Miracle-dz/UniFAR-FTUs huggingface dataset:
PALM: Predicting Internet Network Distances Using Peer-to-Peer Measurements

Landmark-based architecture has been commonly adopted in the networking community as a mechanism to mea- sure and characterize a host's location on the Internet. In m ost existing landmark based approaches, end hosts use the distance measurements to a common, fixed set of landmarks to derive an estimated location on the Internet. This paper investigates whether it is possible for participating peer nodes in an overlay network to collaboratively construct an accurate geometric model of its topology in a completely decentralized peer-to-peer fashion, without using a fixed set of landmarks. We call such a peer- to-peer approach in topology discovery and modeling using landmarks PALM (Peers As LandMarks). We evaluate the performance characteristics of such a decentralized coordinate- based approach under several factors, including dimensionality of the geometric space, peer distance distribution, and the number of peer-to-peer distance measurements used. We evaluate two PALM-based schemes: RAND-PALM and ISLAND. In RAND- PALM, a peer node randomly selects from existing peer nodes as its landmarks. In ISLAND (Intelligent Selection of Landmarks), each peer node selects its landmarks by exploiting the topological information derived based on existing peer nodes' coordina tes.
Trajectory Classification in n Dimensions using Subspace Projection

This paper presents a novel descriptor for trajectory classification in n dimensions, which is invariant with respect to scaling and rigid transformation. Using a hierarchical approach, the descriptor is able to capture both local and global features of the trajectory. The algorithm iteratively splits up every trajectory into smaller trajectory segments resulting in a binary tree. Inspired by the Frenet-Serret formulas, a projection onto a lower dimensional subspace is performed for every trajectory segment, providing a characteristic description of every trajectory. The subspace projection acts as a pseudo-curvature measure in every dimension. Successful applicability is shown through classification experiments in three and six dimensions using an RGB-D camera. For comparison with other algorithms, the Australian Sign Language dataset is also used for classification, showing a superior classification rate.
Pain Intensity in Hospitalized Adults: A Multilevel Analysis of Barriers and Facilitators of Pain Management

Background Despite an enhanced interest and evolution in pain management, prevalence remains high. Interventions to optimize pain-related care can only be effective if barriers are identified and accounted for. Aim To assess pain intensity and examine its association with patient- (including health literacy defined in this study as “requiring help to read health information”), nurse-, and system-related (including social capital defined as “the importance of network and norms at work”) barriers/facilitators to pain management. Methods A two-center, cross-sectional study was performed between October 2012 and April 2013. The study included patients and nurses of 39 noncritical wards of two hospitals in Belgium. Patients who were 18 years of age or older and without impaired cognition or consciousness were eligible to take part. All nurses working in the included ward were invited to participate. Pain intensity and patient-related barriers were collected by a structured and standardized questionnaire, completed in dialogue with the patient. Nurses completed the questionnaire on the nurse- and system-related barriers and the social capital scale. Multilevel analysis was used to analyze the data because of the hierarchical structure of the data. Results The average pain of all patients across all wards on a 0–10 scale was 2.2 (SD = 3.6). The multilevel analysis indicates that pain intensity can be explained by variables at patient and ward levels. A significant independent association was found between higher pain intensity and younger age, receiving pain medication, the conviction of patients that pain medication does not improve pain, inadequate health literacy in patients, nurses without advanced education, and nurse’s concerns about side effects. Social capital did not emerge as predictor of pain intensity. Discussion Patient and nurse level factors should be taken into account in hospitals when setting up strategies to improve pain management.
PLANWELL: spatial user interface for collaborative petroleum well-planning

