Miracle-dz/UniFAR-FTUs huggingface dataset:
Recognizing Parkinsonian Gait Pattern by Exploiting Fine-Grained Movement Function Features

Parkinson's disease (PD) is one of the typical movement disorder diseases among elderly people, which has a serious impact on their daily lives. In this article, we propose a novel computation framework to recognize gait patterns in patients with PD. The key idea of our approach is to distinguish gait patterns in PD patients from healthy individuals by accurately extracting gait features that capture all three aspects of movement functions, that is, stability, symmetry, and harmony. The proposed framework contains three steps: gait phase discrimination, feature extraction and selection, and pattern classification. In the first step, we put forward a sliding window--based method to discriminate four gait phases from plantar pressure data. Based on the gait phases, we extract and select gait features that characterize stability, symmetry, and harmony of movement functions. Finally, we recognize PD gait patterns by applying a hybrid classification model. We evaluate the framework using an open dataset that contains real plantar pressure data of 93 PD patients and 72 healthy individuals. Experimental results demonstrate that our framework significantly outperforms the four baseline approaches.
Tyrosine Hydroxylase and DOPA Decarboxylase Gene Variants in Personality Traits

Personality influences several characteristics of normal and pathologic behaviors and it is associated with neurotransmitter systems that are under genetic control. The dopaminergic system has been proposed to play a role in the modulation of personality traits. In the present study, variants of the tyrosine hydroxylase (TH) and DOPA decarboxylase (DDC) genes (for TH: rs3842727, rs6356; for DDC: rs1451371, rs1470750, rs998850) were investigated in 111 suicide attempters and 289 healthy subjects to assess the involvement of the dopaminergic synthesis pathway in personality traits. No strong evidence was found for the associations between personality and TH or DDC in overall tests. An interaction effect of genotype and diagnosis was present, with TH and DDC SNPs having a greater effect on the respective personality dimensions in the group of suicide attempters. Because of the risk of false positives, these findings should be interpreted with highest caution. Direct replication attempts within independent groups of suicide attempters will help to resolve this question.
PPP and the Real Exchange Rate - Real Interest Rate Differential Puzzle Revisited: Evidence from Non-Stationary Panel Data

This paper examines the evidence for two of the relationships that underpin (explicitly or implicitly) much of international macroeconomics. The first is purchasing power parity (PPP), or the hypothesis that there exists a constant long-run equilibrium real exchange rate. The second establishes a relationship between real exchange rates and real interest rate differentials. The tests are conducted on a panel of 18 OECD economies using the United States as a numeraire for the post-Bretton Woods era. The results are obtained using new non-stationary panel estimation techniques, which significantly increase the power of the tests. All the tests suggest that there is little evidence supporting PPP when it is tested directly. This contrasts with earlier panel data studies, which tended to find that the real exchange rate was stationary. The results supporting a long-run relationship between real exchange rates and real interest rate differentials appear to be more positive. This again provides a contrast with earlier results, which tended to find no evidence of cointegration. Such studies concentrated on G7 economies. To investigate this further the panel was split into two groups: the G7 and eleven small open economies. For the panel of small open economies strong evidence in favour of cointegration is found. In contrast, there is no evidence of cointegration in a panel that consists purely of the G7 economies.
Dynamic behavioral diversity

Behavioral Diversity (BD) aims at improving the evolution of robots by fostering exploration on the basis of their behavior, whereas evolutionary algorithms typically consider the diversity on a genotypic level. Several Behavioral Similarity Measures (BSM), the key component to improve behavioral diversity, have been investigated in the litterature. Current benchmarks show that (1) most tested BSM improve the final performance, (2) they do not lead to the same improvements and, (3) it is hard to predict a priori which BSM will work the best. Instead of trying to find the best BSM, a different approach is proposed here: assuming that several BSM are available, we propose to randomly switch between then each K generations (e.g. K=20). This new approach is tested on a ball collecting task. Results show that better fitness values are obtained with the random switch approach than with any single measure. In effect, the present contribution show that it is possible use behavioral diversity while avoiding to choose between behavioral similarity measures.
Metal-in-metal localised surface plasmon resonance

