Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Smoothed Loss and Penalty Function for Noncrossing Composite Quantile Estimation via Deep Neural Networks

Uncertainty analysis in the form of probabilistic forecasting can significantly improve decision making processes in the smart power grid when integrating renewable energy sources such as wind. Whereas point forecasting provides a single expected value, probabilistic forecasts provide more information in the form of quantiles, prediction intervals, or full predictive densities. Traditionally quantile regression is applied for such forecasting and recently quantile regression neural networks have become popular for weather and renewable energy forecasting. However, one major shortcoming of composite quantile estimation in neural networks is the quantile crossover problem. This paper analyzes the effectiveness of a novel smoothed loss and penalty function for neural network architectures to prevent the quantile crossover problem. Its efficacy is examined on the wind power forecasting problem. A numerical case study is conducted using publicly available wind data from the Global Energy Forecasting Competition 2014. Multiple quantiles are estimated to form 10\%, to 90\% prediction intervals which are evaluated using a quantile score and reliability measures. Benchmark models such as the persistence and climatology distributions, multiple quantile regression, and support vector quantile regression are used for comparison where results demonstrate the proposed approach leads to improved performance while preventing the problem of overlapping quantile estimates.
Balancing Domain Gap for Object Instance Detection

Object instance detection in cluttered indoor environment is a core functionality for service robots. We can readily build a detection system by following recent successful strategy of deep convolutional neural networks, if we have a large annotated dataset. However, it is hard to prepare such a huge dataset in instance detection problem where only small number of samples are available. This is one of main impediment to deploying an object detection system. To overcome this obstacle, many approaches to generate synthetic dataset have been proposed. These approaches confront the domain gap or reality gap problem stems from discrepancy between source domain (synthetic training dataset) and target domain (real test dataset). In this paper, we propose a simple approach to generate a synthetic dataset with minimum human effort. Especially, we identify that domain gaps of foreground and background are unbalanced and propose methods to balance these gaps. In the experiment, we verify that our methods help domain gaps to balance and improve the accuracy of object instance detection in cluttered indoor environment.
Revisit Knowledge Distillation: a Teacher-free Framework

Knowledge Distillation (KD) aims to distill the knowledge of a cumbersome teacher model into a lightweight student model. Its success is generally attributed to the privileged information on similarities among categories provided by the teacher model, and in this sense, only strong teacher models are deployed to teach weaker students in practice. In this work, we challenge this common belief by following experimental observations: 1) beyond the acknowledgment that the teacher can improve the student, the student can also enhance the teacher significantly by reversing the KD procedure; 2) a poorly-trained teacher with much lower accuracy than the student can still improve the latter significantly. To explain these observations, we provide a theoretical analysis of the relationships between KD and label smoothing regularization. We prove that 1) KD is a type of learned label smoothing regularization and 2) label smoothing regularization provides a virtual teacher model for KD. From these results, we argue that the success of KD is not fully due to the similarity information between categories, but also to the regularization of soft targets, which is equally or even more important. Based on these analyses, we further propose a novel Teacher-free Knowledge Distillation (Tf-KD) framework, where a student model learns from itself or manually-designed regularization distribution. The Tf-KD achieves comparable performance with normal KD from a superior teacher, which is well applied when teacher model is unavailable. Meanwhile, Tf-KD is generic and can be directly deployed for training deep neural networks. Without any extra computation cost, Tf-KD achieves up to 0.65\% improvement on ImageNet over well-established baseline models, which is superior to label smoothing regularization. The codes are in: \url{this https URL}
Multiwavelength consensus of large-scale linear bias

We model the large-scale linear galaxy bias bg(x, z) as a function of redshift z and observed absolute magnitude threshold x for broad-band continuum emission from the far-infrared to ultraviolet, as well as for prominent emission lines, such as the H α, H β, Ly a, and [O ii] lines. The modelling relies on the semi-analytic galaxy formation model galform, run on the state-of-the-art N-body simulation surfs with the Planck 2015 cosmology. We find that both the differential bias at observed absolute magnitude x and the cumulative bias for magnitudes brighter than x can be fitted with a five-parameter model: bg(x, z) = a + b(1 + z)e(1 + exp [(x − c)d]). We also find that the bias for the continuum bands follows a very similar form regardless of wavelength due to the mixing of star-forming and quiescent galaxies in a magnitude-limited survey. Differences in bias only become apparent when an additional colour separation is included, which suggest extensions to this work could look at different colours at fixed magnitude limits. We test our fitting formula against observations, finding reasonable agreement with some measurements within 1σ statistical uncertainties, and highlighting areas of improvement. We provide the fitting parameters for various continuum bands, emission lines, and intrinsic galaxy properties, enabling a quick estimation of the linear bias in any typical survey of large-scale structure.
Identify More, Observe Less: Mediation Analysis Synthetic Control

