Miracle-dz/UniFAR-FTUs huggingface dataset:
Bihemispheric Motor Cortex Transcranial Direct Current Stimulation Improves Force Steadiness in Post-Stroke Hemiparetic Patients: A Randomized Crossover Controlled Trial

Post-stroke patients usually exhibit reduced peak muscular torque (PT) and/or force steadiness during submaximal exercise. Brain stimulation techniques have been proposed to improve neural plasticity and help to restore motor performance in post-stroke patients. The present study compared the effects of bihemispheric motor cortex transcranial direct current stimulation (tDCS) on PT and force steadiness during maximal and submaximal resistance exercise performed by post-stroke patients vs. healthy controls. A double-blind randomized crossover controlled trial (identification number: TCTR20151112001; URL: http://www.clinicaltrials.in.th/) was conducted involving nine healthy and 10 post-stroke hemiparetic individuals who received either tDCS (2 mA) or sham stimulus upon the motor cortex for 20 min. PT and force steadiness (reflected by the coefficient of variation (CV) of muscular torque) were assessed during unilateral knee extension and flexion at maximal and submaximal workloads (1 set of 3 repetitions at 100% PT and 2 sets of 10 repetitions at 50% PT, respectively). No significant change in PT was observed in post-stroke and healthy subjects. Force steadiness during knee extension (~25–35%, P < 0.001) and flexion (~22–33%, P < 0.001) improved after tDCS compared to the sham condition in post-stroke patients, but improved only during knee extension (~13–27%, P < 0.001) in healthy controls. These results suggest that tDCS may improve force steadiness, but not PT in post-stroke hemiparetic patients, which might be relevant in the context of motor rehabilitation programs.
SNNS application for crop classification using HyMap data

The goal of this paper is the presentation of a method and results for artificial neural networks crops classification based on HyMap hyperspectral data. The method that uses an ANNs does not only depend on statistical parameters of particular class and hence makes it possible to include texture information. To experiment with variable pattern size two data sets were chosen with 10 bands obtained after MNF and 5 hyperspectral vegetation indicies. Next to post classification crops maps, additional quality layers were generated to check which classes are “problematic” because of spectral similarity or errors in the training/reference data. The best accuracy was achieved using the 10 MNF bands with the 3×3 pixel sub pattern size −94,8 %.
Hazard Rate Scaling for the GI / M / n + GI Queue

We obtain a heavy-traffic limit for the GI/M/n+GI queue which includes the entire abandonment distribution. Our main approach is to scale the hazard rate function in an appropriate way such that our resulting diffusion approximation contains the entire hazard rate function. We then show through numerical studies that for various key performance measures, our approximations outperform those commonly used in practice which only involved the abandonment distribution through the value of its density at the origin. The robustness of our results is also demonstrated by applying them to solving constraint satisfaction problems arising in the context of telephone call centers.
The Effects of Case Mixing on Word Recognition: Evidence from a PET Study

The early stages of visual word recognition were investigated by scanning participants using PET as they took part in implicit and explicit reading tasks with visually disrupted stimuli. CaSe MiXiNg has been shown in behavioral studies to increase reaction times (RTs) in naming and other word recognition tasks. In this study, we found that during both an implicit (feature detection) task and an explicit word-naming task, mixed-case words compared to same-case words produced increased activation in an area of the right parietal cortex previously associated with visual attention. No effect of case was found in this area for pseudowords or consonant strings. Further, lowering the contrast of the stimuli slowed RTs as much as case mixing, but did not lead to the same increase in right parietal activation. No significant effect of case mixing was observed in left-hemisphere language areas. The results suggest that reading mixed-case words requires increased attentional processing. However, later word recognition processes may be relatively unaffected by the disruption in presentation.
The Role of Oncology Nurses in Discussing Clinical Trials.

PURPOSE/OBJECTIVES To describe oncology nurses' experiences discussing clinical trials with their patients, and to assess barriers to these discussions. RESEARCH APPROACH A qualitative study designed to elicit narratives from oncology nurses. SETTING Community- and academic-based oncology clinics throughout the United States. PARTICIPANTS 33 oncology nurses involved in direct patient care in community-based and large hospital-based settings. The sample was drawn from members of the Oncology Nursing Society. METHODOLOGIC APPROACH In-depth interviews were conducted and analyzed using a immersion/crystallization approach to identify themes and patterns. The analyses highlight specific issues, examples, and contexts that present challenges to clinical trial discussions with patients. FINDINGS Oncology nurses view their roles as patient educators and advocates to be inclusive of discussion of clinical trials. Barriers to such discussions include lack of knowledge and strategies for addressing patients' common misconceptions and uncertainty about the timing of discussions. INTERPRETATION These data indicate that enabling nurses to actively engage patients in discussions of clinical trials requires educational interventions to build self-efficacy and close knowledge gaps. IMPLICATIONS FOR NURSING Oncology nurses can play a critical role in advancing cancer care by supporting patients in decision making about clinical trial participation. This will require training and education to build their knowledge, reduce barriers, and increase their self-efficacy to fulfill this responsibility in various clinical settings.
Risk factors for COPD spirometrically defined from the lower limit of normal in the BOLD project

