Miracle-dz/UniFAR-FTUs huggingface dataset:
Goniometric reliability in a clinical setting. Elbow and knee measurements.

Reliability of goniometric measurements has been examined only under standardized conditions and usually with healthy subjects. The purpose of this study was to assess goniometric reliability in a clinical setting. The reliability of goniometric measurements of passive elbow and knee positions was assessed using patients as subjects. The effect of using the means of repeated measurements and the interdevice reliability of three common goniometers were also examined. Results showed that intratester reliability for flexion and extension of the knee and the elbow joints was high (r = .91 to .99). Intertester reliability was also high (r = .88 to .97) for these measurements except for measurements of knee extension (r = .63 to .70). Although previous investigators have suggested that using the means of multiple measurements improves reliability, our data indicate that this procedure never improves the correlation coefficient more than . 12. The reliability was similar for all three devices. The results of this study indicate that for the knee and elbow joints, goniometric measurements performed in a clinical setting can be highly reliable. The method described in this study provides a simple protocol that can be used clinically to investigate goniometric reliability.
Examining the Impact of Socioeconomic Status and Socioecologic Stress on Physical and Mental Health Quality of Life Among Breast Cancer Survivors

Purpose/Objectives: To examine how physical and mental health quality of life (QOL) varies in relation to the socioeconomic status and ethnicity among breast cancer survivors; to determine key socioecologic factors influencing outcomes. Design: Cross-sectional. Setting: Participants were recruited from the California Cancer Surveillance Program, from hospital registries
High-Performance Metal/High-$k$ n- and p-MOSFETs With Top-Cut Dual Stress Liners Using Gate-Last Damascene Process on (100) Substrates

Newly proposed mobility-booster technologies are demonstrated for metal/high-k gate-stack n- and pMOSFETs. The process combination of top-cut SiN dual stress liners and damascene gates remarkably enhances local channel stress particularly for shorter gate lengths in comparison with a conventional gate-first process. Dummy gate removal in the damascene gate process induces high channel stress, because of the elimination of reaction force from the dummy gate. PFETs with top-cut compressive stress liners and embedded SiGe source/drains are performed by using atomic layer deposition TiN/HfO2 gate stacks with Tinv=1.4 nm on (100) substrates. On the other hand, nFETs with top-cut tensile stress liners are obtained by using HfSix/HfO2 gate stacks with Tinv=1.4 nm. High-performance n- and pFETs are achieved with Ion=1300 and 1000 muA/mum at Ioff =100 nA/mum, Vdd=1.0 V, and a gate length of 40 nm, respectively.
Accelerated Network Coding with Dynamic Stream Decomposition on Graphics Processing Unit

Network coding, a well-known technique for optimizing data-flow in wired and wireless network systems, has attracted considerable attention in various fields. However, the decoding complexity in network coding becomes a major performance bottleneck in the practical network systems; thus, several researches have been conducted for improving the decoding performance in network coding. Nevertheless, previously proposed parallel network coding algorithms have shown limited scalability and performance imbalance for different-sized transfer units and multiple streams. In this paper, we propose a new parallel decoding algorithm for network coding using a graphics processing unit (GPU). This algorithm can simultaneously process multiple incoming streams and can maintain its maximum decoding performance irrespective of the size and number of transfer units. Our experimental results show that the proposed algorithm exhibits a 682.2 Mbps decoding bandwidth on a system with GeForce GTX 285 GPU and speed-ups of up to 26 as compared to the existing single stream decoding procedure with a 128 × 128 coefficient matrix and different-sized data blocks.
Using deep learning to detect radio frequency interference in pulsar observations

Pulsars are highly-magnetized, rapidly-rotating neutron stars producing beams of radio emission that are swept across the sky as pulsars about their axis, in a similar way as lighthouses. Radio observations of pulsars are unfortunately subjected to numerous types of parasite signals, the so-called “Radio Frequency Interferences” (RFIs). RFIs result from human activity and need to be excised in order to be able to study pulsars with the cleanest possible data. The RFI excision method we present here detects RFIs in dynamic spectograms of the radio pulsar observations, using a deep neural network. Dynamic spectrograms are treated as images by the network, which in turn uses image segmentation to classify the points in the time-frequency space as RFI or non-RFI data. Preliminary results indicate that the network can detect most RFI types while being less “destructive”, i.e., it discards less actual pulsar signal than common RFI excision methods based on statistical thresh-olding.
Engagement in dance is associated with emotional competence in interplay with others

