Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Spatio-Temporal Directed Graph Neural Network for Parkinson's Detection using Vertical Ground Reaction Force

Vertical Ground Reaction Force (VGRF) signal obtained from foot-worn sensors, also known as plantar data, provides a highly informative and detailed representation of an individual's gait features. Existing methods, such as CNNs, LSTMs and Transformers, have revealed the efficiency of deep learning in Parkinson's Disease (PD) diagnosis using VGRF signal. However, the intrinsic topologic graph and pressure transmission characteristics of plantar data are overlooked in those approaches, which are essential features for gait analysis. In this paper, we propose to construct a plantar directed topologic graph to fully exploit the plantar topology in gait circles. It can facilitate the expression of gait information by representing sensors as nodes and pressure transmissions as directional edges. Accordingly, an Adaptive Spatio-Temporal Directed Graph Neural Network (AST-DGNN) is proposed to extract the connection features of the plantar directed topologic graph. Each AST-DGNN Unit includes an Adaptive Directed Graph Network (ADGN) block and a Temporal Convolutional Network (TCN) block. In order to capture both local and global spatial relationships among sensor nodes and pressure transmission edges, the ADGN block performs message passing on the plantar directed topologic graph in an adaptive manner. To capture the temporal features of sensor nodes and pressure transmission edges, the TCN block defines a temporal feature extraction process for each node and edge in the graph. Moreover, the data augmentation is introduced for plantar data to improve the generalization ability of the AST-DGNN. Experimental results on Ga, Ju, and Si datasets demonstrate that the proposed method outperforms the existing methods under both cross-dataset validation and mixed-data cross-validation. Especially in cross-dataset validation, there is an average improvement of 2.13%, 7.73%, and 12.27% in accuracy, F1 score, and G-mean, respectively.
MRAC'23: 1st International Workshop on Multimodal and Responsible Affective Computing

Multimodal emotion recognition has become an important research topic due to its wide applications in human-computer interaction. Over the last few decades, the technology has made remarkable progress with the development of deep learning. However, existing technologies are hard to meet the demand for practical applications. To this end, we organize this workshop to bring together researchers in this field to further discuss recent research and future directions.
On The Efficient Determination of Most Near Neighbors

The time-worn aphorism "close only counts in horseshoes and hand-grenades" is clearly inadequate. Close also counts in golf, shuffleboard, archery, darts, curling, and other games of accuracy in which hitting the precise center of the target isn't to be expected every time, or in which we can expect to be driven from the target by skilled opponents. This lecture is not devoted to sports discussions, but to efficient algorithms for determining pairs of closely related web pages -- and a few other situations in which we have found that inexact matching is good enough; where proximity suffices. We will not, however, attempt to be comprehensive in the investigation of probabilistic algorithms, approximation algorithms, or even techniques for organizing the discovery of nearest neighbors. We are more concerned with finding nearby neighbors; if they are not particularly close by, we are not particularly interested. In thinking of when approximation is sufficient, remember the oft-told joke about two campers sitting around after dinner. They hear noises coming towards them. One of them reaches for a pair of running shoes, and starts to don them. The second then notes that even with running shoes, they cannot hope to outrun a bear, to which the first notes that most likely the bear will be satiated after catching the slower of them. We seek problems in which we don't need to be faster than the bear, just faster than the others fleeing the bear.
LandmarkGait: Intrinsic Human Parsing for Gait Recognition

Gait recognition is an emerging biometric technology for identifying pedestrians based on their unique walking patterns. In past gait recognition, global-based methods are inadequate to meet the growing demand for accuracy, while commonly used part-based methods provided coarse and inaccurate feature representation for specific body parts. Human parsing appears to be a better option for accurately representing specific and complete body parts in gait recognition. However, its practical application in gait recognition is often hindered by missing RGB modality, lack of annotated body parts, and difficulty in balancing parsing quantity and quality. To address this issue, we propose LandmarkGait, an accessible and alternative parsing-based solution for gait recognition. LandmarkGait introduces an unsupervised landmark discovery network to transform the dense silhouette into a finite set of landmarks with remarkable consistency across various conditions. By grouping landmarks subsets corresponding to distinct body part regions, following a reconstruction task and further refinement from high-quality input silhouettes, we can directly obtain fine-grained parsing results from original binary silhouettes in an unsupervised manner. Moreover, we also develop a multi-scale feature extractor that simultaneously captures global and parsing feature representations based on the integrity and flexibility of specific body parts. Extensive experiments demonstrate that our LandmarkGait can extract more stable features and exhibit significant performance improvement under all conditions, especially in various dressing conditions. Code is available at https://github.com/wzb-bupt/LandmarkGait.
Embedding Based Multilingual Atlas of Semantic Fields