We present our prototype of PlanWell, a spatial augmented reality interface that facilitates collaborative field operations. PlanWell allows a central overseer (in a command and control center) and a remote explorer (an outdoor user in the field) to explore and collaborate within a geographical area. PlanWell provides the overseer with a tangible user interface (TUI) based on a 3D printout of surface geography which acts as a physical representation of the region to be explored. Augmented reality is used to dynamically overlay properties of the region as well as the presence of the remote explorer and their actions on to the 3D representation of the terrain. The overseer is able to perform the actions directly on the TUI and then the overseer's actions are presented as dynamic AR visualizations superimposed on the explorer's view in the field. Although our interface could applied to many domains, the PlanWell prototype was developed to facilitate petroleum engineering tasks such as well planning and coordination of drilling operations. Our paper describes the details of the design and implementation of the current PlanWell prototype in the context of petroleum well planning and drilling, and discusses some of the preliminary reflections of two focus group sessions with domain experts.
Progression of Mechanical Properties during On-field Sprint Running after Returning to Sports from a Hamstring Muscle Injury in Soccer Players

Abstract The objectives of this study were to examine the consequences of an acute hamstring injury on performance and mechanical properties of sprint-running at the time of returning to sports and after the subsequent ~2 months of regular soccer training after return. 28 semi-professional male soccer players, 14 with a recent history of unilateral hamstring injury and 14 without prior injury, participated in the study. All players performed two 50-m maximal sprints when cleared to return to play (Test 1), and 11 injured players performed the same sprint test about 2 months after returning to play (Test 2). Sprint performance (i. e., speed) was measured via a radar gun and used to derive linear horizontal force-velocity relationships from which the following variables obtained: theoretical maximal velocity (V 0 ), horizontal force (F H0 ) and horizontal power (Pmax). Upon returning to sports the injured players were moderately slower compared to the uninjured players. F H0 and Pmax were also substantially lower in the injured players. At Test 2, the injured players showed a very likely increase in F H0 and Pmax concomitant with improvements in early acceleration performance. Practitioners should consider assessing and training horizontal force production during sprint running after acute hamstring injuries in soccer players before they return to sports.
Place-dependent people tracking

People detection and tracking are important in many situations where robots and humans work and live together. But unlike targets in traditional tracking problems, people typically move and act under the constraints of their environment. The probabilities and frequencies for when people appear, disappear, walk or stand are not uniform but vary over space making human behavior strongly place-dependent. In this paper we present a model that encodes spatial priors on human behavior and show how the model can be incorporated into a people-tracking system. We learn a non-homogeneous spatial Poisson process that improves data association in a multi-hypothesis target tracker through more informed probability distributions over hypotheses. We further present a place-dependent motion model whose predictions follow the space-usage patterns that people take and which are described by the learned spatial Poisson process. Large-scale experiments in different indoor and outdoor environments using laser range data, demonstrate how both extensions lead to more accurate tracking behavior in terms of data-association errors and number of track losses. The extended tracker is also slightly more efficient than the baseline approach. The system runs in real-time on a typical desktop computer.
Learning the Optimal Neighborhood Kernel for Classification

Kernel methods have been applied successfully in many applications. The kernel matrix plays an important role in kernel-based learning methods, but the "ideal" kernel matrix is usually unknown in practice and needs to be estimated. In this paper, we propose to directly learn the "ideal" kernel matrix (called the optimal neighborhood kernel matrix) from a pre-specified kernel matrix for improved classification performance. We assume that the prespecified kernel matrix generated from the specific application is a noisy observation of the ideal one. The resulting optimal neighborhood kernel matrix is shown to be the summation of the pre-specified kernel matrix and a rank-one matrix. We formulate the problem of learning the optimal neighborhood kernel as a constrained quartic problem, and propose to solve it using two methods: level method and constrained gradient descent. Empirical results on several benchmark data sets demonstrate the efficiency and effectiveness of the proposed algorithms.
The Effect of Disc Radius on Heat Flux and Temperature Distribution in Friction Clutches

Friction clutch is an important machine element, which transmits the torque from the engine to the gearbox. Hence, an accurate estimation of the heat flux and temperatures on the friction surfaces is essential for proper design of the clutch to avoid failure due to thermal stresses. The dimensionless radius ratio (R=inner disc radius/outer disc radius) is the main key quantifying the quantities of heat flux and temperature on friction materials of clutch. Furthermore, the effect of (R) on the axial force, inner and outer radius, peripheral velocity and torque is investigated as well. The MATLAB and ANSYS have been used to perform the numerical calculation in this paper.
Diagnostic Imaging for Structural Damage

