Miracle-dz/UniFAR-FTUs huggingface dataset:
Co-Scheduling on Fused CPU-GPU Architectures With Shared Last Level Caches

Fused CPU-GPU architectures integrate a CPU and general-purpose GPU on a single die. Recent fused architectures even share the last level cache (LLC) between CPU and GPU. This enables hardware-supported byte-level coherency. Thus, CPU and GPU can execute computational kernels collaboratively, but novel methods to co-schedule work are required. This paper contributes three dynamic co-scheduling methods. Two of our methods implement workers that autonomously acquire work from a common set of independent work items (similar to bag-of-tasks scheduling). The third method, <italic>host-side profiling</italic>, uses a fraction of the total work of a kernel to determine a ratio of how to distribute work to CPU and GPU based on profiling. The resulting ratio is used for the following executions of the same kernel. Our methods are realized using OpenCL 2.0, which introduces fine-grained shared virtual memory (SVM) to allocate coherent memory between CPU and GPU. We port the Rodinia Benchmark Suite, a standard suite for heterogeneous computing, to fine-grained SVM and fused CPU-GPU architectures (<italic>Rodinia-SVM</italic>). We evaluate the overhead of fine-grained SVM and analyze the suitability of OpenCL 2.0’s new features for co-scheduling. Our host-side profiling method performs competitively to the optimal choice of executing kernels <italic>either</italic> on CPU <italic>or</italic> GPU (hypothetical xor-Oracle). On average, it achieves 97% of xor-Oracle’s performance and a <inline-formula> <tex-math notation="LaTeX">$1.43\times$ </tex-math></inline-formula> speedup over using the GPU alone (standard in Rodinia). We show, however, that in most cases it is not beneficial to split the work of a kernel between CPU and GPU compared to exclusively running it on the most suitable single compute device. For a fixed amount of work per device, cache-related stalls can increase by up to <inline-formula> <tex-math notation="LaTeX">$1.75\times$ </tex-math></inline-formula> when both devices are used in parallel instead of exclusively while cache misses remain the same. Thus, not the cost of cache conflicts, but inefficient cache coherence is a major performance bottleneck for current fused CPU-GPU Intel architectures with shared LLC.
Data-Driven Output Feedback Optimal Control for a Class of Nonlinear Systems via Adaptive Dynamic Programming Approach: Part I-Algorithms

Approximate/adaptive dynamic programming (ADP) has demonstrated great successes in the construction of data-driven output feedback optimal control for linear time-invariant systems and data-driven state feedback optimal control for nonlinear systems. This work investigates data-driven output feedback optimal control design for a class of nonlinear systems. It proposes to parameterize all admissible output feedback optimal control policies over accessible signals (system output and its time derivatives). In the case that system state can be parameterized as functions of accessible signals, then the value function and control policy can be parameterized over accessible signals, which allow ADP to be driven by accessible data. For a special case, where system state, value function and control policy can be linearly parameterized over a finite functional space over accessible signals, the policy iteration algorithm (PI) of ADP is reduced to solve a system of linear equations. Two data-driven PIs are developed to accomplish data-driven output feedback optimal control design. Simulation validates the proposed methodology.
Category-level Object Models to Enhance Monocular SLAM