This study has explored the relation between dance achievement and alexithymia in a larger Swedish population sample (Swedish Twin Registry) with a study sample of 5431 individuals. Dance achievement (CAQ) was assessed in relation to Alexithymia (Toronto Alexithymia Scale, TAS-20) including the three subscales: Difficulty Identifying Feelings (DIF), Difficulty Describing Feelings (DDF), and Externally Oriented Thinking (EOT). The results show a significant negative association between the TAS subscale (EOT) and creative achievement in dance. A high EOT score corresponds to poor ability to communicate feelings to the environment. There was no consistent association between the other factors DIF and DDF and dance achievement. Dance activity and training seem to be involved in the body’s emotional interplay with others. Embodied cognition, emotional perception, and action are discussed as factors relevant to measuring the skill of a dancer.
Threshold Structure-Preserving Signatures: Strong and Adaptive Security under Standard Assumptions

Structure-preserving signatures (SPS) have emerged as an important cryptographic building block, as their compatibility with the Groth-Sahai (GS) NIZK framework allows to construct protocols under standard assumptions with reasonable efficiency. Over the last years there has been a significant interest in the design of threshold signature schemes. However, only very recently Crites et al. (ASIACRYPT 2023) have introduced threshold SPS (TSPS) along with a fully non-interactive construction. While this is an important step, their work comes with several limitations. With respect to the construction, they require the use of random oracles, interactive complexity assumptions and are restricted to so called indexed Diffie-Hellman message spaces. Latter limits the use of their construction as a drop-in replacement for SPS. When it comes to security, they only support static corruptions and do not allow partial signature queries for the forgery. In this paper, we ask whether it is possible to construct TSPS without such restrictions. We start from an SPS from Kiltz, Pan and Wee (CRYPTO 2015) which has an interesting structure, but thresholdizing it requires some modifications. Interestingly, we can prove it secure in the strongest model (TS-UF-1) for fully non-interactive threshold signatures (Bellare et al., CRYPTO 2022) and even under fully adaptive corruptions. Surprisingly, we can show the latter under a standard assumption without requiring any idealized model. All known constructions of efficient threshold signatures in the discrete logarithm setting require interactive assumptions and idealized models. Concretely, our scheme in type III bilinear groups under the SXDH assumption has signatures consisting of 7 group elements. Compared to the TSPS from Crites et al. (2 group elements), this comes at the cost of efficiency. However, our scheme is secure under standard assumptions, achieves strong and adaptive security guarantees and supports general message spaces, i
The Data Stream Space Complexity of Cascaded Norms

We consider the problem of estimating cascaded aggregates over a matrix presented as a sequence of updates in a data stream. A cascaded aggregate P ±Q is defined by evaluating aggregate Q repeatedly over each row of the matrix, and then evaluating aggregate P over the resulting vector of values. This problem was introduced by Cormode andMuthukrishnan, PODS, 2005 [CM]. We analyze the space complexity of estimating cascaded norms on an n £d matrix to within a small relative error. Let Lp denote the p-th norm, where p is a non-negative integer. We abbreviate the cascaded normLk ±Lp by Lk,p . (1) For any constant k ¸ p ¸ 2, we obtain a 1-pass e O(n1¡2/kd1¡2/p )-space algorithm for estimating Lk,p . This is optimal up to polylogarithmic factors and resolves an open question of [CM] regarding the space complexity of L4,2. We also obtain 1-pass space-optimal algorithms for estimating L1,k and Lk,1. (2)We prove a space lower bound of­(n1¡1/k ) on estimating Lk,0 and Lk,1, resolving an open question due to Indyk, IITK Data StreamsWorkshop (Problem 8), 2006. We also resolve two more questions of [CM] concerning Lk,2 estimation and block heavy hitter problems. Ganguly, Bansal and Dube (FAW, 2008) claimed an e O(1)-space algorithm for estimating Lk,p for any k,p 2 [0,2]. Our lower bounds show this claimis incorrect.
Scene Parsing With Integration of Parametric and Non-Parametric Models

