Miracle-dz/UniFAR-FTUs huggingface dataset:
The Best-of-n Problem in Robot Swarms: Formalization, State of the Art, and Novel Perspectives

The ability to collectively choose the best among a finite set of alternatives is a fundamental cognitive skill for robot swarms. In this paper, we propose a formal definition of the best-of-n problem and a taxonomy that details its possible variants. Based on this taxonomy, we analyze the swarm robotics literature focusing on the decision-making problem dealt with by the swarm. We find that, so far, the literature has primarily focused on certain variants of the best-of-n problem while other variants have been the subject of only a few isolated studies. Additionally, we consider a second taxonomy about the design methodologies used to develop collective decision-making strategies. Based on this second taxonomy, we provide an in-depth survey of the literature that details the strategies proposed so far and discusses the advantages and disadvantages of current design methodologies.
Effect of dietary protein content on weight gain, energy expenditure, and body composition during overeating: a randomized controlled trial.

CONTEXT The role of diet composition in response to overeating and energy dissipation in humans is unclear. OBJECTIVE To evaluate the effects of overconsumption of low, normal, and high protein diets on weight gain, energy expenditure, and body composition. DESIGN, SETTING, AND PARTICIPANTS A single-blind, randomized controlled trial of 25 US healthy, weight-stable male and female volunteers, aged 18 to 35 years with a body mass index between 19 and 30. The first participant was admitted to the inpatient metabolic unit in June 2005 and the last in October 2007. INTERVENTION After consuming a weight-stabilizing diet for 13 to 25 days, participants were randomized to diets containing 5% of energy from protein (low protein), 15% (normal protein), or 25% (high protein), which they were overfed during the last 8 weeks of their 10- to 12-week stay in the inpatient metabolic unit. Compared with energy intake during the weight stabilization period, the protein diets provided approximately 40% more energy intake, which corresponds to 954 kcal/d (95% CI, 884-1022 kcal/d). MAIN OUTCOME MEASURES Body composition was measured by dual-energy x-ray absorptiometry biweekly, resting energy expenditure was measured weekly by ventilated hood, and total energy expenditure by doubly labeled water prior to the overeating and weight stabilization periods and at weeks 7 to 8. RESULTS Overeating produced significantly less weight gain in the low protein diet group (3.16 kg; 95% CI, 1.88-4.44 kg) compared with the normal protein diet group (6.05 kg; 95% CI, 4.84-7.26 kg) or the high protein diet group (6.51 kg; 95% CI, 5.23-7.79 kg) (P = .002). Body fat increased similarly in all 3 protein diet groups and represented 50% to more than 90% of the excess stored calories. Resting energy expenditure, total energy expenditure, and body protein did not increase during overfeeding with the low protein diet. In contrast, resting energy expenditure (normal protein diet: 160 kcal/d [95% CI, 102-218 kcal/d]; high protein diet: 227 kcal/d [95% CI, 165-289 kcal/d]) and body protein (lean body mass) (normal protein diet: 2.87 kg [95% CI, 2.11-3.62 kg]; high protein diet: 3.18 kg [95% CI, 2.37-3.98 kg]) increased significantly with the normal and high protein diets. CONCLUSIONS Among persons living in a controlled setting, calories alone account for the increase in fat; protein affected energy expenditure and storage of lean body mass, but not body fat storage. TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT00565149.
The Design and Implementation of the Inversion File System

This paper describes the design, implementation, and performance of the Inversion file system. Inversion provides a rich set of services to file system users, and manages a large tertiary data store. Inversion is built on top of the POSTGRES database system, and takes advantage of low-level DBMS services to provide transaction protection, fine-grained time travel, and fast crash recovery for user files and file system metadata. Inversion gets between 30% and 80% of the throughput of ULTRIX NFS backed by a non-volatile RAM cache. In addition, Inversion allows users to provide code for execution directly in the file system manager, yielding performance as much as seven times better than that of ULTRIX NFS.
Image-based street-side city modeling

