Miracle-dz/UniFAR-FTUs huggingface dataset:
GraphSteal: Dynamic Re-Partitioning for Efficient Graph Processing in Heterogeneous Clusters

With continuously growing data, clusters also need to grow periodically to accommodate the increased demand of data processing. This is usually done by addition of newer hardware, whose configuration might differ from the existing nodes. As a result, clusters are becoming heterogeneous in nature. For many real world machine learning and data mining applications, data is represented in the form of graphs. Most of the existing distributed graph processing frameworks such as Pregel and Graphlab assume that the computational nodes are homogeneous. These frameworks split the graph into approximately equal subgraphs, which is appropriate for homogeneous clusters. In heterogeneous clusters, these frameworks perform poorly in most of the scenarios. To the best of our knowledge, GraphIVE is the only heterogeneity-aware graph processing framework. It learns the relative capabilities of the nodes based on runtime metrics of previous jobs and partitions the graph proportionally. However, it may not perform well if a new job differs drastically in terms of resource requirements when compared to previous jobs executed on the cluster. To overcome this limitation, we propose GraphSteal, a dynamic graph re-partitioning policy for vertex-cut based graph processing frameworks on heterogeneous clusters. GraphSteal dynamically re-partitions the graph based on the runtime characteristics of the job. To avoid computational skew in the cluster, it migrates edges from slow nodes to fast nodes. To demonstrate our approach, we modify the source code of Graphlab to incorporate dynamic graph re-partitioning strategy. Experimental results show that GraphSteal significantly improves the performance over Graphlab.
Cloudroid: A Cloud Framework for Transparent and QoS-Aware Robotic Computation Outsourcing

Many robotic tasks require heavy computation, which can easily exceed the robot's onboard computer capability. A promising solution to address this challenge is outsourcing thecomputation to the cloud. However, exploiting the potential ofcloud resources in robotic software is difficult, because it in-volves complex code modification and extensive (re)configurationprocedures. Moreover, quality of service (QoS) such as timeliness, which is critical to robot's behavior, have to be considered. Inthis paper, we propose a transparent and QoS-aware softwareframework called Cloudroid for cloud robotic applications. Thisframework supports direct deployment of existing robotic soft-ware packages to the cloud, transparently transforming theminto Internet-accessible cloud services. And with the automati-cally generated service stubs, robotic applications can outsourcetheir computation to the cloud without any code modification. Furthermore, the robot and the cloud can cooperate to maintainthe specific QoS property such as request response time, evenin a highly dynamic and resource-competitive environment. Weevaluated Cloudroid based on a group of typical robotic scenariosand a set of software packages widely adopted in real-worldrobot practices. Results show that robots capability can beenhanced significantly without code modification and specific QoSobjectives can be guaranteed. In certain tasks, the "cloud + robot" setup shows improved performance in orders of magnitudecompared with the robot native setup.
COMPARATIVE EVALUATION OF POTATO STARCH AND BANANA POWDER AS DISINTEGRATING AGENTS IN ACECLOFENAC TABLET FORMULATION

Objective: The main objective of this research was to introduce and evaluate disintegrant property of natural excipients like potato starch and banana powder in tablet formulation. Pharmaceutical excipients developed from natural sources are economic. Methods: Dehydrated banana powder and potato starch were prepared and physicochemical properties like solubility, iodine test, angle of repose, bulk density, tapped density, carr’s index, hausner’s ratio and melting point were evaluated. The interaction between the excipcents and aceclofenac was also studied through FTIR spectroscopy. Tablets were then prepared by direct compression method using different disintegrants and the disintegration time of the tablets formulated was determined. Dissolution study was conducted to characterize release mechanism from the tablet system and data were fitted to various kinetic models. Results: It was found that tablets with banana powder and potato starch disintegrate more rapidly than the tablets with microcrystalline cellulose. The prepared formulations were passed the evaluation test that is weight variation, hardness, friability and content uniformity. The mechanism of drug release from tablets was found to be non-Fikian, anomalous transport. Conclusion: Results from various evaluations suggested that banana powder and potato starch could be used as disintegrants in tablet formulation.
New Word Pair Level Embeddings to Improve Word Pair Similarity

We present a novel approach for computing similarity of English word pairs. While many previous approaches compute cosine similarity of individually computed word embeddings, we compute a single embedding for the word pair that is suited for similarity computation. Such embeddings are then used to train a machine learning model. Testing results on MEN and WordSim-353 datasets demonstrate that for the task of word pair similarity, computing word pair embeddings is better than computing word embeddings only.
Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets

This paper proposes an approach for applying GANs to NMT. We build a conditional sequence generative adversarial net which comprises of two adversarial sub models, a generator and a discriminator. The generator aims to generate sentences which are hard to be discriminated from human-translated sentences ( i.e., the golden target sentences); And the discriminator makes efforts to discriminate the machine-generated sentences from human-translated ones. The two sub models play a mini-max game and achieve the win-win situation when they reach a Nash Equilibrium. Additionally, the static sentence-level BLEU is utilized as the reinforced objective for the generator, which biases the generation towards high BLEU points. During training, both the dynamic discriminator and the static BLEU objective are employed to evaluate the generated sentences and feedback the evaluations to guide the learning of the generator. Experimental results show that the proposed model consistently outperforms the traditional RNNSearch and the newly emerged state-of-the-art Transformer on English-German and Chinese-English translation tasks.
Optimization Techniques on Riemannian Manifolds

The techniques and analysis presented in this paper provide new methods to solve optimization problems posed on Riemannian manifolds. A new point of view is offered for the solution of constrained optimization problems. Some classical optimization techniques on Euclidean space are generalized to Riemannian manifolds. Several algorithms are presented and their convergence properties are analyzed employing the Riemannian structure of the manifold. Specifically, two apparently new algorithms, which can be thought of as Newton's method and the conjugate gradient method on Riemannian manifolds, are presented and shown to possess, respectively, quadratic and superlinear convergence. Examples of each method on certain Riemannian manifolds are given with the results of numerical experiments. Rayleigh's quotient defined on the sphere is one example. It is shown that Newton's method applied to this function converges cubically, and that the Rayleigh quotient iteration is an efficient approximation of Newton's method. The Riemannian version of the conjugate gradient method applied to this function gives a new algorithm for finding the eigenvectors corresponding to the extreme eigenvalues of a symmetric matrix. Another example arises from extremizing the function $\mathop{\rm tr} {\Theta}^{\scriptscriptstyle\rm T}Q{\Theta}N$ on the special orthogonal group. In a similar example, it is shown that Newton's method applied to the sum of the squares of the off-diagonal entries of a symmetric matrix converges cubically.
Synthesis and characterization of lithium tantalate thin films fabricated by sol-gel method

Multilayer lithium tantalate thin films have been successfully prepared on Pt/Ti/SiO2/Si(100) substrate using sol-gel and spin-coating method. Polycrystalline perovskite films were obtained through pyrolysis at 400°C and subsequent calcination at various temperatures from 500°C to 700°C for 1h, which were optimized by means of X-ray diffraction, Raman spectroscopy and thermal analysis. The morphology on the top surface and fractured cross section of LT films was observed by a field-emission scanning electron microscope. The 300 nm thick LT film annealed at 650°C exhibited a dielectric constant of 19.8 and a loss tangent of 0.06 at10KHz.
Conditional transition systems with upgrades

We introduce a variant of transition systems, where activation of transitions depends on conditions of the environment and upgrades during runtime potentially create additional transitions. Using a cornerstone result in lattice theory, we show that such transition systems can be modelled in two ways: as conditional transition systems (CTS) with a partial order on conditions, or as lattice transition systems (LaTS), where transitions are labelled with the elements from a distributive lattice. We define equivalent notions of bisimilarity for both variants and characterise them via a bisimulation game. We explain how conditional transition systems are related to featured transition systems for the modelling of software product lines. Furthermore, we show how to compute bisimilarity symbolically via BDDs by defining an operation on BDDs that approximates an element of a Boolean algebra into a lattice. We have implemented our procedure and provide runtime results.
The Impacts of Information on Stock PricesAssessed by Social Media Sentiment

This paper discusses why information on social media could reflect people's sentiment and how the sentiment further impacts stock prices. A synthesized theoretical model is introduced to answer why social media sentiment is able to impact stock prices, then experiments on stock prices and corresponding social media sentiment is conducted on two cases to verify the impacts. Based on the experiment results, statistical and rational analysis was put forward. This paper not only theoretically interpreted the phenomenons but also experimentally verified the correlationship. The conclusion manifests as social media sentiments is highly possible to have impacts on stock prices.
Transferrin receptor 1 is a reticulocyte-specific receptor for Plasmodium vivax