The thesis presents a novel framework for monocular object-based simultaneous localization and mapping (SLAM) in quasi-static scenes. This presents a new paradigm for real-time object-oriented SLAM with a monocular camera. Contrary to previous approaches, that rely on object-level models, we construct category-level models from CAD collections which are now widely available. To alleviate the need for huge amounts of labeled data, we develop a rendering pipeline that enables synthesis of large data sets from a limited amount of manually labeled data. Using data thus synthesized, we learn category-level models for object deformations in 3D, as well as discriminative object features in 2D. These category models are instance-independent and aid in the design of object landmark observations that can be incorporated into a generic monocular SLAM framework. Where typical object-SLAM approaches usually solve only for object and camera poses, we also estimate object shape on-the-fly, allowing for a wide range of objects from the category to be present in the scene. Moreover, since our 2D object features are learned discriminatively, the proposed object-SLAM system succeeds in several scenarios where sparse feature-based monocular SLAM fails due to insufficient features or parallax. Also, the proposed category-models help in object instance retrieval, useful for Augmented Reality (AR) applications. We evaluate the proposed framework on multiple challenging real-world scenes and show — to the best of our knowledge — first results of an instance-independent monocular object-SLAM system and the benefits it enjoys over feature-based SLAM methods.
Protégé Perceptions of High Mentorship Quality Depend on Shared Values More Than on Demographic Match

ABSTRACT Mentoring, particularly same-gender and same-race mentoring, is increasingly seen as a powerful method to attract and retain more women and racial minorities into science, technology, engineering, and mathematics (STEM) education and careers. This study examines elements of a mentoring dyad relationship (i.e., demographic and perceived similarity of values) that influenced the perceived quality of mentorship, as well as the effect of mentorship on STEM career commitment. A national sample of African American undergraduates majoring in STEM disciplines were surveyed in their senior year. Overall, perceived similarity, rather than demographic similarity of values, was the most important factor associated with protégé perceptions of high-quality mentorship, which in turn was associated with higher commitment to STEM careers. We discuss the implications for mentoring underrepresented students and broadening participation in STEM.
Exploiting the ODD framework to define a novel effective graph kernel

In this paper, we show how the Ordered Decomposition DAGs kernel framework, a framework that allows the denition of graph kernels from tree kernels, allows to easily dene new state-of-the-art graph kernels. Here we consider a quite fast graph kernel based on the Subtree kernel (ST), and we improve it by increasing its expressivity by adding new features involving partial tree features. While the worst-case complexity of the new obtained graph kernel does not increase, its eectiveness is improved, as shown on several chemical datasets, reaching state-of-the-art performances.
Stabilization of an Unstable Wave Equation Using an Infinite Dimensional Dynamic Controller

This paper deals with the stabilization of an anti-stable string equation with Dirichlet actuation where the instability appears because of the uncontrolled boundary condition. Then, infinitely many unstable poles are generated and an infinite dimensional control law is therefore proposed to exponentially stabilize the system. The idea behind the choice of the controller is to extend the domain of the PDE so that the anti-damping term is compensated by a damping at the other boundary condition. Additionally, notice that the system can then be exponentially stabilized with a chosen decay-rate and is robust to uncertainties on the wave speed and the anti-damped coefficient of the wave equation, with the only use of a point-wise boundary measurement. The efficiency of this new control strategy is then compared to the backstepping approach.
Duality-based Asymptotic-Preserving method for highly anisotropic diffusion equations

The present paper introduces an efficient and accurate numerical scheme for the solution of a highly anisotropic elliptic equation, the anisotropy direction being given by a variable vector field. This scheme is based on an asymptotic preserving reformulation of the original system, permitting an accurate resolution independently of the anisotropy strength and without the need of a mesh adapted to this anisotropy. The counterpart of this original procedure is the larger system size, enlarged by adding auxiliary variables and Lagrange multipliers. This Asymptotic-Preserving method generalizes the method investigated in a previous paper [arXiv:0903.4984v2] to the case of an arbitrary anisotropy direction field.
Capturing change in clonal composition amongst single mouse germinal centers

