Miracle-dz/UniFAR-FTUs huggingface dataset:
Communication architectures for scalable GPU-centric computing systems

In recent years, power consumption has become the main concern in High Performance Computing (HPC). This has lead to heterogeneous computing systems in which Central Processing Units (CPUs) are supported by accelerators, such as Graphics Processing Units (GPUs). While GPUs used to be seen as slave devices to which the main processor offloads computation, today’s systems tend to deploy more GPUs than CPUs. Eventually, the GPU will become a first-class processor, bearing increasing responsibilities. Promoting the GPU to a first-class processor comes with many challenges, such as progress guarantees, dynamic memory management, and scheduling. However, one of the main challenges is the GPU’s inability to orchestrate communication, which is currently entirely handled by the CPU. This work addresses that issue and presents solutions to allow GPUs to source and sink network traffic independently. Many important aspects are addressed, ranging from the application level to how networking hardware is accessed. First, important and large scale exascale applications are studied to further understand their communication behavior and applications’ requirements. Several metrics are presented, including time spent for communication, message sizes, and the length of queues that are required to match messages with receive requests. One aspect the analysis revealed is that messages are becoming smaller at scale, which renders the matching of messages and receive requests an important problem to address. The next part analyzes how the GPU can directly access the network with various communication models being presented and benchmarked. It is shown that a flat address space of distributed GPU memories shows superior bandwidth than put/get communication or CPU-controlled message passing, but less communication can be overlapped with computation. Overall, GPU-controlled communication is always superior, both in terms of time-to-solution and energy spending. The final part addresses communication management on GPUs, which is required to provide high-level communication abstractions. Besides other fundamental building blocks, an algorithm for the message matching is presented that yields similar performance as CPUs. However, it is also shown that the messaging protocol can be relaxed to improve performance significantly, leveraging the massive amount of parallelism provided by the GPU’s architecture.
Stereoscopic cloud base reconstruction using high-resolution whole sky imagers

Cloud base height and volume estimation is needed in meteorology and other applications. We have deployed a pair of custom-designed Whole Sky Imagers, which capture stereo pictures of the sky at regular intervals. Using these images, we propose a method to create rough 3D models of the base of clouds, using feature point detection, matching, and triangulation. The novelty of our method lies in the fact that it locates the cloud base in all three dimensions, instead of only estimating cloud base height. For validation, we compare the results with measurements from weather radar.
A New Method of Identifying Influential Users in the Micro-Blog Networks

Micro-blog services have become popular tools in the social networks. Online users discuss various topics in the micro-blog and some influential users can affect the opinions, attitudes, behaviors, or emotions of others. This paper proposes a user influence rank (UIRank) algorithm to identify the influential users through interaction information flow and interaction relationships among users in the micro-blog. The UIRank algorithm considers the contribution of user’s tweet and the characteristics of information dissemination in the micro-blog networks and calculates user influence score iteratively by user follower graph. Experimental results show that the UIRank algorithm outperforms other existing related algorithms in the precision, recall, and F1-Measure value.
pH-sensitive polymeric micelles for the Co-delivery of proapoptotic peptide and anticancer drug for synergistic cancer therapy

Mitochondria plays a vital role in a wide range of biological processes in human health and diseases. They are considered to be important organelles responsible for cellular apoptosis or programmed cell death. Therefore, the targeting of chemotherapy towards mitochondria would be highly desirable. Herein, we developed a pH-sensitive polymer that is designed for the subcellular co-delivery of anticancer drugs and therapeutic peptides to tumor cells. The amphiphilic copolymer poly(β-amino esters)-poly(ethylene glycol) was synthesized and conjugated with the dual-targeting proapoptotic peptide CGKRKD(KLAKLAK)2. The conjugate can self-assemble into a core–shell micellar structure at the physiological pH of 7.4. The anticancer drug docetaxel (DTX) was encapsulated inside the core of the micelles. The CGKRK peptide is specifically targeted to angiogenic blood vessels in tumors and tumor cells, whereby the micelles are efficiently internalized into tumor cells via an energy-dependent, lipid draft/caveolae-mediated endocytosis pathway. Once inside the acidic endosomal compartment, the stimuli-responsive micellar carriers disassemble and release both pharmacological agents. CGKRK efficiently transports D(KLAKLAK)2 towards mitochondria to trigger mitochondria-dependent apoptosis. DTX affects microtubulin for arresting the cancer cell cycle. Thus, the combination of DTX and the therapeutic peptide displayed a synergistic antitumor effect in an MCF-7 cell line.
Adaptive super resolution algorithm based on RBM dictionary learning