We adopt convolutional neural networks (CNNs) to be our parametric model to learn discriminative features and classifiers for local patch classification. Based on the occurrence frequency distribution of classes, an ensemble of CNNs (CNN-Ensemble) are learned, in which each CNN component focuses on learning different and complementary visual patterns. The local beliefs of pixels are output by CNN-Ensemble. Considering that visually similar pixels are indistinguishable under local context, we leverage the global scene semantics to alleviate the local ambiguity. The global scene constraint is mathematically achieved by adding a global energy term to the labeling energy function, and it is practically estimated in a non-parametric framework. A large margin-based CNN metric learning method is also proposed for better global belief estimation. In the end, the integration of local and global beliefs gives rise to the class likelihood of pixels, based on which maximum marginal inference is performed to generate the label prediction maps. Even without any post-processing, we achieve the state-of-the-art results on the challenging SiftFlow and Barcelona benchmarks.
Journal of Business Venturing Insights

Empirical understanding of why individuals become former entrepreneurs is not well-established. This investigation draws upon Identity Theory and Role Exit Theory to explore patterns in Entrepreneurship in the Population (EPOP) Survey Project dataset. The key finding is a theory-consistent tension between commitment and obstacles encountered in predicting exit from the entrepreneur role. The likelihood of being a former entrepreneur is decreased by surrogate indicators of commitment such as having a business as primary personal income source. The likelihood is increased by proximal obstacles such as low levels of familial support. These patterns persist after statistical control for a wide swath of demographic and business characteristics. Based on these observed patterns, avenues for future research and implications for entrepreneurs, educators, and policymakers are considered.
Gene expression profiling reveals early cellular responses to intracellular magnetic labeling with superparamagnetic iron oxide nanoparticles

With MRI (stem) cell tracking having entered the clinic, studies on the cellular genomic response toward labeling are warranted. Gene expression profiling was applied to C17.2 neural stem cells following superparamagnetic iron oxide/PLL (poly‐L‐lysine) labeling over the course of 1 week. Relative to unlabeled cells, less than 1% of genes (49 total) exhibited greater than 2‐fold difference in expression in response to superparamagnetic iron oxide/PLL labeling. In particular, transferrin receptor 1 (Tfrc) and heme oxygenase 1 (Hmox1) expression was downregulated early, whereas genes involved in lysosomal function (Sulf1) and detoxification (Clu, Cp, Gstm2, Mgst1) were upregulated at later time points. Relative to cells treated with PLL only, cells labeled with superparamagnetic iron oxide/PLL complexes exhibited differential expression of 1399 genes. Though these differentially expressed genes exhibited altered expression over time, the overall extent was limited. Gene ontology analysis of differentially expressed genes showed that genes encoding zinc‐binding proteins are enriched after superparamagnetic iron oxide/PLL labeling relative to PLL only treatment, whereas members of the apoptosis/programmed cell death pathway did not display increased expression. Overexpression of the differentially expressed genes Rnf138 and Abcc4 were confirmed by quantitative real‐time polymerase chain reaction. These results demonstrate that, although early reactions responsible for iron homeostasis are induced, overall neural stem cell gene expression remains largely unaltered following superparamagnetic iron oxide/PLL labeling. Magn Reson Med 63:1031–1043, 2010. © 2010 Wiley‐Liss, Inc.
Estimating uncaught exceptions in Standard ML programs from type-based equations

We present a static analysis that detects potential runtime exceptions that are raised and never handled inside Standard ML (SML) programs. Contrary to our earlier method (Yi, 1994) based on abstract interpretation, where the input program's control flow is simultaneously computed while our exception analysis progresses, we separate the two phases in a manner similar to conventional data flow analysis. Before the exception analysis begins, we first estimate the input program's control flow from the type information from SML/NJ compiler. Based on this call-graph structure, exception flow is specified as a set of equations, whose solution is computed using an iterative least fixpoint method. A prototype of this analysis is applied to two realistic SML programs (ML-LEX and OR-SML core) and is 3 or 40 times faster than the earlier method and saves memory by 35 or 65 percent.
Spatial Assessment of Water Use in an Environmentally Sensitive Wetland