We propose an automatic approach to generate street-side 3D photo-realistic models from images captured along the streets at ground level. We first develop a multi-view semantic segmentation method that recognizes and segments each image at pixel level into semantically meaningful areas, each labeled with a specific object class, such as building, sky, ground, vegetation and car. A partition scheme is then introduced to separate buildings into independent blocks using the major line structures of the scene. Finally, for each block, we propose an inverse patch-based orthographic composition and structure analysis method for façade modeling that efficiently regularizes the noisy and missing reconstructed 3D data. Our system has the distinct advantage of producing visually compelling results by imposing strong priors of building regularity. We demonstrate the fully automatic system on a typical city example to validate our methodology.
Deriving Behavior from Personality : A Reinforcement Learning Approach

Creating artificial intelligent agents that are high-fidelity simulations of natural agents will require that behavioral scientists be able to write code themselves, not merely act as consultants with the ensuing knowledge acquisition bottleneck. We are designing a system that will make it possible to create rich agents using concepts familiar to behavioral scientists, such as personality models from psychology. However, translating personality models into the concrete behavior of an agent using currently available programming constructs would require a level of code complexity that would make the system inaccessible to behavioral scientists. What we need is a way to derive the concrete actions of an agent directly from psychological personality models. This paper describes a reinforcement learning approach to solving this problem in which we represent trait-theoretic personality models as reinforcement learning agents. We validate our approach by creating a virtual reconstruction of a psychology experiment using human subjects and showing that our virtual agents exhibit similar behavior patterns.
7-T MRI in Cerebrovascular Diseases: Challenges to Overcome and Initial Results

Abstract Magnetic resonance imaging (MRI) plays a key role in the investigation of cerebrovascular diseases. Compared with computed tomography (CT) and digital subtraction angiography (DSA), its advantages in diagnosing cerebrovascular pathology include its superior tissue contrast, its ability to visualize blood vessels without the use of a contrast agent, and its use of magnetic fields and radiofrequency pulses instead of ionizing radiation. In recent years, ultrahigh field MRI at 7 tesla (7 T) has shown promise in the diagnosis of many cerebrovascular diseases. The increased signal-to-noise ratio (SNR; 2.3x and 4.7x increase compared with 3 and 1.5 T, respectively) and contrast-to-noise ratio (CNR) at this higher field strength can be exploited to obtain a higher spatial resolution and higher lesion conspicuousness, enabling assessment of smaller brain structures and lesions. Cerebrovascular diseases can be assessed at different tissue levels; for instance, changes of the arteries feeding the brain can be visualized to determine the cause of ischemic stroke, regional changes in brain perfusion can be mapped to predict outcome after revascularization, and tissue damage, including old and recent ischemic infarcts, can be evaluated as a marker of ischemic burden. For the purpose of this review, we will discriminate 3 levels of assessment of cerebrovascular diseases using MRI: Pipes, Perfusion, and Parenchyma (3 Ps). The term Pipes refers to the brain-feeding arteries from the heart and aortic arch, upwards to the carotid arteries, vertebral arteries, circle of Willis, and smaller intracranial arterial branches. Perfusion is the amount of blood arriving at the brain tissue level, and includes the vascular reserve and perfusion territories. Parenchyma refers to the acute and chronic burden of brain tissue damage, which includes larger infarcts, smaller microinfarcts, and small vessel disease manifestations such as white matter lesions, lacunar infarcts, and microbleeds. In this review, we will describe the key developments in the last decade of 7-T MRI of cerebrovascular diseases, subdivided for these 3 levels of assessment.
Key Role of the GITR/GITRLigand Pathway in the Development of Murine Autoimmune Diabetes: A Potential Therapeutic Target