According to the SCSR (sparse coding sparse representation), algorithm based on general dictionary can not characterize the various structural types of image and global sparse reconstruction introduced these 2 shortcomings of redundant, proposed super-resolution algorithm adaptive decomposition based on MCA (morphological component analysis). This algorithm, first of all, using sparse K-SVD method to obtain the low resolution Dictionary of training for low resolution image reconstruction and down sampling as dictionary training samples, improved the correlation between low resolution images and reconstructed the dictionary. Secondly, in the reconstruction phase, the MCA method is used to extract the texture components of the image to reconstruct the sparse image. Experimental results show that compared with other advanced algorithms, the proposed algorithm is able to recover the image edge details better, and the reconstructed quality is better.
Automatic personality assessment in the wild

Analyzing personality of a subject is an important aspect of automatic human behavior understanding. Generally, estimation of the OCEAN (Openness, Conscientiousness, Extroversion, Agreeableness, Neuroticism) traits are used to represent the personality of an individual. Personality assessment based only on individual actions is not sufficient and considering social context is also important. The focus of this Ph.D. work is to explore automatic personality assessment (APA) in the real-world environment. Most of the work till now in this area has been focusing on BF and related traits prediction of the subject in lab-controlled environments. There are several challenges involved in moving from lab-controlled environments to real-world APA scenarios such as face tracking, illumination, occlusion and social context. The early work in this Ph.D. project explores the evolution of graphs, which capture the interaction patterns and structural changes of a group of people. We call them Personality Interaction Graphs (PIG). PIGs are constructed based on the nonverbal cues to study the behavior of a subject both at a group level and an individual level. This work brings in the power of PIGs to improve the prediction accuracy and visualization of the summary of personality traits with valid cause-effect analysis at both the individual and group level. Furthermore, various machine learning techniques to analyze the personality and emotion of subjects will be explored.
BSOI MOEMS

Micro-opto-electro-mechanical systems (MOEMS) typically require optical surfaces with high flatness and low roughness to be combined with high quality mechanical parts and low power, high force microactuators. In the past, attention has concentrated overwhelmingly on polysilicon surface micromachining, which allows an extremely flexible approach to the design of complex optical systems. However, polysilicon components typically suffer from poor surface flatness and high roughness, and lack the strength and rigidity to support and manipulate macroscopic optical components, which often have weights in the milligram range. Bonded silicon-on-insulator (BSOI) material provides an excellent alternative, allowing high optical quality to be combined with high mechanical strength. This paper will review a number of different approaches to BSOI MEMS, including deep reactive ion etching (for in-plane devices), double-sided processing (for through-wafer devices) and surface tension self-assembly (for 3D MOEMS). Applications ranging from variable optical attenuators to tunable laser systems will be described. Methods of mounting, aligning and fixing hybrid-integrated components will also be considered, together with appropriate high-force micro-actuators.
Developing, deploying, and operating twelve-factor applications with TOSCA

With Cloud Computing, offering and delivering services over the Internet became commonly feasible. This has impacts on application design, development as well as on the automation of application provisioning. The Twelve-Factor App is a methodology that documents best practices for building and operating scalable, maintainable, and portable web-based SaaS applications. However, a standards-based approach to build, release, and run Twelve-Factor Apps independently of individual cloud providers and specific deployment technologies is missing, which quickly leads to a vendor or technology lock-in. In this paper, we introduce a guideline to establish a development process using the Twelve-Factor App methodology together with the OASIS standard TOSCA to address this issue. We show how to realize the twelve factors with TOSCA and how the approach supports portability and automated deployment.
Improvement of 1.3-μm GaInNAs Vertical Cavity Surface Emitting Lasers Grown by MOVPE

We report the improved performance of the conventional contact 1.3 #m GalnNAs vertical cavity surface emitting lasers (VCSELs) grown by metal-organic vapour-phase epitaxy (MOVPE). A new wet etching approach was applied in the fabrication of 1.3μm GaInNAs oxide-confined VCSELs. The threshold current of single mode device is 1.0mA. The multiple mode devices show very low threshold currents below 2 mA at 5-85℃, which were the best results for 1.3μm GaInNAs VCSELs reported. Maximum single mode output power of 0.256mW and the maximum multiple mode power of 0.883mW were obtained at room temperature.
Effectiveness of Universal School-Based Mental Health Awareness Programs Among Youth in the United States: A Systematic Review.