Vivax malaria host receptor Human malaria is caused by half a dozen species of Plasmodium protozoan parasites, each with distinctive biology. P. vivax, which causes relapsing malaria, specifically parasitizes immature red blood cells called reticulocytes. Gruszczyk et al. identified TfR1 (host transferrin receptor 1) as an alternative receptor for P. vivax. TfR1 binds to a specific P. vivax surface protein. However, the parasite that causes cerebral malaria, P. falciparum, does not share TfR1 as a receptor: P. falciparum could still infect cells in which TfR1 expression was knocked down, but P. vivax could not. Monoclonal antibodies to the P. vivax protein successfully hindered P. vivax infection of red blood cells. Science, this issue p. 48 Invasion of immature red blood cells by the malaria parasite Plasmodium vivax is mediated by binding to the host’s transferrin receptor. Plasmodium vivax shows a strict host tropism for reticulocytes. We identified transferrin receptor 1 (TfR1) as the receptor for P. vivax reticulocyte-binding protein 2b (PvRBP2b). We determined the structure of the N-terminal domain of PvRBP2b involved in red blood cell binding, elucidating the molecular basis for TfR1 recognition. We validated TfR1 as the biological target of PvRBP2b engagement by means of TfR1 expression knockdown analysis. TfR1 mutant cells deficient in PvRBP2b binding were refractory to invasion of P. vivax but not to invasion of P. falciparum. Using Brazilian and Thai clinical isolates, we show that PvRBP2b monoclonal antibodies that inhibit reticulocyte binding also block P. vivax entry into reticulocytes. These data show that TfR1-PvRBP2b invasion pathway is critical for the recognition of reticulocytes during P. vivax invasion.
Quantifying the Transition from Python 2 to 3: An Empirical Study of Python Applications

Background: Python is one of the most popular modern programming languages. In 2008 its authors introduced a new version of the language, Python 3.0, that was not backward compatible with Python 2, initiating a transitional phase for Python software developers. Aims: The study described in this paper investigates the degree to which Python software developers are making the transition from Python 2 to Python 3. Method: We have developed a Python compliance analyser, PyComply, and have assembled a large corpus of Python applications. We use PyComply to measure and quantify the degree to which Python 3 features are being used, as well as the rate and context of their adoption. Results: In fact, Python software developers are not exploiting the new features and advantages of Python 3, but rather are choosing to retain backward compatibility with Python 2. Conclusions: Python developers are confining themselves to a language subset, governed by the diminishing intersection of Python 2, which is not under development, and Python 3, which is under development with new features being introduced as the language continues to evolve.
Prediction of thinning behavior for complex-shaped, lightweight alloy panels formed through a hot stamping process*

This paper provides an overview of hot stamping of light alloys using a method known as HFQ ® forming. We present a new method of forming complex shaped automotive components through the use of a hot stamping process for high strength AA6082 alloy. In particular, the component selected for study is a complex automotive door inner component that could not be conventionally formed from such a strong alloy. The forming process is described and the post-formed thickness distribution is presented. An FE simulation is given to demonstrate the potential to predict the forming process and to provide guidance as to the final component thicknesses.
The Montreal Cognitive Assessment: Normative Data from a Large Swedish Population-Based Cohort

Background: The Montreal Cognitive Assessment (MoCA) has a high sensitivity for detecting cognitive dysfunction. Swedish normative data does not exist and international norms are often derived from populations where cognitive impairment has not been screened for and not been thoroughly assessed to exclude subjects with dementia or mild cognitive impairment. Objective: To establish norms for MoCA and develop a regression-based norm calculator based on a large, well-examined cohort. Methods: MoCA was administered on 860 randomly selected elderly people from a population-based cohort from the EPIC study. Cognitive dysfunction was screened for and further assessed at a memory clinic. After excluding cognitively impaired participants, normative data was derived from 758 people, aged 65–85. Results: MoCA cut-offs (–1 to –2 standard deviations) for cognitive impairment ranged from <25 to <21 for the lowest educated and <26 to <24 for the highest educated, depending on age group. Significant predictors for MoCA score were age, sex and level of education. Conclusion: We present detailed normative MoCA data and cut-offs according to the DSM-5 criteria for cognitive impairment based on a large population-based cohort of elderly individuals, screened and thoroughly investigated to rule out cognitive impairment. Level of education, sex, and age should be taken in account when evaluating MoCA score, which is facilitated by our online regression-based calculator that provide percentile and z-score for a subject’s MoCA score.
The Role of Physiotherapy Extended Scope Practitioners in Musculoskeletal care with Focus on Decision Making and Clinical Outcomes: A Systematic Review of Quantitative and Qualitative Research

OBJECTIVE Physiotherapy extended scope practitioner (ESP) roles are widely utilized in the management of musculoskeletal conditions. The present article reviews the current literature, with particular emphasis on the decision-making process, patient/clinician interaction and clinical outcomes. METHODS A systematic review of musculoskeletal extended scope practice was carried out. The review focused on the outcome of interventions, and the interactions and decision-making processes between ESPs and their patients. A wide search strategy was employed, through multiple databases, grey literature and experts in the field. Qualitative and quantitative studies alike were included and a mixed-methods synthesis approach was undertaken in analysing the findings of included studies. RESULTS A total of 476 articles were identified for inclusion, 25 of which (22 quantitative and three qualitative) meeting the criteria for full quality appraisal and synthesis. It was not possible to conduct a meta-analysis owing to data heterogeneity. The results showed high patient satisfaction with the ESP role, support for ESP staff listing patients for orthopaedic surgery, a high positive correlation of decision making between ESPs and orthopaedic surgeons and evidence of a positive impact on patient outcomes. Qualitative themes reflected the importance of ESP clinical decision making and interpersonal skills and their role in patient education. CONCLUSIONS There is broad support for the physiotherapy ESP role and evidence of favourable outcomes from ESP intervention. Clinical decisions made by ESPs correlate well with those of medical colleagues, although there is a lack of detail explaining the ESP decision-making process itself and the influences and mechanisms by which this occurs. Copyright © 2016 John Wiley & Sons, Ltd.
A randomized controlled trial to enhance coping and posttraumatic growth and decrease posttraumatic stress disorder in HIV-Infected men who have sex with men in Beijing, China

