Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of a care planning intervention for reducing depression in older people in residential care

Approximately 40% of older people in residential care have significant symptoms of depression. A training and care-planning approach to reducing depression was implemented for 114 depressed residents living in 14 residential care homes in North Yorkshire, UK. Care staff were offered brief mental health training by community mental health teams for older people. They were then assigned to work individually with residents in implementing the care-planning intervention, which was aimed at alleviating depression and any health, social or emotional factors that might contribute to the resident's depression. Clinically significant improvements in depression scores were associated with implementation of the care-planning intervention as evidenced by changes in scores on the Geriatric Mental State Schedule—Depression Scale. There was evidence of an interaction between the power of the intervention and degree of dementia. These improvements were not accounted for by any changes in psychotropic medication. The training was highly valued by care staff and heads of homes, and they considered that the care-planning intervention represented an improvement in quality of care for all residents, irrespective of levels of dementia. Staff also reported improvements in morale and increased confidence in the caring role as a result of their participation. The limitations of this study are discussed. On the basis of a growing body of evidence, it is argued that there is an urgent need for a suitably powered randomised controlled trial and economic evaluation, to test the cost-effectiveness of personalised care planning interventions aimed at reducing depression in older people in residential care.
Sequential Tool Use in Great Apes

Sequential tool use is defined as using a tool to obtain another non-food object which subsequently itself will serve as a tool to act upon a further (sub)goal. Previous studies have shown that birds and great apes succeed in such tasks. However, the inclusion of a training phase for each of the sequential steps and the low cost associated with retrieving the longest tools limits the scope of the conclusions. The goal of the experiments presented here was, first to replicate a previous study on sequential tool use conducted on New Caledonian crows and, second, extend this work by increasing the cost of retrieving a tool in order to test tool selectivity of apes. In Experiment 1, we presented chimpanzees, orangutans and bonobos with an out-of-reach reward, two tools that were available but too short to reach the food and four out-of-reach tools differing in functionality. Similar to crows, apes spontaneously used up to 3 tools in sequence to get the reward and also showed a strong preference for the longest out-of reach tool independently of the distance of the food. In Experiment 2, we increased the cost of reaching for the longest out-of reach tool. Now apes used up to 5 tools in sequence to get the reward and became more selective in their choice of the longest tool as the costs of its retrieval increased. The findings of the studies presented here contribute to the growing body of comparative research on tool use.
The Review of Language Studies in International Business : Suggestions and Future Directions for Japan

We conduct a systematic and narrative review of the literature in the area of language studies in international business (IB). Our contributions seek to outline the research of language in IB both in Western countries and in Japan. Moreover, we aim to open up the debate of language studies in IB in Japan as the most important contribution; we offer suggestions for future research in Japan based on prior studies and discuss managerial implications for Japanese corporations. The pertinent literature we use in this paper is based on chronological and categorical turns, by dividing language studies in IB into three stages: infancy, toddler and preschool stages.
Exploring motor system contributions to the perception of social information: Evidence from EEG activity in the mu/alpha frequency range

Putative contributions of a human mirror neuron system (hMNS) to the perception of social information have been assessed by measuring the suppression of EEG oscillations in the mu/alpha (8–12 Hz), beta (15–25 Hz) and low-gamma (25–25 Hz) ranges while participants processed social information revealed by point-light displays of human motion. Identical dynamic displays were presented and participants were instructed to distinguish the intention, the emotion, or the gender of a moving image of a person, while they performed an adapted odd-ball task. Relative to a baseline presenting a nonbiological but meaningful motion display, all three biological motion conditions reduced the EEG amplitude in the mu/alpha and beta ranges, but not in the low-gamma range. Suppression was larger in the intention than in the emotion and gender conditions, with no difference between the latter two. Moreover, the suppression in the intention condition was negatively correlated with an accepted measure of empathy (EQ), revealing that participants high in empathy scores manifested less suppression. For intention and emotion the suppression was larger at occipital than at central sites, suggesting that factors other than motor system were in play while processing social information embedded in the motion of point-light displays.
Commutator errors in large-eddy simulation

Commutator errors arise in large-eddy simulation of incompressible turbulent flow from the application of non-uniform filters to the continuity -- and Navier-Stokes equations. For non-uniform, high order filters with bounded moments the magnitude of the commutator errors is shown to be of the same order as that of the turbulent stress fluxes. Consequently, one cannot reduce the size of the commutator errors independently of the turbulent stress terms by any judicious construction of such filter operators. Independent control over the commutator errors compared to the turbulent stress fluxes can, instead, be obtained by appropriately restricting the spatial variations of the filter-width and filter-skewness. For situations in which the dynamical consequences of the commutator errors are significant, e.g., near solid boundaries, explicit similarity modelling for the commutator errors is proposed, including the application of Leray regularization. The performance of this commutator error parametrization is illustrated for the one-dimensional Burgers equation. The Leray approach is found to capture the filtered flow with higher accuracy than conventional similarity modelling, which is particularly relevant for large filter-width variations.
A Gait Recognition Method Based on Features Fusion and SVM