Anomalous strong resonances in silver and gold nano-porous thin films which conduct are found to arise from isolated metal nano-islands separated from the surrounding percolating metal network by a thin loop of insulator. This observed resonant optical response is modelled. The observed peak position is in agreement with the observed average dimensions of the silver core and insulator shell. As the insulating ring thickness shrinks the resonance moves to longer wavelengths and strengthens. This structure is the Babinet’s principle counterpart of dielectric core-metal shell nanoparticles embedded in dielectric. Like the latter tuning of resonant absorption is possible, but here the matrix reflects rather than transmits and tuning to longer wavelengths is more practical. A new class of metal mirror for a single thin layer is identified using the same resonances in dense metal mirrors. Narrow band deep localised dips in reflectance result.
Efficient quality enhancement of disparity maps based on alpha matting

We propose an efficient disparity map enhancement method that improves the alignment of disparity edges and color edges even in the presence of mixed pixels and provides alpha values for pixels at disparity edges as a byproduct. In contrast to previous publications, the proposed method addresses mixed pixels at disparity edges and does not introduce mixed disparities that can lead to object deformations in synthesized views. The proposed algorithm computes transparencies by performing alpha matting per disparity-layer. These alpha values indicate the degree of affiliation to a disparity-layer and can hence be used as an indicator for a disparity reassignment that aligns disparity edges with color edges and accounts for mixed pixels. We demonstrate the capabilities of the proposed method on various images and corresponding disparity maps, including images that contain fuzzy object borders (e.g., fur). Furthermore, the proposed method is qualitatively and quantitatively evaluated using disparity ground truth and compared to previously published disparity post-processing methods.
A New Differentially Private Data Aggregation With Fault Tolerance for Smart Grid Communications

Privacy-preserving data aggregation has been widely studied to meet the requirement of timely monitoring measurements of users while protecting individual's privacy in smart grid communications. In this paper, a new secure data aggregation scheme, named differentially private data aggregation with fault tolerance (DPAFT), is proposed, which can achieve differential privacy and fault tolerance simultaneously. Specifically, inspired by the idea of Diffie-Hellman key exchange protocol, an artful constraint relation is constructed for data aggregation. With this novel constraint, DPAFT can support fault tolerance of malfunctioning smart meters efficiently and flexibly. In addition, DPAFT is also enhanced to resist against differential attacks, which are suffered in most of the existing data aggregation schemes. By improving the basic Boneh-Goh-Nissim cryptosystem to be more applicable to the practical scenarios, DPAFT can resist much stronger adversaries, i.e., user's privacy can be protected in the honest-but-curious model. Extensive performance evaluations are further conducted to illustrate that DPAFT outperforms the state-of-the-art data aggregation schemes in terms of storage cost, computation complexity, utility of differential privacy, robustness of fault tolerance, and the efficiency of user addition and removal.
The Mauritian economy : a reader

Introduction and Overview R.Dabee & D.Greenaway Macroeconomic Policy and Performance R.Lamusse Exchange Rate and Balance of Payments Policy R.Dabee Structural Adjustment and Economic Growth in Mauritius D.Greenaway & N.Gooroochurn International Trade and Trade Policy in Mauritius C.Milner The Export Processing Zone R.Durbarry Tax Policy in Mauritius O.Morrissey Government Expenditure and Economic Development S.Nath Agriculture and sugar W.Morgan Evolution of the Financial System K.Junglee Labour Market Adjustment C.Milner & P.Wright Local Government Finance S.Nath Population and Demographics M.Pochun
Nonlinear convex control of the Furuta pendulum based on its descriptor model