ABSTRACT Although HIV-infected men who have sex with men (MSM) constitute a newly emerged high-risk group in China, little research outside Western countries is available on effective intervention programs to enhance their well-being. The purpose of this randomized controlled trial was to evaluate the efficacy of a group intervention program designed to improve the well-being and adaptive coping strategies of 60 HIV-infected MSM in Beijing, China, randomly assigned either to the intervention group for participation in four weekly sessions or to the control group for placement on a waiting list. They all completed measurements at pre- and postintervention. Compared with the control group, the intervention group reported significantly increased problem-focused coping strategies and levels of posttraumatic growth (PTG) as well as decreased symptoms of posttraumatic stress disorder (PTSD) at the completion of the intervention. In addition, mediation analysis showed that changes in problem-focused coping strategies mediated the intervention effect on increases in PTG; however, the mediating effect of coping strategies on the association of intervention and PTSD was not significant. This study provides empirical evidence for conducting psychological intervention to promote the well-being of HIV-infected MSM. The findings also elucidate the mechanism through which intervention improved PTG.
Neurofibromatosis type 1 alternative splicing is a key regulator of Ras/ERK signaling and learning behaviors in mice

Appropriate activation of the Ras/extracellular signal-regulated kinase (ERK) protein signaling cascade within the brain is crucial for optimal learning and memory. One key regulator of this cascade is the Nf1 Ras GTPase activating protein (RasGAP), which attenuates Ras/ERK signaling by converting active Ras is bound to guanosine triphosphate, activating Ras into inactive Ras is bound to guanosine diphosphate, inactivating Ras. A previous study using embryonic stem cells and embryonic stem cell-derived neurons indicated that Nf1 RasGAP activity is modulated by the highly regulated alternative splicing of Nf1 exon 23a. In this study, we generated Nf123aIN/23aIN mice, in which the splicing signals surrounding Nf1 exon 23a were manipulated to increase exon inclusion. Nf123aIN/23aIN mice are viable and exon 23a inclusion approaches 100% in all tissues, including the brain, where the exon is normally almost completely skipped. Ras activation and phosphorylation of ERK1/2 downstream of Ras are both greatly increased in Nf123aIN/23aIN mouse brain lysates, confirming that exon 23a inclusion inhibits Nf1 RasGAP activity in vivo as it does in cultured cells. Consistent with the finding of altered Ras/ERK signaling in the brain, Nf123aIN/23aIN mice showed specific deficits in learning and memory compared with Nf1+/+ mice. Nf123aIN/23aIN mice performed poorly on the T-maze and Morris water maze tests, which measure short- and long-term spatial memory, respectively. In addition, Nf123aIN/23aIN mice showed abnormally elevated context-dependent fear and a diminished ability to extinguish a cued fear response, indicating defective associative fear learning. Therefore, the regulated alternative splicing of Nf1 is an important mechanism for fine-tuning Ras/ERK signaling as well as learning and memory in mice.
Propagation Based Prototype Prediction

The prediction phase is used to interact with end users, so its response speed is critical for a good user experience to large category recognition tasks. This paper presents a novel and fast algorithm for prototype prediction which may solve the current computing challenges in character input applications on smart terminals. We construct a social network for prototypes and their pair-wise connections. Such "prototype network" falls into "scale-free network", emerging "small-world effect". Under reasonable conditions, there exists a small geodesic path between each node pair. This feature guarantees us to search "better" nodes following the directed edges. Unfortunately, the naive search strategy results in a computing complexity of exponential order. To convert the problem to a manageable scale, we enhance the generic breadth-first search with greedy selectivity. As a result, our method just consider a small candidate set and further propagate from those seeds. Thorough analysis both on network structure and algorithmic propagation patterns is conducted and advantages in efficiency and practicality are revealed. Finally, we evaluated the proposed algorithm on a large-scale, large-category handwritten Chinese character recognition task. Experimental results show that the proposed algorithm can be tuned with either faster prediction speed or higher prediction accuracy.