Abstract Wetlands are among the most valuable ecosystems in the world and are crucial in supporting biodiversity. They also provide space for storing surface waters, where intense biological processing occurs that helps improve water quality. Human activities, particularly irrigated agriculture and urban developments instigating water diversions from rivers, have altered the hydrology of most wetlands. The Lower Murrumbidgee wetland, located in the Murrumbidgee River Catchment, is an example and is one of the significant wetlands across the Murray Darling Basin of Australia. Historic estimates show the volumes of water ranged from none in dry years to about 300 to 400 GL (1 GL = 109 L) in an average and wet year, respectively. The flows reaching the Lower Murrumbidgee wetland have been drastically reduced by at least 60% because of the upstream diversions introduced during the last century. These reductions have adversely affected the health of natural vegetation and agricultural crops in the Lower Murrumbidgee floodplain. This article presents the results of the quantification of total water consumption of various land uses in the Lower Murrumbidgee floodplain using the remote sensing–based Surface Energy Balance Algorithm for Land (SEBAL) modeling approach. The spatial analysis of actual evapotranspiration (ETa) shows that ETa rates are the highest (13–26%) for the red gum (Eucalyptus camaldulensis) forests both in summer and winter days. However, in terms of total ETa volume, lignum (Muehlenbeckia florulenta) constitutes the most significant part, which is around 14–30% of the total ETa volume for the area. Actual evapotranspiration from winter cereal cropping areas following the summer ponding is the third highest consumer of water after the river red gum and lignum. Actual evapotranspiration from the fallow land is also significant, representing 5–28% of total ETa from the region. In view of the extent of the unaccounted flows in the overall water balance of the system, there is a need to upgrade measuring and reporting infrastructure by strengthening the institutional and management arrangements to better gauge the efficiency of environmental and consumptive water use. The state-of-the-art technology of remote sensing–based SEBAL modeling proved to have potential for measuring actual water use with reliable accuracy and can be used for assessing the environmental and productive use of water from wetlands in other regions of Australia.
A Language Modeling Approach to Identifying Code-Switched Sentences and Words

Globalization and multilingualism contribute to code-switching – the phenomenon in which speakers produce utterances containing words or expressions from a second language. Processing code-switched sentences is a significant challenge for multilingual intelligent systems. This study proposes a language modeling approach to the problem of codeswitching language processing, dividing the problem into two subtasks: the detection of code-switched sentences and the identification of code-switched words in sentences. A codeswitched sentence is detected on the basis of whether it contains words or phrases from another language. Once the code-switched sentences are identified, the positions of the code-switched words in the sentences are then identified. Experimental results on MandarinTaiwanese code-switching sentences show that the language modeling approach achieved a 79.52% F-measure and an accuracy of 80.23% for detecting code-switched sentences, and a 51.20% F-measure for the identification of code-switched words.
Proton MR Spectroscopy Study Amyotrophic Lateral Sclerosis: A High-Field Mesial Prefrontal Cortex Degeneration in