The algorithm based on multi-feature and SVM is proposed. The paper firstly uses wavelet de-noising for gait images. The text offers to use width descriptors as gait features and combines lower angle features. The Kernel-based fisher criterion and support vector machine is combined to classification and identification. The gait characteristic is extracted by KFDA, which can obtain the best projection direction and enhance the capacity of data classification. Then the support vector machine (SVM) models are trained by the decomposed feature vectors. The gaits are classified by the trained SVM models. The paper tries using wavelet kernel and obtains better result. This algorithm is applied to a data-set including thirty individuals. Extensive experimental results demonstrate that the proposed algorithm performs at an encouraging recognition rate of 91% and at a relatively lower computational cost.
A Mapping Heuristic for Minimizing Message Latency in Massively Distributed MCTS

We propose a topology-aware heuristic that significantly reduces the message latency for search trees of tree parallel Monte-Carlo Tree Search. There exist many communication-aware and topology-aware mappings. However, those mappings are not applicable to the hash driven parallel search techniques. This is because in hash driven parallel search each graph/tree node is randomly distributed based on a hash function and each edge is also randomly connected, so each computation cluster only knows about the tasks that are being executed on themselves, so it is not possible to do dynamic load balancing according to the current status of the network. To cope with that, we devise an heuristic based on the depth of each search tree node and the betweenness centrality of each computational cluster of the network topology. Our experimental results show that we can reduce the average message latency by 15% to 35%.
Exploring physical information cloth on a multitouch table

We expand multitouch tabletop information exploration by placing 2D information on a physically-based cloth in a shallow 3D viewing environment. Instead of offering 2D information on a rigid window or screen, we place our information on a soft flexible cloth that can be draped, pulled, stretched, and folded with multiple fingers and hands, supporting any number of information views. Combining our multitouch flexible information cloth with simple manipulable objects provides a physically-based information viewing environment that offers similar advantages to complex detail-in-context viewing. Previous detail-in-context views can be re-created by draping cloth over virtual objects in this physics simulation, thereby approximating many of the existing techniques by providing zoomed-in information in the context of zoomed-out information. These detail-in-context views are approximated because, rather than use distortion, the draped cloth naturally drapes and folds showing magnified regions within a physically understandable context. In addition, the information cloth remains flexibly responsive, allowing one to tweak, unfold, and smooth out regions as desired.
Incorporating the Conditional Speech Presence Probability in Multi-Channel Wiener Filter Based Noise Reduction in Hearing Aids

A multi-channel noise reduction technique is presented based on a Speech Distortion-Weighted Multi-channel Wiener Filter (SDW-MWF) approach that incorporates the conditional Speech Presence Probability (SPP). A traditional SDW-MWF uses a fixed parameter to a trade-off between noise reduction and speech distortion without taking speech presence into account. Consequently, the improvement in noise reduction comes at the cost of a higher speech distortion since the speech dominant segments and the noise dominant segments are weighted equally. Incorporating the conditional SPP in SDW-MWF allows to exploit the fact that speech may not be present at all frequencies and at all times, while the noise can indeed be continuously present. In speech dominant segments it is then desirable to have less noise reduction to avoid speech distortion, while in noise dominant segments it is desirable to have as much noise reduction as possible. Experimental results with hearing aid scenarios demonstrate that the proposed SDW-MWF incorporating the conditional SPP improves the signal-to-noise ratio compared to a traditional SDW-MWF.
Highly Compressed Zernike Moments by Smoothing

A novel methodology that improves the discrimination capabilities of the compressed feature vectors, used in pattern recognition tasks, is presented in this paper. The Zernike moment signals of some patterns are extracted and are compressed by using a typical wavelet compression technique. In order to increase the compression ratio without loosing a great amount of significant information, the moment signals are smoothed appropriately before the compression. As a result, highly compressed feature vectors that include enough classification information are derived. The resulted improved features are studied for their discriminative power through appropriate experiments.
Spectral–Spatial Hyperspectral Image Classification via Multiscale Adaptive Sparse Representation

Sparse representation has been demonstrated to be a powerful tool in classification of hyperspectral images (HSIs). The spatial context of an HSI can be exploited by first defining a local region for each test pixel and then jointly representing pixels within each region by a set of common training atoms (samples). However, the selection of the optimal region scale (size) for different HSIs with different types of structures is a nontrivial task. In this paper, considering that regions of different scales incorporate the complementary yet correlated information for classification, a multiscale adaptive sparse representation (MASR) model is proposed. The MASR effectively exploits spatial information at multiple scales via an adaptive sparse strategy. The adaptive sparse strategy not only restricts pixels from different scales to be represented by training atoms from a particular class but also allows the selected atoms for these pixels to be varied, thus providing an improved representation. Experiments on several real HSI data sets demonstrate the qualitative and quantitative superiority of the proposed MASR algorithm when compared to several well-known classifiers.
Enhanced Koszulity in Galois cohomology