Semantic fields(domains) are an important construct in neuroscience, linguistics, psychology, and natural language processing. However, semantic field resources typically lack scalability and are not based on language usage, but on scientific and commercial taxonomies. The present project aims to create maps of semantic fields for multiple languages constructed from Word Embeddings. The clustering process is systematically described, and preliminary results for the Spanish language are presented, showing similarities and differences compared to current classifications. The present work opens up possibilities for a usage-based word classification of semantic fields and for the generation of language atlases that allow for multilingual comparison and improve the development of the aforementioned disciplines.
Computing ABox Justifications for Query Answers via Datalog Rewriting

Justifications are a very useful tool for explaining DL consequences. They highlight parts of the ontology that are responsible for the consequence, and can serve as the basis for more detailed explanations such as proofs. In this paper, we present an approach that can compute ABox justifications, i.e., justifications restricted to assertions, for answers to conjunctive queries, assuming that these queries are Datalog-rewritable over the input ontology. We implemented the approach based on the rewriting tool Clipper and ProvSQL, which can be used to compute provenance information, including justifications, for SQL queries. The potentially recursive nature of Datalog rewritings does not allow a direct translation into SQL queries, but requires some additional processing steps, depending on the cyclic structure of the Datalog program and the ABox. We show that the set of all ABox justifications can be computed in reasonable time, and compare the performance with Soufflé, a Datalog engine that also supports explanations.
EFFECTS OF FISCAL POLICY SHOCKS IN CE3 COUNTRIES (TVAR APPROACH)

The real output deterioration, high ﬁ scal de ﬁ cits and increased sovereign debt burden represents key phenomena that affected the maneuverability of ﬁ scal authorities in the early crisis years. Controversy between ﬁ scal sustainability and ﬁ scally driven economic recovery fueled a large number of academic and policy discussions about the appropriate response of governments to the crisis challenges. Empirical literature provides mixed evidence about the effects of ﬁ scal policy adjustments on the macroeconomic performance. Moreover, pro-cyclical patterns in ﬁ scal policies of many countries during the pre-crisis period did not reveal clear lessons learned that would be bene ﬁ cial for ﬁ scal authorities during the crisis years. In the paper we examine effects of the ﬁ scal policy shocks in CE3 (the Slovak Republic, the Czech Republic and Hungary) within different stages of the business cycle by employing threshold vector autoregression (TVAR) model. We calculate ﬁ scal multipliers and generalized impulse-response functions to assess the responsiveness of the real output to the ﬁ scal policy adjustments. The main objective is to determine whether effects of the ﬁ scal policy shocks differ during expansion and recession. Our results indicate that the size of ﬁ scal multipliers and responsiveness of the real output are generally higher for spending ﬁ scal shocks while effects of revenue ﬁ scal shocks are much less dynamic in all three countries. While the effects of the ﬁ scal spending shocks are more dynamic during recession in the Czech Republic and Hungary, ﬁ scal spending multipliers in the Slovak Republic are generally high during the recession as well though higher during expansion. Moreover, differences in the responsiveness of the real output are slightly higher in case of the expenditure based ﬁ scal adjustments in all three countries (in terms of both, regimes and sub-periods).
Inversion for sources of crustal deformation and gravity change at the Yellowstone caldera

The 3100 km2 Yellowstone caldera, Yellowstone National Park, was formed in the latest of three explosive eruptions of rhyolites and ash flow tuffs totalling 3700 km3 at 2, 1.2, and 0.6 m.y. before present. Its youthful volcanic history, widespread hydrothermal activity, intense seismicity, and extremely high heat flow, in excess of 30 times the continental average, marks the Yellowstone volcanic system as a giant caldera at unrest Orthometric height increases of the caldera of up to 76 cm, measured from precise leveling surveys from 1923 to 1975–1977, were inverted to determine volume expansion source models for the caldera-wide deformation. For the 1923 to 1977 uplift episode, two regions of expansion were found: (1) in the northern part of the caldera near the Sour Creek resurgent dome of ∼0.37 km3, and (2) in the southern part of the caldera, near the Mallard Lake resurgent dome of ∼0.41 km3. Both bodies occur in the upper crust from near-surface depths to 6.0 km, but the largest volume expansions were found in the 3.0–6.0 tan depth range. The southern caldera source volume, near the Mallard Lake dome, may extend down to 9.0 km. The data are, however, unable to resolve if these two resurgent domes, separated laterally by ∼40 km, are connected. Prom 1976 to 1987, nearly simultaneous measurements of elevation and gravity changes were made on a profile across the northern caldera during a period of net uplift. Models of the temporal gravity variation infer that the volume increase for the northern caldera source must lie above 9.0 km and involved a density perturbation greater than +0.002 g/cm3. The modeled volumetric sources are in the same general locations as bodies of low P wave velocities, high seismic attenuation, and large negative Bouguer gravity anomalies. In view of the intense Quaternary volcanism, the anomalously high heat flow, and the correlation of the source volumes with the regional geophysical anomalies, it is likely that the modeled volumetric increases were caused by migration of magmas and/or the introduction of large volumes of hydrothermal fluids into the upper crust.
National Quail