Background The cross-talk between pathogenic T lymphocytes and regulatory T cells (Tregs) plays a major role in the progression of autoimmune diseases. Our objective is to identify molecules and/or pathways involved in this interaction and representing potential targets for innovative therapies. Glucocorticoid-induced tumor necrosis factor receptor (GITR) and its ligand are key players in the T effector/Treg interaction. GITR is expressed at low levels on resting T cells and is significantly up-regulated upon activation. Constitutive high expression of GITR is detected only on Tregs. GITR interacts with its ligand mainly expressed on antigen presenting cells and endothelial cells. It has been suggested that GITR triggering activates effector T lymphocytes while inhibiting Tregs thus contributing to the amplification of immune responses. In this study, we examined the role of GITR/GITRLigand interaction in the progression of autoimmune diabetes. Methods and Findings Treatment of 10-day-old non-obese diabetic (NOD) mice, which spontaneously develop diabetes, with an agonistic GITR-specific antibody induced a significant acceleration of disease onset (80% at 12 weeks of age). This activity was not due to a decline in the numbers or functional capacity of CD4+CD25+Foxp3+ Tregs but rather to a major activation of ‘diabetogenic’ T cells. This conclusion was supported by results showing that anti-GITR antibody exacerbates diabetes also in CD28−/− NOD mice, which lack Tregs. In addition, treatment of NOD mice, infused with the diabetogenic CD4+BDC2.5 T cell clone, with GITR-specific antibody substantially increased their migration, proliferation and activation within the pancreatic islets and draining lymph nodes. As a mirror image, blockade of the GITR/GITRLigand pathway using a neutralizing GITRLigand-specific antibody significantly protected from diabetes even at late stages of disease progression. Experiments using the BDC2.5 T cell transfer model suggested that the GITRLigand antibody acted by limiting the homing and proliferation of pathogenic T cells in pancreatic lymph nodes. Conclusion GITR triggering plays an important costimulatory role on diabetogenic T cells contributing to the development of autoimmune responses. Therefore, blockade of the GITR/GITRLigand pathway appears as a novel promising clinically oriented strategy as GITRLigand-specific antibody applied at an advanced stage of disease progression can prevent overt diabetes.
Enhancement of Chlorogenic Acid Production in Hairy Roots of Platycodon grandiflorum by Over-Expression of An Arabidopsis thaliana Transcription Factor AtPAP1

To improve the production of chlorogenic acid (CGA) in hairy roots of Platycodon grandiflorum, we induced over-expression of Arabidopsis thaliana transcription factor production of anthocyanin pigment (AtPAP1) using an Agrobacterium rhizogenes-mediated transformation system. Twelve hairy root lines showing over-expression of AtPAP1 were generated. In order to investigate the regulation of AtPAP1 on the activities of CGA biosynthetic genes, the expression levels of seven P. grandiflorum CGA biosynthetic genes were analyzed in the hairy root line that had the greatest accumulation of AtPAP1 transcript, OxPAP1-1. The introduction of AtPAP1 increased the mRNA levels of all examined CGA biosynthetic genes and resulted in a 900% up-regulation of CGA accumulation in OxPAP1-1 hairy roots relative to controls. This suggests that P. grandiflorum hairy roots that over-express the AtPAP1 gene are a potential alternative source of roots for the production of CGA.
A Tutorial Dialogue System with Knowledge-Based Understanding and Classification of Student Explanations

We are engaged in a research project to create a tutorial dialogue system that helps students to explain the reasons behind their problem-solving actions, in order to help them learn with greater understanding. Currently, we are pilottesting a prototype system that is able to analyze student explanations, stated in their own words, recognize the types of omissions that we typically see in these explanations, and provide feedback. The system takes a knowledge-based approach to natural language understanding and uses a statistical text classifier as a backup. The main features are: robust parsing, logic-based representation of semantic content, representation of pedagogical content knowledge in the form of a hierarchy of partial and complete explanations, and reactive dialogue management. A preliminary evaluation study indicates that the knowledge-based natural language component correctly classifies 80% of explanations and produces a reasonable classification for all but 6% of explanations.
Mutant DMPK 3′-UTR transcripts disrupt C2C12 myogenic differentiation by compromising MyoD