BACKGROUND Stigmatizing attitudes toward mental illness and low mental health literacy have been identified as links to social adversity, and barriers to seeking and adhering to treatment among adolescents suffering from mental illness. Prior research has found that it is possible to improve these outcomes using school-based mental health awareness interventions. The purpose of this study was to review empirical literature pertaining to universal mental health awareness interventions aiming to improve mental health related outcomes among students enrolled in US K-12 schools, especially minorities vulnerable to health disparities. METHODS PsycINFO, Cochrane Library, PUBMED, and reference lists of relevant articles were searched for K-12 school-based mental health awareness interventions in the United States. Universal studies that measured knowledge, attitudes, and/or help-seeking pertinent to mental health were included. RESULTS A total of 15 studies were selected to be part of the review. There were 7 pretest/post-test case series, 5 nonrandomized experimental trial, 1 Solomon 4-groups, and 2 randomized controlled trial (RCT) designs. Nine studies measuring knowledge, 8 studies measuring attitudes, and 4 studies measuring help-seeking, indicated statistically significant improvements. CONCLUSIONS Although results of all studies indicated some level of improvement, more research on implementation of universal school-based mental health awareness programs is needed using RCT study designs, and long-term follow-up implementation.
Progress in research and development on MAX phases: a family of layered ternary compounds

The MAX phases are a group of layered ternary compounds with the general formula Mn+1AXn (M: early transition metal; A: group A element; X: C and/or N; n = 1–3), which combine some properties of metals, such as good electrical and thermal conductivity, machinability, low hardness, thermal shock resistance and damage tolerance, with those of ceramics, such as high elastic moduli, high temperature strength, and oxidation and corrosion resistance. The publication of papers on the MAX phases has shown an almost exponential increase in the past decade. The existence of further MAX phases has been reported or proposed. In addition to surveying this activity, the synthesis of MAX phases in the forms of bulk, films and powders is reviewed, together with their physical, mechanical and corrosion/oxidation properties. Recent research and development has revealed potential for the practical application of the MAX phases (particularly using the pressureless sintering and physical vapour deposition coating routes) as well as of MAX based composites. The challenges for the immediate future are to explore further and characterise the MAX phases reported to date and to make further progress in facilitating their industrial application.
Deep Nonlinear Metric Learning for 3-D Shape Retrieval

Effective 3-D shape retrieval is an important problem in 3-D shape analysis. Recently, feature learning-based shape retrieval methods have been widely studied, where the distance metrics between 3-D shape descriptors are usually hand-crafted. In this paper, motivated by the fact that deep neural network has the good ability to model nonlinearity, we propose to learn an effective nonlinear distance metric between 3-D shape descriptors for retrieval. First, the locality-constrained linear coding method is employed to encode each vertex on the shape and the encoding coefficient histogram is formed as the global 3-D shape descriptor to represent the shape. Then, a novel deep metric network is proposed to learn a nonlinear transformation to map the 3-D shape descriptors to a nonlinear feature space. The proposed deep metric network minimizes a discriminative loss function that can enforce the similarity between a pair of samples from the same class to be small and the similarity between a pair of samples from different classes to be large. Finally, the distance between the outputs of the metric network is used as the similarity for shape retrieval. The proposed method is evaluated on the McGill, SHREC’10 ShapeGoogle, and SHREC’14 Human shape datasets. Experimental results on the three datasets validate the effectiveness of the proposed method.
Biomedical applications of nanofibers

Nanofiber technology is an exciting area attracting the attention of many researchers as a potential solution to the current challenges in the biomedical field such as burn and wound care, organ repair, and treatment for osteoporosis and various diseases. Nanofibers are attractive in this field for several reasons. First, surface area on nanofibers is much higher compared to bulk materials, which allows for enhanced adhesion of cells, proteins, and drugs. Second, nanofibers can be fabricated into sophisticated macro-scale structures. The ability to fabricate nanofibers allows renewed efforts in developing hierarchical structures that mimic those in animals and human. On top of that, a wide range of polymers can be fabricated into nanofibers to suit different applications. Nanofibers are most commonly fabricated through electrospinning, which is a low cost method that allows control over fiber morphology and is capable of being scaled-up for mass production. This review explored two popular areas of biomedical nanofiber development: tissue regeneration and drug delivery, and included discussions on the basic principles for how nanofibers promote tissue regeneration and drug delivery, the parameters that affect nanofiber performance and the recent progress in these areas. The recent work on biomedical nanofibers showed that the large surface area on nanofibers could be translated into enhanced cell activities, drug encapsulation, and drug release rate control. Furthermore, by optimizing the electrospinning process via adjusting the material choices and fiber orientation, for example, further enhancement in cell differentiation and drug release control could be achieved. Copyright © 2010 John Wiley & Sons, Ltd.
Velocity estimation and control of 3-DOF lab helicopter based on optical flow

