strategies, including how to evaluate overlap, obtain estimates of PS and balancing weights, check for covariate balance, and assess sensitivity to unobserved confounding. We compare the performance of a number of commonly used estimation methods on a synthetic data set based on the Physical Activity and Exercise Outcomes in Huntington Disease (PACE-HD) study, which explored whether enhanced physical activity affects the progression and severity of the disease. We provide general guidelines for the choice of method for estimation of PS and balancing weights, interpretation, and sensitivity analysis of results. We also present R code for implementing the different methods and assessing balance.
Machine Learning Evaluation of the Echo-Chamber Effect in Medical Forums

We propose the Echo-Chamber Effect assessment of an online forum. Sentiments perceived by the forum readers are at the core of the analysis; a complete message is the unit of the study. We build 14 models and apply those to represent discussions gathered from an online medical forum. We use four multi-class sentiment classification applications and two Machine Learning algorithms to evaluate prowess of the assessment models.
Studying Visualization Guidelines According to Grounded Theory

Visualization guidelines, if defined properly, are invaluable to both practical applications and the theoretical foundation of visualization. In this paper, we present a collection of research activities for studying visualization guidelines according to Grounded Theory (GT). We used the discourses at VisGuides, which is an online discussion forum for visualization guidelines, as the main data source for enabling data-driven research processes as advocated by the grounded theory methodology. We devised a categorization scheme focusing on observing how visualization guidelines were featured in different threads and posts at VisGuides, and coded all 248 posts between September 27, 2017 (when VisGuides was first launched) and March 13, 2019. To complement manual categorization and coding, we used text analysis and visualization to help reveal patterns that may have been missed by the manual effort and summary statistics. To facilitate theoretical sampling and negative case analysis, we made an in-depth analysis of the 148 posts (with both questions and replies) related to a student assignment of a visualization course. Inspired by two discussion threads at VisGuides, we conducted two controlled empirical studies to collect further data to validate specific visualization guidelines. Through these activities guided by grounded theory, we have obtained some new findings about visualization guidelines.
Spectral POD analysis of the turbulent wake of a disk at Re = 50, 000.

The coherent structures in the turbulent wake of a disk at a moderately high Reynolds number ($\Rey$) of $50,000$ are examined using spectral proper orthogonal decomposition (SPOD) which considers all three velocity components in a numerical database. The SPOD eigenvalues at a given streamwise ($x$) location are functions of azimuthal wavenumber ($m$), frequency ($\Str$), and SPOD index ($n$). By $x/D =10$, two specific modes dominate the fluctuation energy: (i) the vortex shedding (VS) mode with $m=1, \Str =0.135, n=1$, and (ii) the double helix (DH) mode with $m=2, \Str \rightarrow 0, n=1$. The VS mode is more energetic than the DH mode in the near wake but, in the far wake, it is the DH mode which is dominant. The DH mode, when scaled with local turbulent velocity and length scales, shows self-similarity in eigenvalues and eigenmodes while the VS mode, which is a global mode, does not exhibit strict self-similarity. Modes $m = 0$, 3 and 4, although subdominant, also make a significant net contribution to the fluctuation energy, and their eigenspectra are evaluated. The reconstruction of TKE and Reynolds shear stress, $\langle u'_{x} u'_{r} \rangle$, is evaluated by varying $(m,\Str,n)$ combinations. Higher SPOD modes contribute significantly to the TKE, especially near the centerline. In contrast, reconstruction of $\langle u'_{x}u'_{r}\rangle $ requires far fewer modes: $|m| \leq 4 $, $|\Str| \leq 1$ and $n \leq 3$. Among azimuthal modes, $m=1$ and $2$ are the leading contributors to both TKE and $\langle u'_{x}u'_{r} \rangle $. While $m=1$ captures the slope of the shear-stress profile near the centerline, $m=2$ is important to capture $\langle u'_{x}u'_{r} \rangle $ at and near its peak. SPOD is also performed in the vicinity of the disk to describe the modal transition to the principal contributors in the wake.
Learning Dexterous Manipulation from Suboptimal Experts

Learning dexterous manipulation in high-dimensional state-action spaces is an important open challenge with exploration presenting a major bottleneck. Although in many cases the learning process could be guided by demonstrations or other suboptimal experts, current RL algorithms for continuous action spaces often fail to effectively utilize combinations of highly off-policy expert data and on-policy exploration data. As a solution, we introduce Relative Entropy Q-Learning (REQ), a simple policy iteration algorithm that combines ideas from successful offline and conventional RL algorithms. It represents the optimal policy via importance sampling from a learned prior and is well-suited to take advantage of mixed data distributions. We demonstrate experimentally that REQ outperforms several strong baselines on robotic manipulation tasks for which suboptimal experts are available. We show how suboptimal experts can be constructed effectively by composing simple waypoint tracking controllers, and we also show how learned primitives can be combined with waypoint controllers to obtain reference behaviors to bootstrap a complex manipulation task on a simulated bimanual robot with human-like hands. Finally, we show that REQ is also effective for general off-policy RL, offline RL, and RL from demonstrations. Videos and further materials are available at this http URL.
Generalized Intersection Algorithms with Fixpoints for Image Decomposition Learning

