Miracle-dz/UniFAR-FTUs huggingface dataset:
Motion and Feature Attention Model Based Tracking for Moving Robots

Primates’ visual system can implement various vision tasks effectively and efficiently by focusing the limited attention resources to relevant information. Motivated by this mechanism, we propose a motion and feature attention model based algorithm in this paper for tracking from moving robot. At first regions of motion attention are obtained by ego-motion compensation and frame difference image projection, and then the most attention-attracting color feature is adaptively selected for each frame as simulation of feature-based attention. Moreover, an attenuation function motivated by foveated vision and a heuristic template update strategy are introduced to increase robustness. Experiments show that our tracking algorithm woks robustly from moving robot under illumination variation conditions in various environments and can reach the speed of 20~25 fps running on 320*240 videos
A Subsection Algorithm for the Inverse Kinematics of the Manipulators based on the Simplex Method

Aimed at the inverse kinematics problem of robot manipulators, this paper proposed a subsection algorithm based on the simplex method. Firstly, the simplex method is explained detailedly, in this paper, and then, aimed at a manipulator with 6 degree-of freedom, a subsection algorithm based on the simplex method is proposed. The proposed algorithm doesn't need to calculate the first derivative of the objective function when calculating the searching direction and step length. After limit iterations, the angle vector can converge to the solution of problem in large range. Finally, aimed at an inverse kinematics problem of a manipulator with 6 degree-of-freedom, some simulation is given under the proposed algorithm, and the effectiveness, high precision and the speediness of this algorithm are shown through comparing to the classical simplex method and the conjugate gradient method
Ion heating in an auroral potential structure

We investigate the interaction of O + ions with a one-dimensional potential well, using Hamiltonian formulation. Heating of plasma originating in the terrestrial ionosphere plays a catalytic role in solar-driven magnetic storms, which dis- sipate energy globally within the magnetosphere of the earth. An interesting candidate for ionospheric plasma heating is a potential well located at auroral arcs in the high-latitude magnetosphere. We consider a potential with an exponential form, having a characteristic length Lx. The oxygen ions drift towards the auroral arc in the presence of a constant magnetic field Bz and a constant electric field Ey. The orbits of individual ions for different initial conditions - phase angle and kinetic energy - are traced. Our results show that, depending upon the initial conditions, test particles can be either accelerated or decelerated. Furthermore, we perform a parametric study for the interactions of mono-energetic and Maxwellian type of initial ion distribu- tion - using random phase angle injection of the particles - with respect to our main model parameter, the characteristic length of the potential Lx. We conclude that for characteristic lengths comparable to twice the ion gyroradius, the O + population is accelerated.
Lectures on Twistor Strings and Perturbative Yang-Mills Theory

Recently, Witten proposed a topological string theory in twistor space that is dual to a weakly coupled gauge theory. In this lectures we will discuss aspects of the twistor string theory. Along the way we will learn new things about Yang-Mills scattering amplitudes. The string theory sheds light on Yang-Mills perturbation theory and leads to new methods for computing Yang-Mills scattering amplitudes.
Impact of Design Challenges upon the Development of Critical Thinking Skills within an Undergraduate Image Manipulation Course: A Mixed Method Action Research Study.

The overarching purpose of this study was to improve upon critical thinking through the use of scenario-based design challenge interventions. The study was conducted within an introductory graphic arts course in higher education that focused upon Adobe Photoshop and image manipulation tools, techniques, and terminology. Twelve introductory level graphic arts students participated in the study. In addition to scenario-based design challenge interventions, a reflective practice framework that used scaffolded questioning was developed to guide students in their use of critical thinking about image manipulations. Through a mixed-methods action research design, the study explored the concepts of design challenge interventions and reflective practice in two ways. First, how do design challenge interventions impact achievement scores within an introductory image manipulation course. Second, how do design challenge interventions impact the development of critical thinking in introductory students. One overarching research question and four supporting questions guided this study. Each question was developed to examine how these interventions improve upon a students’ transferrable knowledge and further connect and apply course objectives and goals with future careers. Qualitative and quantitative data collection techniques such as preand postassessments, design challenge interventions, image manipulation projects, and semi-structured interviews were used. The study revealed that knowledge was transferred throughout the study, yet a correlation between the design challenge interventions and image manipulation projects was not significantly detected. Analysis and evaluation of reflective statements produced during the design challenge interventions revealed that the students were applying prior knowledge and learned skills to their image manipulations. The semi-structured interviews also revealed that students were making connections between thinking critically about design and the basic image manipulation design processes. The variability in findings and supporting themes suggest implications for a course improvement action plan, and future research and practice. To improve upon the course design, an action plan that updates the overarching course goals, inserts active learning strategies, and uses classroom assessment techniques to improve upon critical thinking and reflection is proposed. Further research supported by educators in graphic arts in the development of critical thinking and additional 21st century skill sets in graphic arts degree programs is suggested.
A Novel Interaction between hScrib and PP1γ Downregulates ERK Signaling and Suppresses Oncogene-Induced Cell Transformation