Myotonic dystrophy (DM) is caused by two similar noncoding repeat expansion mutations (DM1 and DM2). It is thought that both mutations produce pathogenic RNA molecules that accumulate in nuclear foci. The DM1 mutation is a CTG expansion in the 3′ untranslated region (3′-UTR) of dystrophia myotonica protein kinase (DMPK). In a cell culture model, mutant transcripts containing a (CUG)200 DMPK 3′-UTR disrupt C2C12 myoblast differentiation; a phenotype similar to what is observed in myoblast cultures derived from DM1 patient muscle. Here, we have used our cell culture model to investigate how the mutant 3′-UTR RNA disrupts differentiation. We show that MyoD protein levels are compromised in cells that express mutant DMPK 3′-UTR transcripts. MyoD, a transcription factor required for the differentiation of myoblasts during muscle regeneration, activates differentiation-specific genes by binding E-boxes. MyoD levels are significantly reduced in myoblasts expressing the mutant 3′-UTR RNA within the first 6 h under differentiation conditions. This reduction correlates with blunted E-box–mediated gene expression at time points that are critical for initiating differentiation. Importantly, restoring MyoD levels rescues the differentiation defect. We conclude that mutant DMPK 3′-UTR transcripts disrupt myoblast differentiation by reducing MyoD levels below a threshold required to activate the differentiation program.
A manifold approach to face recognition from low quality video across illumination and pose using implicit super-resolution

We consider the problem of matching a face in a low resolution query video sequence against a set of higher quality gallery sequences. This problem is of interest in many applications, such as law enforcement. Our main contribution is an extension of the recently proposed Generic Shape-Illumination Manifold (gSIM) framework. Specifically, (i) we show how super-resolution across pose and scale can be achieved implicitly, by off-line learning of subsampling artefacts; (ii) we use this result to propose an extension to the statistical model of the gSIM by compounding it with a hierarchy of subsampling models at multiple scales; and (iii) we describe an extensive empirical evaluation of the method on over 1300 video sequences – we first measure the degradation in performance of the original gSIM algorithm as query sequence resolution is decreased and then show that the proposed extension produces an error reduction in the mean recognition rate of over 50%.
Hindcasting the continuum of Dansgaard–Oeschger variability: mechanisms, patterns and timing

Abstract. Millennial-scale variability associated with Dansgaard–Oeschger events is arguably one of the most puzzling climate phenomena ever discovered in paleoclimate archives. Here, we set out to elucidate the underlying dynamics by conducting a transient global hindcast simulation with a 3-D intermediate complexity earth system model covering the period 50 to 30 ka BP. The model is forced by time-varying external boundary conditions (greenhouse gases, orbital forcing, and ice-sheet orography and albedo) and anomalous North Atlantic freshwater fluxes, which mimic the effects of changing northern hemispheric ice volume on millennial timescales. Together these forcings generate a realistic global climate trajectory, as demonstrated by an extensive model/paleo data comparison. Our results are consistent with the idea that variations in ice-sheet calving and subsequent changes of the Atlantic Meridional Overturning Circulation were the main drivers for the continuum of glacial millennial-scale variability seen in paleorecords across the globe.
The Material Mechanical Properties Reverse Calculation Method Bases on Depth-Sensing Indentation

It introduced the depth-sensing indentation principle,characteristics,and the major applications and advantages in micromechanical properties and damage research. It focused on analyzing the typical load-depth test curve from the material depth-sensing indentation experiment, then talked about how the curve was used in the reverse calculation process for material mechanics properties.It can provide the technology route reference for the other metal mechanical properties research in the future.
Scalable data mining with model constraints

Data mining can be abstractly defined as the process of extracting concise and interesting models (or, patterns) from large amounts of data. Unfortunately, conventional mining systems provide users with only very restricted mechanisms for specifying models of interest. As a consequence, the mining process is typically characterized by lack of focus and users often end up paying computational costs that are inordinately high compared to the specific models]patterns of interest. Exploiting user-defined model constraints during the mining process can help alleviate this problem and ensure system performance that is commensurate with the level of user focus. Attaining such performance goals, however, is not straightforward and, typically, requires the design of novel data mining algorithms that make effective use of the model constraints. In this paper, we provide an overview of our recent work on scalable, constraint-based algorithms for (1) decision tree construction with size and accuracy constraints for the desired decision tree model, and (2) sequential pa*tern extraction in the presence of structural, regular expression constraints for the target patterns. By "pushing" the model constraints inside the mining process, our algorithms give mining users exactly the models that they are looking for, while achieving performance speedups that often exceed one order of magnitude. Further, our work on sequential pattern mining has uncovered some valuable insights into the tradeoffs that arise when complex constraints that do not subscribe to "nice" properties (like anti-monotonicity) are integrated into the mining process. We argue that, in general, a cost-based approach (similar to that employed in conventional query optimizers) is needed to explore these tradeoffs in a principled manner and produce effective execution plans for ad-hoc mining queries.
Performance analysis of multicore and multinodal implementation of SpMV operation