This paper presents an angular velocity estimation method based on optical flow to address the hovering and angle tracking control problem for a three degree-of-freedom (3-DOF) lab helicopter. An analytical expression of the travel angular velocity is deduced based on optical flow. Then, a controller consisting of a feedforward controller and a linear quadratic regulation (LQR) state feedback controller is designed. Experiment results on the lab helicopter demonstrate the validity of the angular velocity estimation method and the LQR control strategy.
Quantitative pulsed phase thermography applied to steel plates

Pulsed Phase Thermography (PPT) has been proven effective on depth retrieval of flat-bottomed holes in different materials such as plastics and aluminum. In PPT, amplitude and phase delay signatures are available following data acquisition (carried out in a similar way as in classical Pulsed Thermography), by applying a transformation algorithm such as the Fourier Transform (FT) on thermal profiles. The authors have recently presented an extended review on PPT theory, including a new inversion technique for depth retrieval by correlating the depth with the blind frequency fb (frequency at which a defect produce enough phase contrast to be detected). An automatic defect depth retrieval algorithm had also been proposed, evidencing PPT capabilities as a practical inversion technique. In addition, the use of normalized parameters to account for defect size variation as well as depth retrieval from complex shape composites (GFRP and CFRP) are currently under investigation. In this paper, steel plates containing flat-bottomed holes at different depths (from 1 to 4.5 mm) are tested by quantitative PPT. Least squares regression results show excellent agreement between depth and the inverse square root blind frequency, which can be used for depth inversion. Experimental results on steel plates with simulated corrosion are presented as well. It is worth noting that results are improved by performing PPT on reconstructed (synthetic) rather than on raw thermal data.
ZKSCAN1 gene and its related circular RNA (circZKSCAN1) both inhibit hepatocellular carcinoma cell growth, migration, and invasion but through different signaling pathways

There is increasing evidence that circular RNA (circRNA) are involved in cancer development, but the regulation and function of human circRNA remain largely unknown. In this study, we demonstrated that ZKSCAN1, a zinc finger family gene, is expressed in both linear and circular (circZKSCAN1) forms of RNA in human hepatocellular carcinoma (HCC) tissues and cell lines. Here, we analyzed a cohort of 102 patients and found that expression of both ZKSCAN1mRNA and circZKSCAN1 was significantly lower (P < 0.05) in the HCC samples compared with that in matched adjacent nontumorous tissues by reverse transcription PCR (RT‐PCR). The low expression level of ZKSCAN1 was only associated with tumor size (P = 0.032), while the cirZKSCAN1 levels varied in patients with different tumor numbers (P < 0.01), cirrhosis (P = 0.031), vascular invasion (P = 0.002), or microscopic vascular invasion (P = 0.002), as well as with the tumor grade (P < 0.001). Silencing both ZKSCAN1mRNA and circZKSCAN1 promoted cell proliferation, migration, and invasion. In contrast, overexpression of both forms of RNA repressed HCC progression in vivo and in vitro. Silencing or overexpression of both forms of RNA did not interfere with each other. RNA‐seq revealed a very different molecular basis for the observed effects; ZKSCAN1mRNA mainly regulated cellular metabolism, while circZKSCAN1 mediated several cancer‐related signaling pathways, suggesting a nonredundant role for ZKSCAN1mRNA and circRNA. In conclusion, our results revealed two post‐translational products (ZKSCAN1mRNA and circZKSCAN1) that cooperated closely with one another to inhibit growth, migration, and invasion of HCC. cirZKSCAN1 might be a useful marker for the diagnosis of HCC.
Investigating the impact of cache pollution attacks in heterogeneous cellular networks

With the growth of Internet-of-Things, mobile data traffic is expected to increase exponentially. To support this rapid growth, heterogeneous cellular networks comprising of femtocells with storage capabilities along with macrocell base stations have been proposed. In this paper, we first investigate the performance impact of a simple randomized cache pollution attack, where the attacker pollutes the cache at the femtocell by requesting unpopular content. We then adopt a principled approach based on the characteristic time of a content in a cache to design an optimized attack strategy. Our experiments show that the proposed attack strategy outperforms the randomized attack with the same attack rate.
Practical concurrent traversals in search trees

Operations of concurrent objects often employ optimistic concurrency-control schemes that consist of a traversal followed by a validation step. The validation checks if concurrent mutations interfered with the traversal to determine if the operation should proceed or restart. A fundamental challenge is to discover a necessary and sufficient validation check that has to be performed to guarantee correctness. In this paper, we show a necessary and sufficient condition for validating traversals in search trees. The condition relies on a new concept of succinct path snapshots, which are derived from and embedded in the structure of the tree. We leverage the condition to design a general lock-free membership test suitable for any search tree. We then show how to integrate the validation condition in update operations of (non-rebalancing) binary search trees, internal and external, and AVL trees. We experimentally show that our new algorithms outperform existing ones.