Chronic obstructive pulmonary disease (COPD) is predicted to become the third most common cause of death and disability worldwide by 2020. The prevalence of COPD defined by the lower limit of normal was estimated using high-quality spirometry in surveys of 14 populations aged ≥40 yrs. The strength and consistency of associations were assessed using random effects meta-analysis. Pack-years of smoking were associated with risk of COPD at each site. After adjusting for this effect, we still observed significant associations of COPD risk with age (OR 1.52 for a 10 yr age difference, 95% CI 1.35–1.71), body mass index in obese compared with normal weight (OR 0.50, 95% CI 0.37–0.67), level of education completed (OR 0.76, 95% CI 0.67–0.87), hospitalisation with a respiratory problem before age 10 yrs (OR 2.35, 95% CI 1.42–3.91), passive cigarette smoke exposure (OR 1.24, 95% CI 1.05–1.47), tuberculosis (OR 1.78, 95%CI 1.17–2.72) and a family history of COPD (OR 1.50, 95% CI 1.19–1.90). Although smoking is the most important risk factor for COPD, other risk factors are also important. More research is required to elucidate relevant risk factors in low- and middle-income countries where the greatest impact of COPD will occur.
Nonparametric Bayesian Models for Unsupervised Event Coreference Resolution

We present a sequence of unsupervised, nonparametric Bayesian models for clustering complex linguistic objects. In this approach, we consider a potentially infinite number of features and categorical outcomes. We evaluated these models for the task of within- and cross-document event coreference on two corpora. All the models we investigated show significant improvements when compared against an existing baseline for this task.
Tag data and personalized information retrieval

Researchers investigating personalization techniques for Web Information Retrieval face a challenge; that the data required to perform evaluations, namely query logs and click-through data, is not readily available due to valid privacy concerns. One option for researchers is to perform a user study, however, such experiments are often limited to small (and sometimes biased) samples of users, restricting somewhat the conclusions that can be drawn. Alternatively, researchers can look for publicly available data that can be used to approximate query logs and click-through data. Recently it has been shown that the information contained in social bookmarking (tagging) systems may be useful for improving Web search. We investigate the use of tag data for evaluating personalized retrieval systems involving thousands of users. Using data from the social bookmarking site del.icio. us, we demonstrate how one can rate the quality of personalized retrieval results. Furthermore, we conduct experiments involving various smoothing techniques and profile settings, which show that a user's "bookmark history" can be used to improve search results via personalization. Analogously to studies involving implicit feedback mechanisms in IR, which have found that profiles based on the content of clicked URLs outperform those based on past queries alone, we find that profiles based on the content of bookmarked URLs are generally superior to those based on tags alone.
Projection Neurons in Lamina III of the Rat Spinal Cord Are Selectively Innervated by Local Dynorphin-Containing Excitatory Neurons

Large projection neurons in lamina III of the rat spinal cord that express the neurokinin 1 receptor are densely innervated by peptidergic primary afferent nociceptors and more sparsely by low-threshold myelinated afferents. However, we know little about their input from other glutamatergic neurons. Here we show that these cells receive numerous contacts from nonprimary boutons that express the vesicular glutamate transporter 2 (VGLUT2), and form asymmetrical synapses on their dendrites and cell bodies. These synapses are significantly smaller than those formed by peptidergic afferents, but provide a substantial proportion of the glutamatergic synapses that the cells receive (over a third of those in laminae I–II and half of those in deeper laminae). Surprisingly, although the dynorphin precursor preprodynorphin (PPD) was only present in 4–7% of VGLUT2 boutons in laminae I–IV, it was found in 58% of the VGLUT2 boutons that contacted these cells. This indicates a highly selective targeting of the lamina III projection cells by glutamatergic neurons that express PPD, and these are likely to correspond to local neurons (interneurons and possibly projection cells). Since many PPD-expressing dorsal horn neurons respond to noxious stimulation, this suggests that the lamina III projection cells receive powerful monosynaptic and polysynaptic nociceptive input. Excitatory interneurons in the dorsal horn have been shown to possess IA currents, which limit their excitability and can underlie a form of activity-dependent intrinsic plasticity. It is therefore likely that polysynaptic inputs to the lamina III projection neurons are recruited during the development of chronic pain states.
Location and Language Use in Social Media Context-based Natural Language Processing for Gis-based Vague Region Visualization Extracting Socioeconomic Patterns from the News: Modelling Text and Outlet Importance Jointly Finding Eyewitness Tweets during Crises Inducing Information Structures for Data-