There has been increasing awareness of the use of intuitional imaging techniques to describe a damage event in the engineered structures. A Lamb wave-based diagnostic imaging approach was developed in this study, by fusing the prior probabilities established by the sensors of an active sensor network at different spatial positions of the structure under inspection. Rather than pinpointing the damage location and shape with definitive parameters, such an approach was intended to probabilistically predict the occurrence of a damage event, which is in nature more consistent with the implication of ‘estimating’ damage in SHM than traditional approaches. As validation, the approach was employed to detect mono- and dual-delamination in CF/EP laminates, and the results were represented in probability contour diagrams, where the structural damage became intuitional. Other major benefits of the approach include the independence of its effectiveness on the number of damage and enhanced tolerance to noise/uncertainties.
A polynomial restriction lemma with applications

A polynomial threshold function (PTF) of degree d is a boolean function of the form f=sgn(p), where p is a degree-d polynomial, and sgn is the sign function. The main result of the paper is an almost optimal bound on the probability that a random restriction of a PTF is not close to a constant function, where a boolean function g is called δ-close to constant if, for some vε{1,-1}, we have g(x)=v for all but at most δ fraction of inputs. We show for every PTF f of degree d≥ 1, and parameters 0<δ, r≤ 1/16, that Pr∾ Rr [fρ is not δ-close to constant] ≤ √ #183;(logr-1· logδ-1)O(d2), where ρ ∾ Rr is a random restriction leaving each variable, independently, free with probability r, and otherwise assigning it 1 or -1 uniformly at random. In fact, we show a more general result for random block restrictions: given an arbitrary partitioning of input variables into m blocks, a random block restriction picks a uniformly random block ℓΕ [m] and assigns 1 or -1, uniformly at random, to all variable outside the chosen block ℓ. We prove the Block Restriction Lemma saying that a PTF f of degree d becomes δ-close to constant when hit with a random block restriction, except with probability at most m-1/2 #183; (logm#183; logδ-1)O(d2). As an application of our Restriction Lemma, we prove lower bounds against constant-depth circuits with PTF gates of any degree 1≤ d≪ √logn/loglogn, generalizing the recent bounds against constant-depth circuits with linear threshold gates (LTF gates) proved by Kane and Williams (STOC, 2016) and Chen, Santhanam, and Srinivasan (CCC, 2016). In particular, we show that there is an n-variate boolean function Fn Ε P such that every depth-2 circuit with PTF gates of degree d≥ 1 that computes Fn must have at least (n3/2+1/d)#183; (logn)-O(d2) wires. For constant depths greater than 2, we also show average-case lower bounds for such circuits with super-linear number of wires. These are the first super-linear bounds on the number of wires for circuits with PTF gates. We also give short proofs of the optimal-exponent average sensitivity bound for degree-d PTFs due to Kane (Computational Complexity, 2014), and the Littlewood-Offord type anticoncentration bound for degree-d multilinear polynomials due to Meka, Nguyen, and Vu (Theory of Computing, 2016). Finally, we give derandomized versions of our Block Restriction Lemma and Littlewood-Offord type anticoncentration bounds, using a pseudorandom generator for PTFs due to Meka and Zuckerman (SICOMP, 2013).
Word Segmentation for Akkadian Cuneiform

We present experiments on word segmentation for Akkadian cuneiform, an ancient writing system and a language used for about 3 millennia in the ancient Near East. To our best knowledge, this is the first study of this kind applied to either the Akkadian language or the cuneiform writing system. As a logosyllabic writing system, cuneiform structurally resembles Eastern Asian writing systems, so, we employ word segmentation algorithms originally developed for Chinese and Japanese. We describe results of rule-based algorithms, dictionary-based algorithms, statistical and machine learning approaches. Our results may indicate possible promising steps in cuneiform word segmentation that can create and improve natural language processing in this area.
Planar Visual Metrology using Partition-based Camera Calibration