Understanding cellular processes occurring in vivo on time scales of days to weeks requires repeatedly interrogating the same tissue without perturbing homeostasis. We describe a novel setup for longitudinal intravital imaging of murine peripheral lymph nodes (LNs). The formation and evolution of single germinal centers (GCs) was visualized over days to weeks. Naïve B cells encounter antigen and form primary foci, which subsequently seed GCs. These experience widely varying rates of homogenizing selection, even within closely confined spatial proximity. The fluidity of GCs is greater than previously observed with large shifts in clonality over short time scales; and loss of GCs is a rare, observable event. The observation of contemporaneous, congruent shifts in clonal composition between GCs within the same animal suggests inter-GC trafficking of memory B cells. This tool refines approaches to resolving immune dynamics in peripheral LNs with high temporospatial resolution and minimal perturbation of homeostasis.
Long-Run Wavelet-Based Correlation for Financial Time Series

Abstract The asset allocation decision often relies upon correlation estimates arising from short-run data. Short-run correlation estimates may, however, be distorted by frictions. In this paper, we introduce a long-run wavelet-based correlation estimator, distinguishing between long-run common behavior and short-run singular events. Using generated data, we demonstrate a reduction in bias and error of up to 84.2% and 38.9%, respectively, relative to a traditional subsampled approach. Exploiting the wavelet decomposition into short- and long-run components, we develop a model to help understand the sources of any heterogeneity in correlation. The implication is that short-run correlation may be downward biased by frictions, the latter manifesting as serial- and cross-serial correlation in the raw time series. In an empirical application to G7 international equity markets, we present evidence of increasing correlations at longer-run horizons. The significance for the asset allocation decision are examined using a minimum-variance framework, highlighting distinct optimal allocation weights at short- and long-run horizons.
Using Game-Based Learning with Kinect Technology in Foreign Language Education Course

The present study investigates the effects of game-based language learning with Kinect technology on students’ self-efficacy beliefs and attitudes toward English. The study was conducted at a state university located in Central Turkey. Foreign language education is promoted in Turkey, yet because of several factors, among which weekly hours of teaching, and student motivation prevail, and students still cannot develop their language skills. The literature shows that these problems might be minimized making use of technological advancements effectively in the classroom. Therefore, game-based language learning, through which students carry out meaningful tasks based on real-life scenarios to develop their communication skills, is getting an increasing attention. In this study, a pre-test/post-test quasi-experimental study was conducted. Participants were the first-year students taking English as a compulsory course. In total, there were 62 participants. The instruments included two questionnaires measuring self-efficacy beliefs and attitude toward English. The results revealed that there was a significant positive increase in some sub-factors of attitude and self-efficacy scores of the students in the experimental group compared to the average scores of the students in the control group. The study explains why game-based learning activities with Kinect technology should be integrated into the foreign language courses
FADE: Fast and Asymptotically Efficient Distributed Estimator for Dynamic Networks

Consider a set of agents that wish to estimate a vector of parameters. For this estimation goal, each agent can measure (in additive Gaussian noise) linear combinations of the unknown vector of parameters and can broadcast information to a few other neighbors. To coordinate the agents, we propose a distributed algorithm called FADE (fast and asymptotically efficient distributed estimator). FADE enjoys five attractive features: first, it is simple to derive; second, it withstands dynamic networks; third, it is strongly consistent with each agent's estimate converging (almost surely) to the true vector of parameters; fourth, it is both asymptotically unbiased and efficient with each agent's estimate becoming unbiased and the mean square error (MSE) of each agent's estimate vanishing to zero at the same rate of the MSE of the optimal centralized estimator; and finally, when compared with the popular consensus+innovation algorithm, it yields estimates with MSEs that, for some networks, can be orders of magnitude smaller.
An Embodied Conversational Agent for Unguided Internet-Based Cognitive Behavior Therapy in Preventative Mental Health: Feasibility and Acceptability Pilot Trial