The Synthetic Control Method (SCM) allows estimating the causal effect of an intervention in settings where panel data on just few treated, and control units are available. In this paper, we show that the existing SCM as well as its extensions can be easily modified to estimate how much of the "total" effect goes through observed causal channels. The additional assumptions needed are arguably very mild in many settings. Furthermore, in an illustrative empirical application we estimate the effects of adopting the euro on labor productivity in several countries and show that a reduction in the Economic Complexity Index helped mitigating the negative short run effects of adopting the new currency in some countries or boosted the positive effects in others.
A theory of condition for unconstrained perturbations

Traditionally, the theory of condition numbers assumes errors in the data that are constrained to be tangent to the input space. We extend this theory to unconstrained perturbations in the case when the input space is an embedded submanifold of an ambient Euclidean space. We will show that the way how the input space is curved inside the ambient space affects the condition number. Furthermore, we exhibit a connection to the sensitivity analysis of Riemannian optimization problems. We validate our main results in two prototypical applications by numerical experiments. The applications we consider are recovery of low-rank matrices in compressed sensing and $n$-camera triangulation in computer vision.
Cohomology of Lie Groupoid Modules and the Generalized van Est Map

The van Est map is a map from Lie groupoid cohomology (with respect to a sheaf taking values in a representation) to Lie algebroid cohomology. We generalize the van Est map to allow for more general sheaves, namely to sheaves of sections taking values in a (smooth or holomorphic) $G$-module, where $G$-modules are structures, which differentiate to representations. Many geometric structures involving Lie groupoids and stacks are classified by the cohomology of sheaves taking values in $G$-modules and not in representations, including $S^1$-groupoid extensions and equivariant gerbes. Examples of such sheaves are $\mathcal{O}^*$ and $\mathcal{O}^*(*D)\,,$ where the latter is the sheaf of invertible meromorphic functions with poles along a divisor $D\,. $ We show that there is an infinitesimal description of $G$-modules and a corresponding Lie algebroid cohomology. We then define a generalized van Est map relating these Lie groupoid and Lie algebroid cohomologies and study its kernel and image. Applications include the integration of several infinitesimal geometric structures, including Lie algebroid extensions, Lie algebroid actions on gerbes, and certain Lie $\infty $-algebroids.
Randomized Iterative Methods for Linear Systems: Momentum, Inexactness and Gossip

In the era of big data, one of the key challenges is the development of novel optimization algorithms that can accommodate vast amounts of data while at the same time satisfying constraints and limitations of the problem under study. The need to solve optimization problems is ubiquitous in essentially all quantitative areas of human endeavor, including industry and science. In the last decade there has been a surge in the demand from practitioners, in fields such as machine learning, computer vision, artificial intelligence, signal processing and data science, for new methods able to cope with these new large scale problems. In this thesis we are focusing on the design, complexity analysis and efficient implementations of such algorithms. In particular, we are interested in the development of randomized iterative methods for solving large scale linear systems, stochastic quadratic optimization problems, the best approximation problem and quadratic optimization problems. A large part of the thesis is also devoted to the development of efficient methods for obtaining average consensus on large scale networks.
A Study on the Relations Between Physical Exercise and Quality of Life in Middle-aged Adults

Purpose: This study was done to identify the relationship between physical exercise and quality of life in middle-aged adults. Method: The subjects of this study consisted of 251 mid-aged adults who lived in D city and N city. The instrument for this study was exercise performance scale by Kim and QOL scale by Ro. Data were collected form February 20 to March 20. 2003. Result: 1. Among participants. 'good' exercise performance consisted and exercise involvement was . 2. The quality of life showed positive significant correlation with the exercise performance(r=.21. p=.00). 3. Exercise performance was a predictor of quality of life and of the variance explained with exercise performance. Conclusion: The results of this study suggest that further development and applying of exercise performance programs are recommended for quality of life of mid-aged adults. In addition. further research needs to be done to identify relationship with these factors for various age group.
B-Spline CNNs on Lie Groups