Previous studies have shown that the cell polarity regulator hScrib interacts with, and consequently controls, the ERK signaling pathway. This interaction occurs through two well-conserved Kinase Interacting Motifs, which allow hScrib to bind ERK1 directly, resulting in a reduction in the levels of phospho-ERK. This suggests that hScrib might recruit a phosphatase to regulate this signaling pathway. Using a proteomic approach we now show that Protein Phosphatase 1γ (PP1γ) is a major interacting partner of hScrib. This interaction is direct and occurs through a conserved PP1γ interaction motif on the hScrib protein, and this interaction appears to be required for hScrib's ability to downregulate ERK phosphorylation. In addition, hScrib also controls the pattern of PP1γ localization, where loss of hScrib enhances the nuclear translocation of PP1γ. Furthermore, we also show that the ability of hScrib to interact with PP1γ is important for the ability of hScrib to suppress oncogene-induced transformation of primary rodent cells. Taken together, these results demonstrate that hScrib acts as a scaffold to integrate the control of the PP1γ and ERK signaling pathways and explains how disruption of hScrib localisation can contribute towards the development of human malignancy.
Local Climate Heterogeneity Shapes Population Genetic Structure of Two Undifferentiated Insular Scutellaria Species

Spatial climate heterogeneity may not only affect adaptive gene frequencies but could also indirectly shape the genetic structure of neutral loci by impacting demographic dynamics. In this study, the effect of local climate on population genetic variation was tested in two phylogenetically close Scutellaria species in Taiwan. Scutellaria taipeiensis, which was originally assumed to be an endemic species of Taiwan Island, is shown to be part of the widespread species S. barbata based on the overlapping ranges of genetic variation and climatic niches as well as their morphological similarity. Rejection of the scenario of “early divergence with secondary contact” and the support for multiple origins of populations of S. taipeiensis from S. barbata provide strong evolutionary evidence for a taxonomic revision of the species combination. Further tests of a climatic effect on genetic variation were conducted. Regression analyses show nonlinear correlations among any pair of geographic, climatic, and genetic distances. However, significantly, the bioclimatic variables that represent the precipitation from late summer to early autumn explain roughly 13% of the genetic variation of our sampled populations. These results indicate that spatial differences of precipitation in the typhoon season may influence the regeneration rate and colonization rate of local populations. The periodic typhoon episodes explain the significant but nonlinear influence of climatic variables on population genetic differentiation. Although, the climatic difference does not lead to species divergence, the local climate variability indeed impacts the spatial genetic distribution at the population level.
Chasing a Fast Robber on Planar Graphs and Random Graphs

We consider a variant of the Cops and Robber game, in which the robber has unbounded speed, that is, can take any path from her vertex in her turn, but she is not allowed to pass through a vertex occupied by a cop. Let c∞(G) denote the number of cops needed to capture the robber in a graph G in this variant, and let tw (G) denote the treewidth of G. We show that if G is planar then c∞(G)=Θ( tw (G)) , and there is a polynomial‐time constant‐factor approximation algorithm for computing c∞(G) . We also determine, up to constant factors, the value of c∞(G) of the Erdős–Rényi random graph G=G(n,p) for all admissible values of p, and show that when the average degree is ω(1), c∞(G) is typically asymptotic to the domination number.
Mechanising the Metatheory of LF

LF is a dependent type theory in which many other formal systems can be conveniently embedded. However, correct use of LF relies on nontrivial metatheoretic developments such as proofs of correctness of decision procedures for LF’s judgments. Although detailed informal proofs of these properties have been published, they have not been formally verified in a theorem prover. We have formalised these properties within Isabelle/HOL using the Nominal Datatype Package, closely following a recent article by Harper and Pfenning. In the process, we identified and resolved a gap in one of the proofs and a small number of minor lacunae in others. Besides its intrinsic interest, our formalisation provides a foundation for studying the adequacy of LF encodings, the correctness of Twelf-style metatheoretic reasoning, and the metatheory of extensions to LF.
Characterizing Cancer Subtypes Using Dual Analysis in Caleydo StratomeX

Dual analysis uses statistics to describe both the dimensions and rows of a high-dimensional dataset. Researchers have integrated it into StratomeX, a Caleydo view for cancer subtype analysis. In addition, significant-difference plots show the elements of a candidate subtype that differ significantly from other subtypes, thus letting analysts characterize subtypes. Analysts can also investigate how data samples relate to their assigned subtype and other groups. This approach lets them create well-defined subtypes based on statistical properties. Three case studies demonstrate the approach's utility, showing how it reproduced findings from a published subtype characterization.
A Hybrid Approach to Clinical Question Answering