Northern bobwhites ( Colinus virginianus ) have experienced severe population declines across their distribution. In order to address population declines and to continue providing hunting opportunities, multistate efforts have been undertaken to stabilize and restore bobwhite populations. Ongoing efforts using the National Bobwhite Conservation Initiative’s quail focus area approach have so far demonstrated success throughout Kentucky. However, population increases in the Peabody Bobwhite Focal Area, in western Kentucky, have not been correlated to increases in perceived hunter success. Consequently, some sportsmen question the effectiveness of focal area conservation. In response to hunter concerns, we tested dog hunting ability with wild and pen-reared bobwhites. We also measured evasive behaviors of wild bobwhite using radiotelemetry. During the 2013–2014 and 2014–2015 hunting seasons we conducted 114 dog trials. Dogs detected bobwhite during 46 of 59 (78.0%) pen-reared trials and 16 of 55 (29.1%) wild bird trials. When dogs did not detect wild quail, birds ran away 64.1% of the time and remained motionless 20.5% of the time. Using an information-theoretic approach, we determined that bird type (wild vs. pen-reared) had a signiﬁcant effect on bird detection, with dogs 8.62 times more likely to detect pen-reared birds than wild birds. We recommend that hunters be informed about differences in dog detection rates between pen-reared and wild bobwhite so that public support needed for wild bobwhite restoration can persist.
Center for Policy Research Working Paper No . 129 TESTING FOR BREAKS IN COINTEGRATED PANELS WITH COMMON AND IDIOSYNCRATIC STOCHASTIC TRENDS

In this paper, we develop tests for structural change in cointegrated panel regressions with common and idiosyncratic trends. We consider both the cases of observable and nonobservable common trends, deriving a Functional Central Limit Theorem for the partial sample estimators under the null of no break. We show that tests based on sup-Wald statistics are powerful versus breaks of size   1/ p O nT , also proving that power is present when the time of change differs across units and when only some units have a break. Our framework is extended to the case of cross correlated regressors and endogeneity. Monte Carlo evidence shows that the tests have the correct size and good power properties.
A Dual Domain Algorithm for Minimum Nuclear Norm Deblending

This paper illustrates an inversion scheme for separating simultaneous sources. The proposed algorithm assumes that an ideal 3D common receiver gather can be represented via a low rank matrix in the frequency-space domain. We propose a dual-space algorithm that minimizes the misfit between the observed blended data and predicted blended data in t− x− y subject to a nuclear norm constraint that is applied to the data in the ω − x− y domain. The algorithm is illustrated with a synthetic 3D vertical seismic profile (VSP) data set.
Changing structure of wet periods across southwest China during 1961-2012

Using homogenized daily precipitation observations during 1961 to 2012, changes in duration-based structure of precipitation over southwest China under drought conditions were investigated. The results reveal a distinct regrouping of wet periods of different length. Wet days and precipitation related to long wet periods (>3 d) decreased significantly. Such decreases resulted in significantly fewer fractional contributions of long wet periods to total wet days and annual precipitation totals. In contrast, contributions from short wet periods (<3 d) showed significant ascending trends. Substantial decreases in precipitation of long wet periods after 2000 accounted for most reductions in annual precipitation totals and resultant drought tendency. A more specific analysis on seasonal timescales highlights the significance of changes in autumn precipitation structure in causing the drying tendency across southwest China.
Quality of Life on the Agricultural Treadmill: Individual and Quality of Life on the Agricultural Treadmill: Individual and Community Determinants of Farm Family Well-Being Community Determinants of Farm Family Well-Being

Individual quality of life (QOL) is a critical foundation of stable and cohesive societies. This research examines QOL among Iowa farmers, who as a group have seen their numbers decline precipitously over the past decades as the farm economy has undergone major restructuring processes. Farm families are nested in rural communities, many of which have also experienced persistent population loss and economic decline over the same period. A multilevel modeling approach is employed to examine determinants of subjective QOL over time, using 29 years of longitudinal data. Results point to positive relationships between household income, community vitality, and farm family QOL. Individual stress and economic dependence on farming were negatively associated with QOL. The finding that community vitality is a critical determinant of farm family quality of life supports long-standing appeals to increase investment in community development efforts.
: complex large-scale visual insight for large hybrid IT infrastructure management. Future Generation