In this paper we present two algorithms for performing sparse matrix-dense vector multiplication (known as SpMV operation). We show parallel (multicore) version of algorithm, which can be efficiently implemented on the contemporary multicore architectures. Next, we show distributed (so-called multinodal) version targeted at high performance clusters. Both versions are thoroughly tested using different architectures, compiler tools and sparse matrices of different sizes. Considered matrices comes from The University of Florida Sparse Matrix Collection. The performance of the algorithms is compared to the performance of SpMV routine from widely used Intel Math Kernel Library.
RRT∗-Smart: Rapid convergence implementation of RRT∗ towards optimal solution

Rapidly Exploring Random Tree (RRT) is one of the quickest and the most efficient obstacle free path finding algorithm. However, it cannot guarantee finding the most optimal path. A recently proposed extension of RRT, known as Rapidly Exploring Random Tree Star (RRT*), claims to achieve convergence towards the optimal solution but has been proven to take an infinite time to do so and with a slow convergence rate. To overcome these limitations, we propose an extension of RRT*, called RRT*-Smart, which aims to accelerate its rate of convergence and to reach an optimum or near optimum solution at a much faster rate and at a reduced execution time. Our novel algorithm inculcates two new techniques in RRT*: these are path optimization and intelligent sampling. Simulation results presented in various obstacle cluttered environments confirm the efficiency of RRT*-Smart.
Mid1p-dependent regulation of the M–G1 transcription wave in fission yeast

The control of gene expression at certain times during the mitotic cell division cycle is a common feature in eukaryotes. In fission yeast, at least five waves of gene expression have been described, with one transcribed at the M–G1 interval under the control of the PBF transcription factor complex. PBF consists of at least three transcription factors, two forkhead-like proteins Sep1p and Fkh2p, and a MADS box-like protein Mbx1p, and binds to PCB motifs found in the gene promoters. Mbx1p is under the direct control of the polo-like kinase Plo1p and the Cdc14p-like phosphatase Clp1p (Flp1p). Here, we show that M–G1 gene expression in fission yeast is also regulated by the anillin-like protein, Mid1p (Dmf1p). Mid1p binds in vivo to both Fkh2p and Sep1p, and to the promoter regions of M–G1 transcribed genes. Mid1p promoter binding is dependent on Fkh2p, Plo1p and Clp1p. The absence of mid1+ in cells results in partial loss of M–G1 specific gene expression, suggesting that it has a negative role in controlling gene expression. This phenotype is exacerbated by also removing clp1+, suggesting that Mid1p and Clp1p have overlapping functions in controlling transcription. As mid1+ is itself expressed at M–G1, these observations offer a new mechanism whereby Mid1p contributes to controlling cell cycle-specific gene expression as part of a feedback loop.
User-Weight Model for Item-based Recommendation Systems

Nowadays, item-based Collaborative Filtering (CF) has been widely used as an effective way to help people cope with information overload. It computes the item-item similarities/differentials and then selects the most similar items for prediction. A weakness of current typical item-based CF approaches is that all users have the same weight in computing the item relationships. In order to improve the recommendation quality, we incorporate users’ weights based on a relationship model of users into item similarities and differentials computing. In this paper, a model of user relationship, a method for computing users’ weights, and weight-based item-item similarities/differentials computing approaches are proposed for item-based CF recommendations. Finally, we experimentally evaluate our approach for recommendation and compare it to typical item-based CF approaches based on Adjusted Cosine and Slope One. The experiments show that our approaches can improve the recommendation results of them.