Despite their central role in Galois theory, absolute Galois groups remain rather mysterious; and one of the main problems of modern Galois theory is to characterize which profinite groups are realizable as absolute Galois groups over a prescribed field. Obtaining detailed knowledge of Galois cohomology is an important step to answering this problem. In our work we study various forms of enhanced Koszulity for quadratic algebras. Each has its own importance, but the common ground is that they all imply Koszulity. Applying this to Galois cohomology, we prove that, in all known cases of finitely generated pro-p-groups, Galois cohomology is a Koszul algebra. In particular, we show that for all known cases where the maximal pro-p-quotient of the absolute Galois group is finitely generated, Galois cohomology is universally Koszul. Assuming the Elementary Type conjecture, this gives us infinitely many refinements of the Bloch-Kato Conjecture. We moreover obtain several unconditional results. Lastly, we show that all forms of enhanced Koszulity are preserved under certain natural operations, which generalizes results that were only known to hold in the commutative case.
Improving Convergence Rates in Multiagent Learning Through Experts and Adaptive Consultation

Multiagent learning (MAL) is the study of agents learning while in the presence of other agents who are also learning. As a field, MAL is built upon work done in both artificial intelligence and game theory. Game theory has mostly focused on proving that certain theoretical properties hold for a wide class of learning situations while ignoring computational issues, whereas artificial intelligence has mainly focused on designing practical multiagent learning algorithms for small classes of games. This thesis is concerned with finding a balance between the game-theory and artificial-intelligence approaches. We introduce a new learning algorithm, FRAME, which provably converges to the set of Nash equilibria in self-play, while consulting experts which can greatly improve the convergence rate to the set of equilibria. Even if the experts are not well suited to the learning problem, or are hostile, then FRAME will still provably converge. Our second contribution takes this idea further by allowing agents to consult multiple experts, and dynamically adapting so that the best expert for the given game is consulted. The result is a flexible algorithm capable of dealing with new and unknown games. Experimental results validate our approach.
A Constructive Restriction of the

We de(cid:0)ne a very natural restriction of the (cid:0)(cid:1)(cid:1)calculus which is stable under reduction and whose type system is a restriction of the Classical Natural Deduction to intuitionistic logic(cid:2) However(cid:3) we show that this system is in some sense degenerated unless we provide a native disjunction(cid:2) We prove that the system with native disjunction is conservative over DIS(cid:1)logic and also that DIS(cid:1)logic is constructive(cid:2) From a computational standpoint(cid:3) this restriction on (cid:0)(cid:1)(cid:1)terms prevents a coroutine from accessing the local environment of another coroutine(cid:2)
Flip-Flop Selection for Mixed Scan and Reset Design Based on Test Generation and Structure of Sequential Circuits

In this paper, a novel mixed selection methodology using flip-flops for scan and reset design is proposed. The method runs test generation for a sequential circuit to obtain reachable states of flip-flops and required states for hard-to-detect faults. The circuit is also explored so as to acquire the structural connection relationship among the flip-flops. By analyzing these three sets of information, the flip-flops can be arranged in an appropriate order for mixed partial scan and reset selection. Instead of selecting the best flip-flop to revise the circuit for the next test generation, we give first priority to independent flip-flops each time in order to reduce the number of iterations. Experimental results show that this method can achieve higher testability with fewer scan/reset flip-flops than can either the scan only or the previous mixed scan/reset methods.
Molecular Phylogeny and Biogeography of Petaurista Inferred from the Cytochrome b Gene, with Implications for the Taxonomic Status of P. caniceps, P. marica and P. sybilla

The polymorphic genus Petaurista includes a group of diverse species and subspecies that are adapted for gliding and arboreal life. This morphological diversity has resulted in taxonomic discrepancies, and molecular phylogenetic studies have been limited by taxon sampling. To clarify this controversial taxonomy, we used the cytochrome b gene to reconstruct the phylogeny to obtain a more accurate picture of the evolutionary relationships, species differentiation and divergence pattern of Petaurista. The results revealed a significant inconsistency between taxonomic designations, phylogeny and genetic distances. When 6 recognized species were included, species delimitation revealed 15 putative species, a finding that warrants a comprehensive morphological diagnosis and a re-assessment of the species status. The validity of P. caniceps and P. marica was discussed. An estimation of the molecular divergence time demonstrated that the diversification and speciation of Petaurista began during the later Miocene and may have been affected by the uplifting of the Qinghai-Tibet plateau and subsequent climate change.
Effect of Vitamin D3 on Biosynthesis of Estrogen in Porcine Granulosa Cells via Modulation of Steroidogenic Enzymes