Background Recent years have seen an increase in the use of internet-based cognitive behavioral therapy in the area of mental health. Although lower effectiveness and higher dropout rates of unguided than those of guided internet-based cognitive behavioral therapy remain critical issues, not incurring ongoing human clinical resources makes it highly advantageous. Objective Current research in psychotherapy, which acknowledges the importance of therapeutic alliance, aims to evaluate the feasibility and acceptability, in terms of mental health, of an application that is embodied with a conversational agent. This application was enabled for use as an internet-based cognitive behavioral therapy preventative mental health measure. Methods Analysis of the data from the 191 participants of the experimental group with a mean age of 38.07 (SD 10.75) years and the 263 participants of the control group with a mean age of 38.05 (SD 13.45) years using a 2-way factorial analysis of variance (group × time) was performed. Results There was a significant main effect (P=.02) and interaction for time on the variable of positive mental health (P=.02), and for the treatment group, a significant simple main effect was also found (P=.002). In addition, there was a significant main effect (P=.02) and interaction for time on the variable of negative mental health (P=.005), and for the treatment group, a significant simple main effect was also found (P=.001). Conclusions This research can be seen to represent a certain level of evidence for the mental health application developed herein, indicating empirically that internet-based cognitive behavioral therapy with the embodied conversational agent can be used in mental health care. In the pilot trial, given the issues related to feasibility and acceptability, it is necessary to pursue higher quality evidence while continuing to further improve the application, based on the findings of the current research.
Impact of Progression of Parkinson's Disease and Various Other Factors on Generalized Anxiety Disorder

ABSTRACT Objective: While much research has been conducted toward understanding the relationship between prevalence of Parkinson's disease (PD) and generalized anxiety, little has been done considering additional influential factors in the relationship by means of a large ethnically diverse sample. Our study strives to fulfill these deficits in the literature as we set out to determine the impact of progression of PD, age, gender, and Hoehn and Yahr (H and Y) staging of PD on generalized anxiety. Methods: A retrospective chart review analysis was performed on PD patients who were regularly examined in a community-based PD and movement disorders center from 2005 to 2010. Results: This study consisted of 310 patients with PD among whom 12% had generalized anxiety. Neither age nor gender was significant onset predictors at P = 0.05. The impact of progression of H and Y Stages 2–3 and 2–4 increased the odds of generalized anxiety disorder (GAD) prevalence though it was statistically insignificant at P = 0.05. Conclusions: Clinicians should not expect the risk of developing anxiety to depend on gender nor change as a function of age though it may increase with symptomatic progression of PD as outlined by H and Y. To the best of our knowledge, this is the largest and most ethnically diverse prevalence study with a focus on generalized anxiety and PD. Significant Outcomes and Limitations: The symptomatic progression of PD, but not age or gender, may be associated with an increased risk for GAD. This study lacked adjustment for potential confounders such as depression and PD medications.
Identifying Differentially Expressed MicroRNAs Between Cirrhotic and Non-Cirrhotic Hepatocellular Carcinoma and Exploring Their Functions Using Bioinformatic Analysis

Background/Aims: Few studies have been designed to directly investigate the exact mechanisms underlying the different phenotypes between cirrhotic and non-cirrhotic hepatocellular carcinoma (HCC). This study aimed to illuminate the incidence and developmental mechanisms for both types of HCC through differentially expressed microRNAs (miRNAs) using bioinformatic analysis. Methods: The miRNA-seq data and clinical data of patients (from The Cancer Genome Atlas (TCGA) database) were utilized to determine differentially expressed miRNAs between cirrhotic and non-cirrhotic HCC. Afterwards, the function of the selected miRNAs was predicted with online tools. Furthermore, the miRNA expression and clinical significance were validated by external datasets and our experiment. Results: The present study included 135 non-cirrhotic and 80 cirrhotic patients to find differentially expressed miRNAs. Among them, four miRNAs (mir-1296, mir-23c, mir-149, and mir-95) were finally selected for further validation and analysis. Correlation analysis found that two miRNAs are greatly associated with the non-cirrhotic HCC patients’ clinical traits, such as the T, M, and N tumor stages. However, only mir-23c was associated with the cirrhotic HCC patients’ tumor T and N stages. Furthermore, survival analysis revealed that increased mir-149 in non-cirrhotic HCC, patients’ age and the existence of vessels in the tumor in cirrhotic HCC were independent risk factors for the patients’ postoperative overall survival. Functional and interaction analyses also supported the notion that these miRNAs function through some pathways that influence the behavior of the HCC. These results are strongly supported by analysis of extra datasets and our experiment. Conclusions: The cirrhotic and non-cirrhotic HCC types involve differentially expressed miRNAs, which are correlated with distinctive pathological traits. To some extent, non-cirrhotic HCC seems more dependent on miRNA network regulation, but additional studies are needed to confirm this conclusion.
Scaling and Bias Codes for Modeling Speaker-Adaptive DNN-Based Speech Synthesis Systems