In image processing, classical methods minimize a suitable functional that balances between computational feasibility (convexity of the functional is ideal) and suitable penalties reflecting the desired image decomposition. The fact that algorithms derived from such minimization problems can be used to construct (deep) learning architectures has spurred the development of algorithms that can be trained for a specifically desired image decomposition, e.g. into cartoon and texture. While many such methods are very successful, theoretical guarantees are only scarcely available. To this end, in this contribution, we formalize a general class of intersection point problems encompassing a wide range of (learned) image decomposition models, and we give an existence result for a large subclass of such problems, i.e. giving the existence of a fixpoint of the corresponding algorithm. This class generalizes classical model-based variational problems, such as the TV-l2 -model or the more general TV-Hilbert model. To illustrate the potential for learned algorithms, novel (non learned) choices within our class show comparable results in denoising and texture removal.
Multi-hop Question Generation with Graph Convolutional Network

Multi-hop Question Generation (QG) aims to generate answer-related questions by aggregating and reasoning over multiple scattered evidence from different paragraphs. It is a more challenging yet under-explored task compared to conventional single-hop QG, where the questions are generated from the sentence containing the answer or nearby sentences in the same paragraph without complex reasoning. To address the additional challenges in multi-hop QG, we propose Multi-Hop Encoding Fusion Network for Question Generation (MulQG), which does context encoding in multiple hops with Graph Convolutional Network and encoding fusion via an Encoder Reasoning Gate. To the best of our knowledge, we are the first to tackle the challenge of multi-hop reasoning over paragraphs without any sentence-level information. Empirical results on HotpotQA dataset demonstrate the effectiveness of our method, in comparison with baselines on automatic evaluation metrics. Moreover, from the human evaluation, our proposed model is able to generate fluent questions with high completeness and outperforms the strongest baseline by 20.8% in the multi-hop evaluation. on. The code is publicly availableat https://github.com/HLTCHKU
Hybrid Beamforming and Adaptive RF Chain Activation for Uplink Cell-Free Millimeter-Wave Massive MIMO Systems

In this work, we investigate hybrid analog–digital beamforming (HBF) architectures for uplink cell-free (CF) millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. We first propose two HBF schemes, namely, decentralized HBF (D-HBF) and semi-centralized HBF (SC-HBF). In the former, both the digital and analog beamformers are generated independently at each AP based on the local channel state information (CSI). In contrast, in the latter, only the digital beamformer is obtained locally at the access point (AP), whereas the analog beamforming matrix is generated at the central processing unit (CPU) based on the global CSI received from all APs. We show that the analog beamformers generated in these two HBF schemes provide approximately the same achievable rates despite the lower complexity of D-HBF and its lack of CSI requirement. Furthermore, to reduce the power consumption, we propose a novel adaptive radio frequency (RF) chain-activation (ARFA) scheme, which dynamically activates/deactivates RF chains and their connected analog-to-digital converters (ADCs) and phase shifters (PSs) at the APs based on the CSI. For the activation of RF chains, low-complexity algorithms are proposed, which can achieve significant improvement in energy efficiency (EE) with only a marginal loss in the total achievable rate.
RobustBench: a standardized adversarial robustness benchmark

Evaluation of adversarial robustness is often error-prone leading to overestimation of the true robustness of models. While adaptive attacks designed for a particular defense are a way out of this, there are only approximate guidelines on how to perform them. Moreover, adaptive evaluations are highly customized for particular models, which makes it difficult to compare different defenses. Our goal is to establish a standardized benchmark of adversarial robustness, which as accurately as possible reflects the robustness of the considered models within a reasonable computational budget. This requires to impose some restrictions on the admitted models to rule out defenses that only make gradient-based attacks ineffective without improving actual robustness. We evaluate robustness of models for our benchmark with AutoAttack, an ensemble of white- and black-box attacks which was recently shown in a large-scale study to improve almost all robustness evaluations compared to the original publications. Our leaderboard, hosted at this http URL, aims at reflecting the current state of the art on a set of well-defined tasks in $\ell_\infty$- and $\ell_2$-threat models with possible extensions in the future. Additionally, we open-source the library this http URL that provides unified access to state-of-the-art robust models to facilitate their downstream applications. Finally, based on the collected models, we analyze general trends in $\ell_p$-robustness and its impact on other tasks such as robustness to various distribution shifts and out-of-distribution detection.
Investigating Orientation Effects Considering Angular Resolution for a Sample of Radio-loud Quasars Using VLA Observations