In visual metrology the real imaging procedure is a complicate nonlinear system. Even after rectification of the radial distortion of the lens, it is hard to achieve high accuracy with single group of planar extrinsic parameters. According to the characteristics of the radial distortion of lens, a partition-based camera extrinsic calibration method for planar visual measurement is proposed. The image plane is partitioned into 9 subspaces according to the lens radial distortion, and in each subspace one group of extrinsic parameters of objects plane is calibrated separately. Although there is only one objects plane where the visual measurement is to be done, the groups of extrinsic parameters calibrated in each subspace are different because of the nonlinear in real system. In the subspace that the image points belong to, the corresponding group of extrinsic parameters of the subspace is adopted to reconstruct the 3D positions of the objects in the camera coordinate system. Finally experimental results show that the partition-based calibration method performs well in visual metrology. Compared with the method which uses single group of extrinsic parameters the proposed method can achieve higher accuracy in measuring distance of planar objects from their perspective images.
The CLaC Discourse Parser at CoNLL-2015

This paper describes our submission (kosseim15) to the CoNLL-2015 shared task on shallow discourse parsing. We used the UIMA framework to develop our parser and used ClearTK to add machine learning functionality to the UIMA framework. Overall, our parser achieves a result of 17.3 F1 on the identification of discourse relations on the blind CoNLL-2015 test set, ranking in sixth place.
Influence of Fabric Mechanical Property on Clothing Dynamic Pressure Distribution and Pressure Comfort on Tight-Fit Sportswear

Clothing pressure distribution in tight-fit sportswear was simulated by using numerical computational method. Three sets of tight-fit sportswears were simulated on the basis of their mechanical properties. In addition, a 3D human body was also simulated in order to investigate its interaction with sportswear in the wearing process. Result shows that pressure increases significantly around the waist girth until it passes through body pelvis during the wearing process. Meanwhile, pressure begins to increase for other tested body locations. Based on body contour plot, we found that pressure distribution was not uniformly distributed and high-pressure zone was concentrated around the waist girth at the end of the wearing process. The simulated pressure has no significant difference (p > 0.05) with the experimental pressure measurements. Furthermore, subjective pressure comfort rating was linearly correlated with the simulation, suggesting the simulation method can provide reliable prediction in term of pressure comfort. From the tight-fit sportswear manufacturer's point of view, simulation result would help them to satisfy consumers' physiological and psychological needs in term of pressure comfort at a lower cost.
Breaking abstractions and unstructuring data structures

To ensure platform independence, mobile programs are distributed in forms that are isomorphic to the original source code. Such codes are easy to decompile, and hence they increase the risk of malicious reverse engineering attacks. Code obfuscation is one of several techniques which has been proposed to alleviate this situation. An obfuscator is a tool which-through the application of code transformations-converts a program into an equivalent one that is more difficult to reverse engineer. In a previous paper (Collberg et al., 1998) we have described the design of a control flow obfuscator for Java. In this paper we extend the design with transformations that obfuscate data structures and abstractions. In particular we show how to obfuscate classes, arrays, procedural abstractions and built-in data types like strings, integers and booleans.
Absence of mammals and the evolution of New Zealand grasses