Vitamin D3 is a fat-soluble secosteroid responsible for enhancing intestinal absorption of calcium, iron, and other materials. Vitamin D3 deficiency, therefore, can cause health problems such as metabolic diseases, and bone disorder. Female sex hormones including estrogen and progesterone are biosynthesized mainly in the granulosa cells of ovary. In this study, we isolated granulosa cells from porcine ovary and cultured for the experiments. In order to examine the effect of vitamin D3 on the ovarian granulosa cells, the mRNA and protein levels of genes were analyzed by real-time PCR and Western blot assay. The production of estrogen from the granulosa cells was also measured by the ELISA assay. Genes associated with follicle growth were not significantly altered by vitamin D3. However, it increases expression of genes involved in the estrogen-biosynthesis. Further, estrogen concentrations in porcine granulosa cell-cultured media increased in response to vitamin D3. These results showed that vitamin D3 is a powerful regulator of sex steroid hormone production in porcine granulosa cells, suggesting that vitamin D deficiency may result in inappropriate sexual development of industrial animals and eventually economic loss.
Localization using Cooperative AOA Approach

In this paper, A Cooperative AOA (Co-AOA) approach associated with either TDoA or the signal propagation model of RSSI is proposed. In the approach, sensor nodes within certain range are made into a group (Super-Node), cooperating to estimate the AOA with respect to the Super-Node. Theoretically deduction has been done to prove that the Super-Node is a virtual AOA capable node, while simulation done to show that the AOA estimation error of the RSSI case is acceptable. Therefore, the Co-AOA approach is feasible, since the RSSI case is proved feasible and the feasibility of TDoA case is self-evident. Then, two kinds of localization schemes using Co-AOA are proposed. Of which, The Co-AOA localization scheme (CALS) is a direct extension from the AOA scheme, and is as precise as the AOA scheme, while the m-DV-Hop schemes is a modified version of the DV-Hop scheme, considering the details of the hop-by-hop path through estimating the AOA using Co-AOA approach.
Management of inflammatory bowel disease in poor responders to infliximab

Infliximab (IFX) is an effective treatment for inducing and maintaining response in Crohn’s disease and ulcerative colitis patients. Some patients present lack of response or loss of response to IFX during maintenance therapy. Empirical management with combination therapy with an immunomodulator, IFX dose escalation, or switching IFX for another antitumor necrosis factor-α drug, mainly adalimumab, is common in clinical practice. Selecting the best choice with the help of serum drug concentrations and trough IFX antibody concentrations could be a very interesting approach. In addition to surgery, a broad spectrum of new drugs has been tested and could expand treatment options in the near future.
Adsmith: an efficient object-based distributed shared memory system on PVM

In this paper, we describe an object-based distributed shared memory called Adsmith. In an object-based DSM, the shared memory consists of many shared objects, through which the shared memory is accessed. Adsmith is built on top of PVM at the library layer using C++. PVM is used as the communication subsystem, because it is a de facto standard and encapsulates many system related details. Several mechanisms are used to improve the performance of Adsmith, such as release memory consistency, load/store-like memory accesses, nonblocking accesses, and atomic operations, etc. Performance results show that even though Adsmith is implemented on top of PVM, programs running on Adsmith can achieve a performance comparable with those running directly on PVM.
ROAD EXTRACTION AND ENVIRONMENT INTERPRETATION FROM LIDAR SENSORS

We present in this article a new vehicle dedicated to road surveying, equipped with a highly precise positioning system, 2D lidar scans and high definition color images. We focus at first on the sensors extrins ic calibration process. Once all sensors have been positioned in the same coordinates system, 3D realistic environments can be computed and interpreted. Moreover, an original algorithm for road extraction has been developed. This two-step method is based on the local road shape and does not rely on the presence of curbs or guardrails. Different uses of the RanSaC algorithm are employed, for road sides rough estimation in the first place, then for unlikely candidates elimination. Road boundary and center points are further processed for road width and curvature computation in order to feed a geographic information system. Finally, a simple extraction of traffi c signs and road markings is presented.
A personal networking solution

This paper presents an overview of research being conducted on Personal Networking Solutions within the Mobile VCE Personal Distributed Environment Work Area. In particular it attempts to highlight areas of commonality with the MAGNET initiative. These areas include trust of foreign devices and service providers, dynamic real-time service negotiation to permit context-aware service delivery, an automated controller algorithm for wireless ad hoc networks, and routing protocols for ad hoc networking environments. Where possible references are provided to Mobile VCE publications to enable further reading.