This paper presents simulation and real-time results of a nonlinear control scheme for stabilization of the Furuta pendulum. Instead of the traditional state-space model, the proposal makes use of a convex exact rewriting of the nonlinear model in its descriptor form, which naturally arises in Lagrange-Euler dynamics. Convexity proves to be useful when combined with an extension of the direct Lyapunov method for singular systems, since it leads to design conditions in the form of linear matrix inequalities, which allow the use of convex programming techniques. Moreover, real-time implementation issues such as actuator saturation limits and decay rate are straightforwardly incorporated in the design. Numerical advantages of the descriptor convex model over the state-space representation are also discussed.
Supplemental Material THELYTOKOUS PARTHENOGENESIS IN EUSOCIAL HYMENOPTERA

The online supplemental material contains a detailed evaluation of the thelytokous bee and ant species and is divided in two sections. In the first section, we review and discuss the species in which thelytoky occurs frequently and comprises a significant role in the species’ life cycle. In the second section, we review the species in which thelytoky occurs infrequently, is restricted to orphaned colonies, or in which thelytoky has been suggested but either requires further validation or has been refuted.
Efficient in-network redundancy filtering in RFID system integrated with wireless sensor networks

RFID promises to revolutionize the process of identifying, tracking, and monitoring physical objects. Additionally, RFID integration with wireless sensor networks helps both in terms of scalability and low cost of networks. However, redundancies in RFID data occur due to two reasons: the methods needed to counteract the unreliable nature of radio frequency transmissions and read tags in overlapping reading ranges in RFID systems. Such redundancies increase network delay and consume limited resources of the network unnecessarily. This problem is recognized as a critical issue in RFID. To resolve this problem, we propose an efficient in-network redundancy filtering (EIRF) method in RFID-capable object tracking systems. Simulation results show that EDÎF reduces network delay and processing time in filtering, while eliminating considerable amount of redundant data.
Impact of Cache Coherence Protocols on the Power Consumption of STT-RAM-Based LLC

To gain higher density and lower leakage, STT-RAM has been considered an alternative to SRAM for implementing last-level caches (LLCs). However, STT-RAM requires high write energy to program. Consequently, frequent write-backs from the upper-level caches or cache fills from the main memory will result in high LLC power. Both the broadcast and write-back traffic are affected by the cache coherence protocol. In this paper, we study the impact of coherence protocols on the power consumption of the STT-RAM LLC, and the entire cache hierarchy (including the interconnection power). Based on full-system simulation executing multi-threaded benchmarks, we show that although for some of the workloads, different protocols produce very different broadcast or write-back traffic, for these workloads, the interconnection and the write-back power are only a small fraction of the overall power consumption. Cache coherence protocol thus has very little impact on the power of the STT-RAM LLC and the cache hierarchy.
A novel multifrequency technique for the detection of point sources in cosmic microwave background maps

In this work we address the problem of simultaneous multifrequency detection of extragalactic point sources in the maps of the cosmic microwave background. We apply a new linear filtering technique, the ‘matched matrix filters’, that incorporates full spatial information, including the cross-correlation among channels, without making any a priori assumption about the spectral behaviour of the sources. A substantial reduction of the background is achieved thanks to the optimal combination of filtered maps. We describe the new technique in detail and apply it to the detection of radio sources and estimation of their parameters in realistic all-sky Planck simulations at 30, 44, 70 and 100 GHz. Then, we compare the results with the single-frequency approach based on the standard matched filter, in terms of reliability, completeness and flux accuracy of the resulting point source catalogues. The new filters outperform the standard matched filters for all these indexes at 30, 44 and 70 GHz, whereas at 100 GHz both kinds of filters have a similar performance. We find a notable increment of the number of true detections for a fixed reliability level. In particular, for a 95 per cent reliability we practically double the number of detections at 30, 44 and 70 GHz.
Unspeakable Truths: Transitional Justice and the Challenge of Truth Commissions