Louis Armstrong (is said to have) said, “I don’t need words — it’s all in the phrasing”. As someone who does natural-language processing for a living, I’m a big fan of words; but lately, my collaborators and I have been studying aspects of phrasing (in the linguistic, rather than musical sense) that go beyond just the selection of one particular word over another. I’ll describe some of these projects in this talk. The issues we’ll consider include: Does the way in which something is worded in and of itself have an effect on whether it is remembered or attracts attention, beyond its content or context? Can we characterize how different sides in a debate frame their arguments, in a way that goes beyond specific lexical choice (e.g., “pro-choice” vs. “pro-life”)? The settings we’ll explore range from movie quotes that achieve cultural prominence; to posts on Facebook, Wikipedia, Twitter, and the arXiv; to framing in public discourse on the inclusion of geneticallymodified organisms in food. Joint work with Lars Backstrom, Justin Cheng, Eunsol Choi, Cristian Danescu-Niculescu-Mizil, Jon Kleinberg, Bo Pang, Jennifer Spindel, and Chenhao Tan.
On isomorphisms of algebras of smooth functions

We show that for any smooth Hausdorff manifolds M and N, which are not necessarily second-countable, paracompact or connected, any isomorphism from the algebra of smooth (real or complex) functions on N to the algebra of smooth functions on M is given by composition with a unique diffeomorphism from M to N. An analogous result holds true for isomorphisms of algebras of smooth functions with compact support.
An Elementary Proof of Convex Phase Retrieval in the Natural Parameter Space via the Linear Program PhaseMax

The phase retrieval problem has garnered significant attention since the development of the PhaseLift algorithm, which is a convex program that operates in a lifted space of matrices. Because of the substantial computational cost due to lifting, many approaches to phase retrieval have been developed, including non-convex optimization algorithms which operate in the natural parameter space, such as Wirtinger Flow. Very recently, a convex formulation called PhaseMax has been discovered, and it has been proven to achieve phase retrieval via linear programming in the natural parameter space under optimal sample complexity. The current proofs of PhaseMax rely on statistical learning theory or geometric probability theory. Here, we present a short and elementary proof that PhaseMax exactly recovers real-valued vectors from random measurements under optimal sample complexity. Our proof only relies on standard probabilistic concentration and covering arguments, yielding a simpler and more direct proof than those that require statistical learning theory, geometric probability or the highly technical arguments for Wirtinger Flow-like approaches.
Identification and Classification for finger movement based on EEG

Identification and classification technology plays an important part in study of the BCI system. There are many algorithms to classify the event of different task related. Here, finger movement was used as the basic and typical tasks to be identified in the BCI experiments. The ideas of BP and ERD were introduced and discussed. The CSSD (common spatial subspace decomposition) algorithm was used for classifying single-trial EEG during the preparation of left-right finger movements after the two kinds of phenomena were expounded in detail in this paper. Experiment and simulating results show that the averaged classification accuracy can be up to the 75.6%
Pull-out or Push in? Impact on Students with Special Needs Social, Emotional, and Academic Success

Abstract[SMS1] Inclusion has become the standard practice in the classroom today but many students continue to be pulled out of the classroom for various services such as counseling, speech therapy, occupational therapy, physical therapy, and extra support. It is up to the teachers and the parents to decide where the least restrictive environment is for the students in order for them to be the most successful. This study was conducted to determine any effects on students’ social success in the classroom. The study involved five general education teachers, two special education teachers, four parents, and six fourth grade students who are on IEPs. The general education teachers teach various grade levels ranging from first grade through sixth and one music teacher. Each teacher and parent was privately interviewed and the children were given an online questionnaire. Upon completion of the study, it is determined that each student should be treated as an individual and recognize that some are more successful in the classroom with their peers, while others do better in smaller settings. An ideal situation would be smaller class sizes with a general and special education teacher co-teaching together.
Covariance Tracking via Geometric Particle Filtering

Region covariance descriptor recently proposed has been approved robust and elegant to describe a region of interest, which has been applied to visual tracking. We develop a geometric method for visual tracking, in which region covariance is used to model objects appearance; then tracking is led by implementing the particle filter with the constraint that the system state lies in a low dimensional manifold: affine Lie group. The sequential Bayesian updating consists of drawing state samples while moving on the manifold geodesics; the region covariance is updated using a novel approach in a Riemannian space. Our main contribution is developing a general particle filtering-based racking algorithm that explicitly take the geometry of affine Lie groups into consideration in deriving the state equation on Lie groups. Theoretic analysis and experimental evaluations demonstrate the promise and effectiveness of the proposed tracking method.
Non-gaussian statistics of pencil beam surveys