Radio core dominance measurements, an indicator of jet orientation, sometimes rely on core flux density measurements from large-area surveys like Faint Images of the Radio Sky at Twenty cm (FIRST) that have an angular resolution of only 5″. Such low-resolution surveys often fail to resolve cores from the extended emission, resulting in an erroneous measurement. We focus on investigating this resolution effect for a sample of 119 radio-loud quasars. We obtained continuum observations from NSF’s Karl G. Jansky Very Large Array (VLA) at 10 GHz in A configuration with a 0. ″2 resolution. Our measurements show that at FIRST spatial resolution, core flux measurements are indeed systematically high even after considering the core variability. For a handful of quasars, 10 GHz images reveal extended features, whereas the FIRST image shows a point source. We found that the resolution effect is more prominent for quasars with smaller angular sizes. We further computed two radio core dominance parameters R and R5100 for use in statistical orientation investigations with this sample. We also present the spectral energy distributions between 74 MHz and 1.4 GHz, which we used to measure the spectral index of the extended emission of these quasars. Our results empirically confirm that determination of radio core dominance requires high spatial resolution data. We highlight the practical issues associated with the choice of frequency and resolution in the measurement of core and extended flux densities.
Continual Unsupervised Domain Adaptation with Adversarial Learning

Unsupervised Domain Adaptation (UDA) is essential for autonomous driving due to a lack of labeled real-world road images. Most of the existing UDA methods, however, have focused on a single-step domain adaptation (Synthetic-to-Real). These methods overlook a change in environments in the real world as time goes by. Thus, developing a domain adaptation method for sequentially changing target domains without catastrophic forgetting is required for real-world applications. To deal with the problem above, we propose Continual Unsupervised Domain Adaptation with Adversarial learning (CUDA^2) framework, which can generally be applicable to other UDA methods conducting adversarial learning. CUDA^2 framework generates a sub-memory, called Target-specific Memory (TM) for each new target domain guided by Double Hinge Adversarial (DHA) loss. TM prevents catastrophic forgetting by storing target-specific information, and DHA loss induces a synergy between the existing network and the expanded TM. To the best of our knowledge, we consider realistic autonomous driving scenarios (Synthetic-to-Real-to-Real) in UDA research for the first time. The model with our framework outperforms other state-of-the-art models under the same settings. Besides, extensive experiments are conducted as ablation studies for in-depth analysis.
Warrior1: A Performance Sanitizer for C++

This paper presents Warrior1, a tool that detects performance anti-patterns in C++ libraries. Many programs are slowed down by many small inefficiencies. Large-scale C++ applications are large, complex, and developed by large groups of engineers over a long period of time, which makes the task of identifying inefficiencies difficult. Warrior1 was designed to detect the numerous small performance issues that are the result of inefficient use of C++ libraries. The tool detects performance anti-patterns such as map double-lookup, vector reallocation, short lived objects, and lambda object capture by value. Warrior1 is implemented as an instrumented C++ standard library and an off-line diagnostics tool. The tool is very effective in detecting issues. We demonstrate that the tool is able to find a wide range of performance anti-patterns in a number of popular performance sensitive open source projects.
Threats and Corrective Measures for IoT Security with Observance to Cybercrime

Internet of Things (IoT) is the utmost assuring framework to facilitate human life with quality and comfort. IoT has contributed significantly in numerous application areas. The stormy expansion of smart devices and their credence for data transfer on wireless mechanics boosts their susceptibility to cyber-attacks. Consequently, the rate of cybercrimes is increasing day by day. Hence, the study of IoT security threats and possible corrective measures can benefit the researchers to identify appropriate solutions to deal with various challenges in cybercrime investigation. IoT forensics plays a vital role in cybercrime investigation. This review paper presents an overview of the IoT framework consisting of IoT architecture, protocols, and technologies. Various security issues at each layer and corrective measures are also discussed in detail. This paper also presents the role of IoT forensics in cybercrime investigation in various domains like smart homes, smart cities, automated vehicles, healthcare, etc. Along with the role of advanced technologies like Artificial Intelligence, Machine Learning, Cloud computing, Edge computing, Fog computing, and Blockchain technology in cybercrime investigation are also discussed. At last, the various open research challenges in the area of IoT to assist cybercrime investigation are explained, which provide a new direction to the researchers to work further.
The roles of Hypoxia Inducible Factor-1, Glutamate and their interaction in Ischemic Tolerance