Group convolutional neural networks (G-CNNs) can be used to improve classical CNNs by equipping them with the geometric structure of groups. Central in the success of G-CNNs is the lifting of feature maps to higher dimensional disentangled representations, in which data characteristics are effectively learned, geometric data-augmentations are made obsolete, and predictable behavior under geometric transformations (equivariance) is guaranteed via group theory. Currently, however, the practical implementations of G-CNNs are limited to either discrete groups (that leave the grid intact) or continuous compact groups such as rotations (that enable the use of Fourier theory). In this paper we lift these limitations and propose a modular framework for the design and implementation of G-CNNs for arbitrary Lie groups. In our approach the differential structure of Lie groups is used to expand convolution kernels in a generic basis of B-splines that is defined on the Lie algebra. This leads to a flexible framework that enables localized, atrous, and deformable convolutions in G-CNNs by means of respectively localized, sparse and non-uniform B-spline expansions. The impact and potential of our approach is studied on two benchmark datasets: cancer detection in histopathology slides in which rotation equivariance plays a key role and facial landmark localization in which scale equivariance is important. In both cases, G-CNN architectures outperform their classical 2D counterparts and the added value of atrous and localized group convolutions is studied in detail.
Professional Learning Communities: Where Does Special Education Fit in?

This project will consist of an in-depth review of Professional Learning Communities (PLCs) and their increasing role in districts and schools across the country. This project will document successes of PLCs and those components of a PLC that are most essential to the success of beginning and maintaining one. It will also look at the barriers that arise in attempting to implement a PLC in a district and/or school, and how best to overcome those barriers. Finally, this project will focus on the implications for special education when formulating and implementing a PLC in a school. Methodology will consist of library and/or archival research, along with attendance at a PLC conference and interviews with those administrators attempting to start a PLC in their respective schools. The final form of this completed thesis will be a lengthy research paper, including the implications for special education in the implementation and administration of PLCs. Degree Type Open Access Senior Honors Thesis Department Special Education First Advisor Jacquelyn S. McGinnis, Ph.D., Supervising Instructor Second Advisor Lidia Lee, Ph.D., Honors Advisor
Body patterns in patients with psychosomatic, musculoskeletal and schizophrenic disorders: Psychometric properties and clinical relevance of resource oriented body examination (ROBE-II)

The purpose of this paper was to study the psychometric properties and clinical relevance of the unipolarly scaled Resource Oriented Body Examination (ROBE I). Our aims were to find out if ROBE I could be reduced into a shorter version, labeled ROBE II. We also wanted to know if ROBE II would treat the variation of items in the 10 different subscales in a such a way that it also provided for discrimination between groups of patients with psychosomatic, musculoskeletal and schizophrenic disorders compared to a group of non-patients. A total of 198 subjects were body examined with a ROBE I protocol. The sample data were entered into a separate factor analysis for each domain. Principal components with varimax rotations were used, and the first two factors for each domain were extracted. The original 254 variables were reduced to 144, constituting an instrument for body examination, ROBE II, with 10 subscales. All subscales showed satisfactory internal consistency. Within all but one of the domains, the subscales showed acceptable intercorrelation. All subscales disclosed information of body patterns relevant for tracing psychosomatic symptoms in accordance with Norwegian Psychomotor Physiotherapy (NPMP). The subscales of ROBE II distinguished the bodily characteristics of patients with psychosomatic, musculoskeletal and schizophrenic disorders.
The Effects of Extracorporeal Shock Wave Therapy on Spasticity in Chronic Stroke Patients

Objective : To evaluate the immediate and short term effects of extracorporeal shock wave therapy (ESWT) on elbow flexor spasticity after stroke, to compare the treatment effects according to the ESWT applied site (muscle group and musculotendinous junction group) and to assess the factors affecting the treatment effect. Method : A total of 32 stroke patients were enrolled, 23 of them were ESWT group and 9 were control group. ESWT was applied 1 session/week, total 3 sessions and 12 patients were applied at biceps muscle belly and 11 patients were applied at biceps musculotendinosus junction. Patients were evaluated using modified Ashworth scale (MAS), modified Tardieu scale (MTS), Korean-modified Barthel index (K-MBI) at baseline, immediately, 1 week and 4 weeks after ESWT. Results : MAS and MTS were significantly improved immediately after ESWT, but were not significantly changed at 1 week, 4 weeks in ESWT group. The scores of K-MBI improved but were not different between ESWT group and control group. The treatment effect was greater in musculotendinous junction group than muscle belly group in MTS and MAS immediately after ESWT. We could not find out any significant factors affecting the effects of ESWT. Conclusion : Spasticity after chronic stroke improved immediately after ESWT, but was not changed significantly at 1 week and 4 weeks after ESWT. Further studies about ESWT parameters for spasticity and factors affecting treatment effect are needed.
Ventajas de las baterías de exploración neuropsicológica en el trastorno por déficit de atención/hiperactividad