Most neural-network based speaker-adaptive acoustic models for speech synthesis can be categorized into either layer-based or input-code approaches. Although both approaches have their own pros and cons, most existing works on speaker adaptation focus on improving one or the other. In this paper, after we first systematically overview the common principles of neural-network based speaker-adaptive models, we show that these approaches can be represented in a unified framework and can be generalized further. More specifically, we introduce the use of scaling and bias codes as generalized means for speaker-adaptive transformation. By utilizing these codes, we can create a more efficient factorized speaker-adaptive model and capture advantages of both approaches while reducing their disadvantages. The experiments show that the proposed method can improve the performance of speaker adaptation compared with speaker adaptation based on the conventional input code.
New combinations for the Montana flora

Three new combinations are proposed. Agoseris carnea Rydb. and A. lackschewitzii Douglass M. Hend. & R.K. Moseley are considered the same entity and have been subsumed into A. aurantiaca (Hook.) Greene as a new infraspecific taxon. Artemisia lindleyana Besser has been treated as a distinct species, but a recent treatment subsumes it into A. ludoviciana Nutt. Similarities between the two entities are undeniable, but morphological and ecological differences lead me to believe that the former is best considered as a subspecies of the latter. Recent transfer of many New World asters into Symphyotrichum requires a new combination for Aster cusickii A. Gray if it is to be recognized as a subspecific taxon in Symphyotrichum foliaceum (Lindl. ex DC.) G.L. Nesom.
Ex Vivo Functionality of 3D Bioprinted Corneal Endothelium Engineered with Ribonuclease 5‐Overexpressing Human Corneal Endothelial Cells

Human corneal endothelial cells (HCECs) are scarcely proliferative in vivo. The cultured HCECs engineered to overexpress ribonuclease (RNase) 5 (R5‐HCECs) are prepared after transient transfection with RNase 5 plasmid vector. As candidate targets of R5‐HCECs for enhancement of cellular proliferation and survival of R5‐HCECs, programmed cell death protein 4 is inhibited, and cyclin D1 and cyclin E1 are activated. The cultured R5‐HCECs and control HCECs on lyophilized amniotic membrane (AM) are deposited as a carrier by extrusion‐based 3D bioprinting to prepare transplantable RNase 5 vector‐transfected HCECs–laden AM graft (R5‐Graft) and the control HCECs–laden AM graft (Ct‐Graft), respectively. The ready‐to‐use R5‐Graft shows clearer basolateral expression of Na+‐K+ ATPase pump and higher cell confluency than Ct‐Graft. From 2 weeks after graft transplantation, both R5‐Graft and Ct‐Graft start restoring clarity of the rabbit corneas, and their central corneal edema are much less than those in the control group at 3 and 4 weeks. The ex vivo expression of corneal endothelial phenotypical markers is clear in R5‐Grafs rather than in Ct‐Grafts at 4 weeks. In conclusion, the fabricated corneal endothelium with cultured HCECs easily survives and functions as corneal endothelium in vivo. Furthermore, the use of the cultured HCECs engineered to overexpress RNase 5 (R5‐HCECs) may be an option to obtain higher graft cellularity and to enhance the function of transplanted grafts.