Anthropogenic alteration of biotic distributions and disturbance regimes has dramatically changed the evolutionary context for the differentiation of species traits. Some of the most striking examples in recent centuries have been on islands where flightless birds, which evolved in the absence of mammalian carnivores, have been decimated following the widespread introduction of exotic predators. Until now, no equivalent case has been reported for plants. Here, we make use of robust analytical tools and an exceptionally well-sampled molecular phylogeny to show that a majority of New Zealand danthonioid grasses (Poaceae) may have adapted to the relaxed vertebrate herbivore pressure during the late Cenozoic through the development of a distinctive and unusual habit: abscission of old leaves. This feature occurs in only about 3 per cent of the world's roughly 11 000 grass species and has been empirically shown to increase plant productivity but to reduce protection against mammal grazing. This result suggests that release from a selective pressure can lead to species radiations. This seemingly anachronistic adaptation may represent an overlooked factor contributing to the severe decline in the geographical extent and species diversity of New Zealand's indigenous grasslands following the introduction of herbivorous terrestrial mammals in the 19th century.
Constitutive class I-restricted exogenous presentation of self antigens in vivo

Ovalbumin (OVA)-specific CD8+ T cells from the T cell receptor- transgenic line OT-I (OT-I cells) were injected into unirradiated transgenic RIP-mOVA mice, which express a membrane-bound form of OVA (mOVA) in the pancreatic islet beta cells and the renal proximal tubular cells. OT-I cells accumulated in the draining lymph nodes (LN) of the kidneys and pancreas and in no other LN. They displayed an activated phenotype and a proportion entered cell cycle. Unilateral nephrectomy 7-13 d before inoculation of OT-I cells into RIP-mOVA mice allowed the injected T cells to home only to the regional LN of the remaining kidney (and pancreas), but when the operation was performed 4 h before injecting the T cells, homing to the LN of the excised kidney was evident. When the bone marrow of RIP-mOVA mice was replaced with one of a major histocompatibility haplotype incapable of presenting OVA to OT-I cells, no homing or activation was detectable. Therefore, OT-I cells were activated by OVA presented by short-lived antigen-presenting cells of bone marrow origin present in the draining LN of OVA- expressing tissue. These results provide the first evidence that tissue- associated "self" antigens can be presented in the context of class I via an exogenous processing pathway. This offers a constitutive mechanism whereby T cells can be primed to antigens that are present in nonlymphoid tissues, which are not normally surveyed by recirculating naive T cells.
Reconstruction and Clustering in Random Constraint Satisfaction Problems

Random instances of constraint satisfaction problems (CSPs) appear to be hard for all known algorithms when the number of constraints per variable lies in a certain interval. Contributing to the general understanding of the structure of the solution space of a CSP in the satisfiable regime, we formulate a set of technical conditions on a large family of random CSPs and prove bounds on three most interesting thresholds for the density of such an ensemble: namely, the satisfiability threshold, the threshold for clustering of the solution space, and the threshold for an appropriate reconstruction problem on the CSPs. The bounds become asymptoticlally tight as the number of degrees of freedom in each clause diverges. The families are general enough to include commonly studied problems such as random instances of Not-All-Equal SAT, k-XOR formulae, hypergraph 2-coloring, and graph k-coloring. An important new ingredient is a condition involving the Fourier expansion of clauses, which characterizes the class of .
Animal Mitochondrial DNA as We Do Not Know It: mt-Genome Organization and Evolution in Nonbilaterian Lineages

Abstract Animal mitochondrial DNA (mtDNA) is commonly described as a small, circular molecule that is conserved in size, gene content, and organization. Data collected in the last decade have challenged this view by revealing considerable diversity in animal mitochondrial genome organization. Much of this diversity has been found in nonbilaterian animals (phyla Cnidaria, Ctenophora, Placozoa, and Porifera), which, from a phylogenetic perspective, form the main branches of the animal tree along with Bilateria. Within these groups, mt-genomes are characterized by varying numbers of both linear and circular chromosomes, extra genes (e.g. atp9, polB, tatC), large variation in the number of encoded mitochondrial transfer RNAs (tRNAs) (0–25), at least seven different genetic codes, presence/absence of introns, tRNA and mRNA editing, fragmented ribosomal RNA genes, translational frameshifting, highly variable substitution rates, and a large range of genome sizes. This newly discovered diversity allows a better understanding of the evolutionary plasticity and conservation of animal mtDNA and provides insights into the molecular and evolutionary mechanisms shaping mitochondrial genomes.