Introduccion. La evaluacion neuropsicologica de las funciones de atencion y de las funciones ejecutivas en pacientes con una sintomatologia compatible con el diagnostico de trastorno por deficit de atencion/hiperactividad (TDAH) supone un estudio prioritario para poder ofrecer un estudio basico, asi como para poder llevar a cabo un seguimiento evolutivo a corto y a largo plazo de los pacientes. Desarrollo. Se plantea un algoritmo diagnostico del TDAH, en el que se incluyen pruebas tradicionales e informatizadas para evaluar las funciones de atencion y las funciones ejecutivas. El desarrollo de nuevas baterias neuropsicologicas con esta finalidad supone el empleo informatizado de diversos test que demuestran ventajas tecnicas y metodologicas en cuanto a su empleo y en referencia al manejo posterior de los datos obtenidos. Conclusiones. Entre las ventajas de la evaluacion informatizada destacan el manejo temporal de las respuestas, la minima expresion del efecto experimentador, el ahorro de tiempo, la precision y rapidez de las puntuaciones, el tratamiento estadistico de nuevas puntuaciones y la mayor cercania respecto a los programas de investigacion.
Global Telemedicine and eHealth Updates: Knowledge Resources

Information and Communications Technologies (ICT) have opened new possibilities for information exchanging in the healthcare environment. New ways of medical assistance become possible, including remote medical assistance, promoting the increase of cohesion between the trinomial: patient, general practitioner and specialized physician. In this context, teleophthalmology can be defined as the application of ICT in the ophthalmology care of the patients, and it covers some facets as making diagnosis, monitoring disease evolution, preventive and teaching activities. This article describes the experience of applying teleophthalmology workflows in the public healthcare system of the province of Ávila, Spain.
INTERMEDIATE REPORT OF FINAL YEAR

Computer vision plays a major role in the ﬁeld of artiﬁcial intelligence. As a preliminary step of many computer vision tasks, semantic segmentation attracts massive research attention. Current methods lack accuracy, temporal cohesion and computational eﬃciency, and thus are not suﬃcient for real time video analysis with high throughput. This project aims to improve the performance of aforementioned aspects by modifying and integrating existing methods. Our current experiment shows satisfactory accuracy and coherence on image-based segmentation while computational eﬃciency needs improvement in the following steps.
Auto-Encoding Nearest Neighbor i-Vectors for Speaker Verification

In the last years, i-vectors followed by cosine or PLDA scoringtechniques were the state-of-the-art approach in speaker veri-fication. PLDA requires labeled background data, and thereexists a significant performance gap between the two scoringtechniques. In this work, we propose to reduce this gap by us-ing an autoencoder to transform i-vector into a new speaker vec-tor representation, which will be referred to as ae-vector. Theautoencoder will be trained to reconstruct neighbor i-vectors in-stead of the same training i-vectors, as usual. These neighbori-vectors will be selected in an unsupervised manner accordingto the highest cosine scores to the training i-vectors. The evalua-tion is performed on the speaker verification trials of VoxCeleb-1 database. The experiments show that our proposed ae-vectorsgain a relative improvement of 42% in terms of EER comparedto the conventional i-vectors using cosine scoring, which fillsthe performance gap between cosine and PLDA scoring tech-niques by 92%, but without using speaker labels
Jaw movements in two tonal contexts

Intra-syllabic articulatory movements have previously been shown to vary depending on tonal context. In this study we concentrate on the jaw and explore its movements in two tonal contexts, i.e. the falling and the rising tone of the Swedish word accents. EMA was used to track the mandible of 19 speakers while they read sentences with target words carrying similar word onsets. Evaluation of the acceleration (second derivative) of the jaw movements revealed three well-defined intervals: jaw opening, jaw open posture and jaw closing. Mixed effects models showed that the jaw opening and the jaw open posture were longer in the rising tone context. Concurrently, spatial data revealed lower jaw trajectories in the falling tone context. Our results resemble that of previous findings on the tongue body. In conclusion, the two tonal contexts induce different jaw movements, presumably because of the involved physical mechanism of two distinct tonal targets.