Abstract : In this paper, we describe our clinical question answering system developed and submitted for the Text Retrieval Conference (TREC 2014) Clinical Decision Support (CDS) track. The task for this track was to retrieve relevant biomedical articles to answer generic clinical questions about medical case reports. As part of our maiden participation in TREC, we submitted a single run using a hybrid Natural Language Processing (NLP)-driven approach to accomplish the given task. Evaluation results showed that our clinical question answering system achieved the best scores in two of eight dual-judged topics: #5 and 27, and performed relatively better compared to the median scores for topics: #13, 18, 19, 22, and 23.
The Design of a Conngurable Text Summarization System

This report presents the design of a exible summarization system consisting of several independent linguistic processing tools that can be rapidly conngured and extensively parameterized. Summarization will begin with segmentation of the text at places where there is a probable topic change. Next, segments will be classiied and segments which strongly evidence of the topic suggested by a user's query will be identiied. Finally, summary sentences will be extracted from the most relevant segments rather than the whole text.
Microarchitecture-Aware Virtual Machine Placement under Information Leakage Constraints

One of the major concerns when moving to Clouds is data confidentiality. Nevertheless, more and more applications are outsourced to a public or private Cloud. In general, the usage of virtualization is acknowledged as an isolation mechanism between applications running on shared resources. But, as previously shown, virtualization does not ensure data security. Indeed, the isolation can be broken due to covert channels existing in both the software and the hardware (e.g., Improperly virtualized caches). Furthermore, even if a perfect control mechanism could be design, it would not protect against covert channels as they bypass control mechanism using legal means. In this paper, we first describe how these attacks are working. Next, after presenting the existing mitigation mechanisms, we show that a good solution is to take into account security while allocating resources (i.e., When placing the VMs). Furthermore, depending on which resources are shared, we demonstrate that the achievable bit rate of these attacks can change dramatically. We propose a new metric to quantify them and use it as an acceptable risk for isolation properties. Then, we show how to use them when allocating resources and the importance of a fine-grained resource allocation mechanism. Finally, we demonstrate that a security-oblivious placement algorithm breaks a fair amount of properties but taking into account the isolation impacts the acceptance rate (i.e., The percentage of successfully placed VMs).
Compared to Dematerialized Money, Cash Increases Impatience in Intertemporal Choice

When it comes to trading time for money (or vice versa), people tend to be impatient and myopic. Often dramatically so. For illustration, half of people would rather collect $15 now than $30 in three months. This willingness to forego 50% of the reward to skip a 3-month wait corresponds to an annual discount rate of 277%. This article investigates how money’s physical form biases intertemporal choice. We ask, what happens to (im)patience (i.e., discount rates) when time is traded against cash rather than against an equivalent sum of dematerialized money? We find that intertemporal decisions pitting time against cash (rather than against dematerialized money) increase impatience. The underlying mechanism relates to the pain of parting from money. Letting go of cash (dematerialized money) we can have now is psychologically more (less) painful, which in turn reduces (increases) our willingness to wait for larger-later payoffs. Importantly, heightening prevention focus (i.e., concerns for safety and security) moderates this bias. The article concludes by discussing the implications of the research, particularly for the psychology of saving behavior.
Supporting collaboration in distributed software engineering teams

Software engineering is no longer the preserve of individuals but is essentially a team-based activity involving a wide variety of stakeholders and thus making the need for communication and co-operation an inherent characteristic. Changes in support technology, economic factors and globalisation of the software process is resulting in the geographical separation of personnel. Where such distribution of personnel occurs, it is clear that the quality of the project will be dependent upon the degree of effective communication and co-operation. This paper presents the results from a study of communication and co-operation practices on a range of distributed software projects from an industrial and educational context. It analyses the advantages and disadvantages of distributed software engineering teams and presents the lessons learned from these studies, including team structure, project management, communication and procedures.
Selection of appropriate reference genes for quantitative real-time PCR in Oxytropis ochrocephala Bunge using transcriptome datasets under abiotic stress treatments