We study the effect of the non-Gaussian clustering of galaxies on the statistics of pencil beam surveys. We find that the higher order moments of the galaxy distribution play an important role in the probability distribution for the power spectrum peaks. Taking into account the observed values for the kurtosis of galaxy distribution we derive the general probability distribution for the power spectrum modes in non-Gaussian models and show that the probability to obtain the 128$\hm$ periodicity found in pencil beam surveys is raised by roughly one order of magnitude. The non-Gaussianity of the galaxy distribution is however still insufficient to explain the reported peak-to-noise ratio of the periodicity, so that extra power on large scales seems required.
Statistical Eigenmode-Based SDMA for Two-User Downlink

This paper proposes a statistical-eigenmode space-division multiple-access (SE-SDMA) transmission approach for a two-user downlink system where two transmit antennas are deployed at the base station and each mobile user has only one receive antenna, assuming that only statistical channel state information (SCSI) is available at the transmitter. By maximizing a lower bound of the ergodic signal-to-leakage-and-noise ratio (SLNR), the proposed SE-SDMA approach selects two users with orthogonal principal statistical eigen-directions and then transmits to each user along the corresponding eigenmode. For this approach, we derive an exact expression of the achievable ergodic rate, and compare it with the zero-forcing beamforming (ZFBF) system exploiting instantaneous CSI. We show that SE-SDMA can achieve the maximum ergodic sum-rate of the two selected users, and provide significant user selection gains. For comparison with ZFBF, we show that the rate gap between SE-SDMA and ZFBF with perfect CSI at the transmitter (CSIT) tends to zero in highly correlated channels. Further, when ZFBF is implemented with inaccurate CSIT, the operating regions of SE-SDMA and ZFBF are investigated for different ranges of signal-to-noise ratio (SNR), instantaneous CSIT accuracy levels, and spatial correlation levels. Numerical results confirm the analytically derived conclusions, and physical insights are provided.
Medication understanding, non‐adherence, and clinical outcomes among adult kidney transplant recipients

We sought to evaluate the prevalence of medication understanding and non‐adherence of entire drug regimens among kidney transplantation (KT) recipients and to examine associations of these exposures with clinical outcomes. Structured, in‐person interviews were conducted with 99 adult KT recipients between 2011 and 2012 at two transplant centers in Chicago, IL; and Atlanta, GA. Nearly, one‐quarter (24%) of participants had limited literacy as measured by the Rapid Estimate of Adult Literacy in Medicine test; patients took a mean of 10 (SD=4) medications and 32% had a medication change within the last month. On average, patients knew what 91% of their medications were for (self‐report) and demonstrated proper dosing (via observed demonstration) for 83% of medications. Overall, 35% were non‐adherent based on either self‐report or tacrolimus level. In multivariable analyses, fewer months since transplant and limited literacy were associated with non‐adherence (all P<.05). Patients with minority race, a higher number of medications, and mild cognitive impairment had significantly lower treatment knowledge scores. Non‐white race and lower income were associated with higher rates of hospitalization within a year following the interview. The identification of factors that predispose KT recipients to medication misunderstanding, non‐adherence, and hospitalization could help target appropriate self‐care interventions.
Formal verification of timed properties of randomized distributed algorithms

In [11] a method for the analysis of the expected time complexity of a randomized distributed algorithm is presented. The method consists of proving auxiliary probabilistic time bound statements of the form U ~ U’, which mean that whenever the algorithm begins in ‘a state in set U, it will reach a state in set U’ within time t with probability at least p . However, [11] does not provide a formal methodology to prove the validity of a specific probabilistic time bound statement from scratch: each statement is proved by means of ad hoc operational arguments. Unfortunately, operational reasoning is generally error-prone and difficult to check. In this paper we overcome the problem by developing a new technique to prove probabilistic time bound statements, which consists of reducing the analysis of a time bound statement to the analysis of a statement that does not involve probability. As a consequence, several existing techniques for non-randomized algorithms can be applied, and correctness proofs can be verified mechanically.
Automated Planning for Intelligent Tutoring Systems

In this paper, we build on the latest in automated planning techniques to develop a generalized framework for courseindependent design of Intelligent Tutoring Systems (ITSs). This is meant to provide targeted and personalized assistance to students, in order to meet the demands of the increasing class size, as well as help instructors who can use higher level specifications to design courses without having to worry about building the course-specific tutoring assistance. Thus the aim of this paper is to demonstrate what automated planning can bring to the table for the design of courseindependent ITS features. We will illustrate these capabilities in Dragoon, an ITS deployed at Arizona State University.