1. Introduction 2. Confronting Past Crimes: Transitional Justice and the Phenomenon of Truth Commissions 3. Why a Truth Commission? 4. The Five Strongest Truth Commissions 5. Other Illustrative Truth Commissions 6. What is the Truth? 7. The Truth About Women and Men 8. Truth and Justice: A Careful but Critical Relationship 9. Truth Commissions and the International Criminal Court 10. Naming Names of Perpetrators 11. Healing from the Past 12. Truth and Reparations 13. Reconciliation and Reforms 14. Leaving the Past Alone 15. When, How, and Who: Basic Questions of Methodology and Operations 16. Reflections: Looking Forward
Presentation Skills Estimation Based on Video and Kinect Data Analysis

This paper identifies, by means of video and Kinect data, a set of predictors that estimate the presentation skills of 448 individual students. Two evaluation criteria were predicted: eye contact and posture and body language. Machine-learning evaluations resulted in models that predicted the performance level (good or poor) of the presenters with 68% and 63% of correctly classified instances, for eye contact and postures and body language criteria, respectively. Furthermore, the results suggest that certain features, such as arms movement and smoothness, provide high significance on predicting the level of development for presentation skills. The paper finishes with conclusions and related ideas for future work.
The Relationship between Affective States and Dialog Patterns during Interactions with AutoTutor

In an attempt to discover links between learning and emotions, this study adopted an emote-aloud procedure in which participants were recorded as they verbalized their affective states while interacting with an intelligent tutoring system, AutoTutor. Various characteristics and assessments of the participants’ interactions with AutoTutor were recovered by mining its log files. These interaction patterns were correlated to the affective states expressed by the participants. We identify significant correlations and speculate on their implications for the larger project of extending the AutoTutor system into a non-intrusive, affect-sensitive, intelligent
Systematic pelvic lymphadenectomy vs. no lymphadenectomy in early-stage endometrial carcinoma: randomized clinical trial.

BACKGROUND Pelvic lymph nodes are the most common site of extrauterine tumor spread in early-stage endometrial cancer, but the clinical impact of lymphadenectomy has not been addressed in randomized studies. We conducted a randomized clinical trial to determine whether the addition of pelvic systematic lymphadenectomy to standard hysterectomy with bilateral salpingo-oophorectomy improves overall and disease-free survival. METHODS From October 1, 1996, through March 31, 2006, 514 eligible patients with preoperative International Federation of Gynecology and Obstetrics stage I endometrial carcinoma were randomly assigned to undergo pelvic systematic lymphadenectomy (n = 264) or no lymphadenectomy (n = 250). Patients' clinical data, pathological tumor characteristics, and operative and early postoperative data were recorded at discharge from hospital. Late postoperative complications, adjuvant therapy, and follow-up data were collected 6 months after surgery. Survival was analyzed by use of the log-rank test and a Cox multivariable regression analysis. All statistical tests were two-sided. RESULTS The median number of lymph nodes removed was 30 (interquartile range = 22-42) in the pelvic systematic lymphadenectomy arm and 0 (interquartile range = 0-0) in the no-lymphadenectomy arm (P < .001). Both early and late postoperative complications occurred statistically significantly more frequently in patients who had received pelvic systematic lymphadenectomy (81 patients in the lymphadenectomy arm and 34 patients in the no-lymphadenectomy arm, P = .001). Pelvic systematic lymphadenectomy improved surgical staging as statistically significantly more patients with lymph node metastases were found in the lymphadenectomy arm than in the no-lymphadenectomy arm (13.3% vs 3.2%, difference = 10.1%, 95% confidence interval [CI] = 5.3% to 14.9%, P < .001). At a median follow-up of 49 months, 78 events (ie, recurrence or death) had been observed and 53 patients had died. The unadjusted risks for first event and death were similar between the two arms (hazard ratio [HR] for first event = 1.10, 95% CI = 0.70 to 1.71, P = .68, and HR for death = 1.20, 95% CI = 0.70 to 2.07, P = .50). The 5-year disease-free and overall survival rates in an intention-to-treat analysis were similar between arms (81.0% and 85.9% in the lymphadenectomy arm and 81.7% and 90.0% in the no-lymphadenectomy arm, respectively). CONCLUSION Although systematic pelvic lymphadenectomy statistically significantly improved surgical staging, it did not improve disease-free or overall survival.
Impact of Principal Component Analysis in the Application of Image Processing