Background: Oxytropis ochrocephala Bunge, an indigenous locoweed species in China, poses great threats to livestock on grasslands. There is a need for further genetic study in the plants per se, for understanding the basis of its acclimation mechanism in various unfavorable environmental conditions and to implement effective control measures. Quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR) is the most commonly used method for gene expression analysis. To facilitate gene expression studies and obtain more accurate qRT-PCR data, normalization relative to stable reference genes is required. The aim of this study was to select the most stable reference genes for transcriptional analysis in O. ochrocephala. Results: We selected 12 candidate reference genes, 18S ribosomal RNA (18S RNA), actin2/7 (ACT7), β-actin (ACTB), actin101 (ACT101), actin11 (ACT11), β-tubulin (TUB), α-tubulin (TUA), glyceraldehyde-3-phosphate dehydrogenase-1 (GAPDH1), GAPDH2, metallothionein-like protein (MET), fructose-bisphosphate aldolase (FBA) and histone H3 (HIS), from the transcriptome datasets of O. ochrocephala and determined the suitability by analyzing their expression levels when exposed to a range of abiotic stress conditions. By employing software packages including geNorm, NormFinder and BestKeeper, HIS, ACT7, and ACT101 were assessed as the most suitable set for normalization in all samples. When normalized with the most stable reference genes, the expression patterns of the three target genes were in accordance with those in the transcriptome data, indicating that the reference genes selected in this study are suitable. Conclusions: The study provided appropriate reference genes for accurate normalization in qRT-PCR analysis in O. ochrocephala and emphasized the importance of validating reference genes for gene expression analysis under specific experimental condition. The usage of inappropriate reference gene would cause misinterpretation.
The Yahoo!: cloud datastore load balancer

Sherpa is a large-scale distributed and globally replicated multi-tenant cloud data storage system. Sherpa scales by horizontally partitioning data into tablets and distributing these tablets across multiple servers. While Sherpa scales for increasing workload sizes by adding servers, it is vulnerable to load imbalance among tablets that cause hotspots to develop on just a few servers. In this paper we describe Yak, the Sherpa load balancer. Yak detects hotspots and then automatically balances load by migrating tablets from the overloaded servers, and also by splitting data into new tablets. We describe Yak's design principles, algorithms and architecture. We then evaluate Yak on workloads based on Sherpa production scenarios.
Load Balancing for Efficient Routing in Wireless Sensor Networks

Congestion is generated by buffer overflow and generally is considered in transport layer. However, there is almost no considered one about existing routing protocols of sensor network because it is difficult to apply congestion control in transport layer of WSNs (wireless sensor networks) that have limited resources such as energy, buffer, and bandwidth of sensor nodes. Therefore, routing protocol of sensor networks must deliberate how to control congestion. In order to overcome this problem, we propose the routing protocol avoidable congestion to improve throughput, packet drop rate, lifetime, and so on. Consequently, each node can evenly distribute loads. We demonstrate how to achieve congestion-free load balancing when there are multiple routing paths towards sink.
A Fourteen Gene GBM Prognostic Signature Identifies Association of Immune Response Pathway and Mesenchymal Subtype with High Risk Group

Background Recent research on glioblastoma (GBM) has focused on deducing gene signatures predicting prognosis. The present study evaluated the mRNA expression of selected genes and correlated with outcome to arrive at a prognostic gene signature. Methods Patients with GBM (n = 123) were prospectively recruited, treated with a uniform protocol and followed up. Expression of 175 genes in GBM tissue was determined using qRT-PCR. A supervised principal component analysis followed by derivation of gene signature was performed. Independent validation of the signature was done using TCGA data. Gene Ontology and KEGG pathway analysis was carried out among patients from TCGA cohort. Results A 14 gene signature was identified that predicted outcome in GBM. A weighted gene (WG) score was found to be an independent predictor of survival in multivariate analysis in the present cohort (HR = 2.507; B = 0.919; p<0.001) and in TCGA cohort. Risk stratification by standardized WG score classified patients into low and high risk predicting survival both in our cohort (p = <0.001) and TCGA cohort (p = 0.001). Pathway analysis using the most differentially regulated genes (n = 76) between the low and high risk groups revealed association of activated inflammatory/immune response pathways and mesenchymal subtype in the high risk group. Conclusion We have identified a 14 gene expression signature that can predict survival in GBM patients. A network analysis revealed activation of inflammatory response pathway specifically in high risk group. These findings may have implications in understanding of gliomagenesis, development of targeted therapies and selection of high risk cancer patients for alternate adjuvant therapies.
Sherlock: Sparse Hierarchical Embeddings for Visually-Aware One-Class Collaborative Filtering

Building successful recommender systems requires uncovering the underlying dimensions that describe the properties of items as well as users' preferences toward them. In domains like clothing recommendation, explaining users' preferences requires modeling the visual appearance of the items in question. This makes recommendation especially challenging, due to both the complexity and subtlety of people's 'visual preferences,' as well as the scale and dimensionality of the data and features involved. Ultimately, a successful model should be capable of capturing considerable variance across different categories and styles, while still modeling the commonalities explained by `global' structures in order to combat the sparsity (e.g. cold-start), variability, and scale of real-world datasets. Here, we address these challenges by building such structures to model the visual dimensions across different product categories. With a novel hierarchical embedding architecture, our method accounts for both high-level (colorfulness, darkness, etc.) and subtle (e.g. casualness) visual characteristics simultaneously.