BACKGROUND AND PURPOSE: Frontotemporal lobar degeneration is responsible for the cognitive abnormalities seen in patients with ALS. We sought to evaluate the in vivo neurochemical changes associated with this pathology indicative of neuronal loss and gliosis. MATERIALS AND METHODS: Twenty-four patients with ALS (2 with ALS-FTD) and 15 healthy controls were studied. High-field proton MR spectroscopy of the mesial prefrontal cortex was used to determine concentrations of NAA and mIns, markers of neuronal integrity and gliosis, respectively. Metabolite concentrations were correlated with cognitive tests (verbal fluency, ACE). RESULTS: NAA/mIns was decreased 17% ( P (cid:1) . 002). Abnormalities were present to a lesser degree in the individual metabolites NAA (decreased 9%; P (cid:1) . 08) and mIns (increased 11%; P (cid:1) . 06) than the ratio of the 2 metabolites. These measures did not correlate significantly with verbal fluency or the ACE. CONCLUSIONS: Prefrontal lobe degeneration exists in patients with ALS as indicated by an abnormal mesial prefrontal cortex neurochemical profile. Further study is necessary to determine the potential utility of the NAA/mIns ratio as a biomarker for frontal lobe degeneration in ALS. ABBREVIATIONS: ACE (cid:1) Addenbrooke’s Cognitive Examination; ALS (cid:1) amyotrophic lateral sclerosis; ALSFRS-R (cid:1) ALS Functional Rating Scale-Revised; FTD (cid:1) frontotemporal dementia; FTLD (cid:1) frontotemporal lobar degeneration; MRS (cid:1) MR spectroscopy; NAA (cid:1) N -acetylaspartate; mIns (cid:1) myo -inositol; PET (cid:1)
Brain on an Open Scanner at 0.2 T Ultrafast Diffusion-Sensitive MR Imaging of

Summary: We describe new strategies for fast diffusion-sensitive MR imaging of ischemic brain or spinal cord lesions. The methods provide diagnostic image quality in less than 1 s per section and are used in conjunction with low-ﬁeld-strength open MR scanners. Single-shot sequences combine diffusion-sensitive preparation with a modiﬁed fast spin-echo data acquisition. Results are presented from healthy volunteers and from two patients with recent and older ischemic brain lesions.
In-Memory Dictionary-Based Indexing of oted RDF Triples

e upcoming RDF 1.2 recommendation is scheduled to introduce the concept of quoted triples, which allows statements to be made about other statements. Since quoted triples en‐ able new forms of data access in SPARQL 1.2, in the form of quoted triple paerns, there is a need for new indexing strategies that can eﬃciently handle these data access paerns. As such, we explore and evaluate diﬀerent in-memory indexing approaches for quoted triples. In this paper, we investigate four indexing approaches, and evaluate their performance over an artiﬁcial dataset with custom triple paern queries. Our ﬁndings show that the so-called in‐ dexed quoted triples dictionary vastly outperforms other approaches in terms of query execu‐ tion time at the cost of increased storage size and ingestion time. Our work shows that index‐ ing quoted triples in a dictionary separate from non-quoted RDF terms achieves good perfor‐ mance, and can be implemented using well-known indexing techniques into existing systems. erefore, we illustrate that the addition of quoted triples into the RDF stack can be achieved in a performant manner.
ON THE DISTRIBUTION OF αp 2 + β MODULO ONE FOR PRIMES p SUCH THAT p + 2 HAS NO MORE TWO PRIME

A classical problem in analytic number theory is to study the distribution of fractional part αp k + β , k ≥ 1 modulo 1, where α is irrational and p runs over the set of primes. We consider the subsequence generated by the primes p such that p + 2 is an almost-prime (the existence of inﬁnitely many such p is another topical result in prime number theory) and prove that its distribution has a similar property.
as an intervention for

Purpose: Eating disorders remain a major health concern and their incidence has further increased since the Covid-19 pandemic. Given the equally increasing demands on treatments and service provision, and high levels of relapse post-treatment, it is important that research explores novel and innovative interventions that can further support recovery for individuals with eating disorders. There is growing evidence that arts interventions are beneficial for recovery from eating disorders. This study evaluates the feasibility of conducting a stand-up comedy course to support eating disorder recovery. Design: The study used a qualitative interview study design to evaluate the recovery benefits of participating in stand-up comedy workshops for a pilot group of people in recovery from eating disorders (n=10). Findings: The comedy intervention was well-attended and had high acceptability and feasibility. For most individuals, participating in the course had a positive impact, including promoting personal recovery outcomes across all five elements of the CHIME framework. Unique assets of the course included providing participants with an opportunity to distance themselves from everyday worries of living with an eating disorder; the opportunity to cognitively reframe situations by making them the object of humour; and providing a safe space to (re-)build a positive sense of self. Originality: This is the first study evaluating stand-up comedy workshops for eating disorder recovery and further demonstrates the potential of arts interventions and the relevance of personal recovery frameworks in this field.