Interactive visual exploration techniques (IVET) such as those advocated by Shnei-derman and extreme scale visual analytics have successfully increased our understanding of a variety of domains that produce huge amounts of complex data. In spite of their complexity, IT infrastructures have not beneﬁted from the application of IVET techniques. Loom is inspired in IVET techniques and builds on them to tame increasing complexity in IT infrastructure management systems guaranteeing interactive response times and integrating key elements for IT management: Relationships be-tween managed entities coming from different IT management subsystems, alerts and actions (or reconﬁgurations) of the IT setup. The Loom system builds on two main pillars: 1) a multiplex graph spanning data from different ITIMs; and 2) a novel visualisation arrangement: the Loom “Thread” visualisation model. We have tested this in a number of real-world applications, showing that Loom can handle million of entities without losing information, with minimum context switching, and offering better performance than other relational/graph-based systems. This ensures interactive response times (few seconds as 90 th percentile). The value of the “Thread” visualisation model is shown in a qualitative analysis of users’ experiences with Loom.
RRI 2016 International Conference «Responsible Research and Innovation» METHODICAL TOOLKIT OF BUSINESS POTENTIAL EVALUATION OF AUTONOMOUS HIGHER EDUCATION INSTITUTION

The article proves that to increase the competitiveness of the autonomous HEI (Higher Education Institution), it is necessary to fundamentally change the conditions of their activity: to switch to the business model of development, which allows the continuous reforming of production, management, and technological solutions. The study's subject was the economic relations arising during the monitoring and evaluation of the autonomous HEI business potential. The scientific works of domestic and foreign scientists, the data of statistical reports and the results of the autonomous HEI business model testing became the theoretical and methodological basis of the work. The results of the calculations show that the actual value of the business potential integrated index is less than the planned value, i.e. there is a provision to increase this index, and therefore there are prospects for the growth of the business potential of the autonomous HEI. The recommendation presented in the article may be used to ensure the competitiveness of the HEIs in the long term and to improve their business potential.
University of Birmingham Cognitive capacity limits are remediated by practice-induced plasticity between the putamen and pre-supplementary motor area

Humans show striking limitations in information processing when multitasking yet can modify these limits with practice. Such limitations have been linked to a frontal-parietal network, but recent models of decision-making implicate a striatal-cortical network. We adjudicated these accounts by investigating the circuitry underpinning multitasking in 100 human individuals and the plasticity caused by practice. We observed that multitasking costs, and their practice-induced remediation, are best explained by modulations in information transfer be-tween the striatum and the cortical areas that represent stimulus-response mappings. Specifically, our results support the view that multitasking stems at least in part from taxation in information sharing between the putamen and pre-supplementary motor area (pre-SMA). Moreover, we propose that modulations to information transfer between these two regions leads to practice-induced improvements in multitasking.
Effects of Cultivation Methods on Yield and Utilization of Temperature and Light of Late Japonica Rice in Southern Double Cropping Rice Areas

【Objective】Under the conditions of late rice of southern double cropping systems,five different cultivar types of japonica were used to investigate the effects of cultivation methods on yield,growth period and utilization of temperature and light. 【Method】 In Shanggao of Jiangxi province,four types of experimental materials including late-maturing medium japonica rice,early-maturing late japonica rice,medium-maturing late japonica rice and late-maturing late japonica rice as well as three cultivation methods including hand transplanting(HT),cast transplanting(CT) and mechanical transplanting(MT) were used to comparatively study the effect of different cultivation methods on the yield,growth period and utilization of temperature and light. 【Result】 The yields of different types of late japonica rice all demonstrated that HTCTMT,and different significant or very significant levels were reached between the yields of different cultivation methods.Compared with the HT,the yields were reduced by 6.05%-25.70% and 9.94%-39.04% under CT and MT,respectively.These differences resulted from the total spikelet,seed setting rate and grain weight under HT were higher than under CT and MT.Compared with the HT,the growth periods of CT and MT were delayed,and their total growth periods were significantly shortened by 4.9 and 10.3 days.For different types of varieties,the shortened day of late japonica was more than that of medium japonica.For the same mature period types,the shortened day of japonica hybrid rice was more than that of japonica conventional rice.The utilization efficiency of accumulated temperature and light time of various types of varieties at different developmental stages showed that HTCTMT,and the whole growth period trended similarly.The utilization efficiency of temperature and light in the whole growth period under MT was only 85.66% and 80.75%. 【Conclusion】On the basis of the above results,a preliminary planning for the suitable range and its supporting suitable variety types of different cultivation methods was made.HT,CT and MT were all suitable in this area,but each cultivation method had its suitable variety types.