iii List of Abbreviations . viii List of Figures and Tables. xii Chapter 1: Introduction . 1 1.1 Background and Significance . 1 1.2 Treatments for ischemic stroke. 6 1.3 Protective Mechanisms that occur in ischemic tolerance . 7 1.3.1 Excitatory neurotransmitters and receptors: . 7 1.3. 2 Mitochondria function . 11 1.3. 3 Heat Shock Response and protein degradation. 12 1.3. 4 Hypoxiainducible factor . 13 1.3. 5 Reactive oxygen species and defense mechanisms against oxidative stress . 14 1.3. 6 Glial Ischemic tolerance. 15 1.4 Potential clinical applications . 16 1.4. 1 Ischemic preconditioning . 16 1.4. 2 Ischemic postconditioning . 17 1.4. 4 Stimulus Inducing Tolerance . 19 1.5 In Summary . 21 1.6 References. 23 Chapter 2: A comparative study: effects of proteasome and hydroxylation inhibitions on hypoxia inducible factor 1α degradation in cerebral ischemia . 40 2.1 ABSTRACT . 41 2.2 INTRODUCTION. 42 2.3 MATERIALS AND METHODS . 44 2.4 RESULTS. 48 2.5 DISCUSSION . 61 2.6 REFERENCES. 68 Chapter 3: Hypoxiainducible factor 1 protects hypoxic astrocytes against glutamate toxicity. 76 3.1 ABSTRACT . 77
Regulatory Implications of Non-Trivial Splicing of Human Proteins

Alternative splicing often affects structured and highly conserved regions of proteins, generating so called non-trivial splicing variants of unknown structure and cellular function. A respective splice variant of Rab1A lacks nearly 40 % of the sequence of the native Rab1A protein, including most of the regulatory interaction sites. However, this variant represents a stable, folded protein with remarkably altered functional characteristics. Overall the results reveal a new regulatory mechanism for human Rab1A in vesicular transport via alternative non-trivial splicing.
Search for Off-pulse Emission in Long-period Pulsars

We have revisited the problem of off-pulse emission in pulsars, where a detailed search for the presence of low-level radio emission outside the pulse window is carried out. The presence of off-pulse emission was earlier reported in two long-period pulsars, PSR B0525+21 and B2046–16, at frequencies below 1 GHz using the Giant Metrewave Radio Telescope (GMRT). However, subsequent studies did not detect off-pulse emission from these pulsars at higher radio frequencies (>1 GHz). We have carefully inspected the analysis scheme used in the earlier detections and found an anomaly with data editing routines used, which resulted in leakage of signal from the on-pulse to the off-pulse region. We show that the earlier detections from PSR B0525+21 and B2046–16 were a result of this leakage. The above analysis scheme has been modified and offline gating has been used to search for off-pulse emission in 21 long-period pulsars (P > 1.2 s) at different observing frequencies of GMRT. The presence of low-level off-pulse emission of the peak flux 0.5 mJy was detected in the brightest pulsar in this list, PSR 0B0628–28, with an off-pulse to average pulsar flux ratio of 0.25%. We suggest that coherent radio emission resulting due to cyclotron resonance near the light cylinder can be a possible source for the off-pulse emission in this pulsar.
A Finite Agent Equilibrium in an Incomplete Market and its Strong Convergence to the Mean-Field Limit

We investigate the problem of equilibrium price formation in an incomplete securities market. Each financial firm (agent) tries to minimize its cost via continuous-time trading with a securities exchange while facing the systemic and idiosyncratic noises as well as the stochastic order-flows from its over-the-counter clients. We have shown, in the accompanying paper (Fujii-Takahashi (2020)), that the solution to a certain forward backward stochastic differential equation of conditional McKean-Vlasov type gives a good approximate of the equilibrium price which clears the market in the large population limit. In this work, we prove the existence of a unique market clearing equilibrium among the heterogeneous agents of finite population size. We show the strong convergence to the corresponding mean-field limit under suitable conditions. In particular, we provide the stability relation between the market clearing price for the heterogeneous agents and that for the homogeneous mean-field limit.
A Self-supervised Cascaded Refinement Network for Point Cloud Completion

Point clouds are often sparse and incomplete, which imposes difficulties for real-world applications, such as 3D object classification, detection and segmentation. Existing shape completion methods tend to generate coarse shapes of objects without fine-grained details. Moreover, current approaches require fully-complete ground truth, which are difficult to obtain in real-world applications. In view of these, we propose a self-supervised object completion method, which optimizes the training procedure solely on the partial input without utilizing the fully-complete ground truth. In order to generate high-quality objects with detailed geometric structures, we propose a cascaded refinement network (CRN) with a coarse-to-fine strategy to synthesize the complete objects. Considering the local details of partial input together with the adversarial training, we are able to learn the complicated distributions of point clouds and generate the object details as realistic as possible. We verify our self-supervised method on both unsupervised and supervised experimental settings and show superior performances. Quantitative and qualitative experiments on different datasets demonstrate that our method achieves more realistic outputs compared to existing state-of-the-art approaches on the 3D point cloud completion task.