Principal component analysis (PCA) is a classical statistical method. It is based on the statistical representation of a random variable. This linear transform has been widely used in data analysis and compression.Impact of PCA is affecting the research work in now a days in the various field like application of Image Processing, pattern recognition ,Neural network and etc . We know that rice is one of the most widely cultivated food crops throughout the world. Demand for rice as a major food item continues to increase and it is estimated that we will have to produce 50% more rice by the year 2025(2). The several diseases most of them are caused by the bacteria, fungus, virus, and parasite etc affect Rice plants. Diseases affect all the parts of the rice plants including the grain and the root, but mainly in aerial part of the plant i.e. stem and leave. Due to the damages by diseases and pest a large percentages of the production gets lost. Only way to prevent this loss is to timely diagnosis of the field problem and to take the appropriate measure. The diagnosis of the field problems is done manually which may causes improper diagnosis and may not be timely. Thus now a day's using the image processing and soft computing techniques some work has been started to automatically diagnosis the field problem (7). One of the most important part of this automatic diagnosis processes is to identify the location of the damage caused by the pest or diseases. Thus in my work we have tried to classify the rice leaf and the stem images with the help of image processing techniques. In my work for this reason I have used the gray, green, hue and intensity distribution of the images vertically to the strip of the leaf and the stem at hundred-pixel interval as a feature vector. Among them I have accepted the result of intensity distribution. Because we know that the leaf surfaces are flat the distribution of intensity will be same along the direction but in case of the stem, the intensity will be increase towards the center and again decrease to the center to the boundary, as they are cylindrical in shape. Then I have applied the principal component analysis (PCA) to reduce the dimension of the feature vector to 7*1. We have used the Baye's Classifier for the classification process with the accuracy of the 70% for leaf and 65% for stem, which is acceptable for the first try.
A branch&bound algorithm for solving one-dimensional cutting stock problems exactly

Many numerical computations reported in the literature show an only small diierence between the optimal value of the one-dimensional cutting stock problem (1CSP) and that of its corresponding linear programming relaxation. Moreover, theoretical investigations have proven that this difference is smaller than 2 for a wide range of subproblems of the general 1CSP. In this paper we give a branch&bound algorithm to compute optimal solutions for instances of the 1CSP. Numerical results are presented of about 900 randomly generated instances with up to 100 small pieces and all of them are solved to optimality.
Climate change and the selective signature of the Late Ordovician mass extinction

Selectivity patterns provide insights into the causes of ancient extinction events. The Late Ordovician mass extinction was related to Gondwanan glaciation; however, it is still unclear whether elevated extinction rates were attributable to record failure, habitat loss, or climatic cooling. We examined Middle Ordovician-Early Silurian North American fossil occurrences within a spatiotemporally explicit stratigraphic framework that allowed us to quantify rock record effects on a per-taxon basis and assay the interplay of macrostratigraphic and macroecological variables in determining extinction risk. Genera that had large proportions of their observed geographic ranges affected by stratigraphic truncation or environmental shifts at the end of the Katian stage were particularly hard hit. The duration of the subsequent sampling gaps had little effect on extinction risk, suggesting that this extinction pulse cannot be entirely attributed to rock record failure; rather, it was caused, in part, by habitat loss. Extinction risk at this time was also strongly influenced by the maximum paleolatitude at which a genus had previously been sampled, a macroecological trait linked to thermal tolerance. A model trained on the relationship between 16 explanatory variables and extinction patterns during the early Katian interval substantially underestimates the extinction of exclusively tropical taxa during the late Katian interval. These results indicate that glacioeustatic sea-level fall and tropical ocean cooling played important roles in the first pulse of the Late Ordovician mass extinction in Laurentia.