nils-herrmann/clean_scirepeval huggingface dataset:
3 D Tracking via Body Radio Reflections Citation
Year: 2014

This paper introduces WiTrack, a system that tracks the 3D motion of a user from the radio signals reflected off her body. It works even if the person is occluded from the WiTrack device or in a different room. WiTrack does not require the user to carry any wireless device, yet its accuracy exceeds current RF localization systems, which require the user to hold a transceiver. Empirical measurements with a WiTrack prototype show that, on average, it localizes the center of a human body to within a median of 10 to 13 cm in the x and y dimensions, and 21 cm in the z dimension. It also provides coarse tracking of body parts, identifying the direction of a pointing hand with a median of 11.2* WiTrack bridges a gap between RF based localization systems which locate a user through walls and occlusions, and human computer interaction systems like Kinect, which can track a user without instrumenting her body, but require the user to stay within the direct line of sight of the device.
On Iteratively Reweighted Algorithms for Nonsmooth Nonconvex Optimization in Computer Vision
Venue: SIAM J. Imaging Sci.
Year: 2015

Natural image statistics indicate that we should use nonconvex norms for most regularization tasks in image processing and computer vision. Still, they are rarely used in practice due to the challenge of optimization. Recently, iteratively reweighed \ell_1$ minimization (IRL1) has been proposed as a way to tackle a class of nonconvex functions by solving a sequence of convex \ell_2$ \ell_1$ problems. We extend the problem class to the sum of a convex function and a (nonconvex) nondecreasing function applied to another convex function. The proposed algorithm sequentially optimizes suitably constructed convex majorizers. Convergence to a critical point is proved when the Kurdyka Lojasiewicz property and additional mild restrictions hold for the objective function. The efficiency and practical importance of the algorithm are demonstrated in computer vision tasks such as image denoising and optical flow. Most applications seek smooth results with sharp discontinuities. These are achieved by combining nonc.
Dynamic clustering of time series data
Venue: ArXiv
Year: 2020

We propose a new method for clustering multivariate time series data based on Dynamic Linear Models. Whereas usual time series clustering methods obtain static membership parameters, our proposal allows each time series to dynamically change their cluster memberships over time. In this context, a mixture model is assumed for the time series and a flexible Dirichlet evolution for mixture weights allows for smooth membership changes over time. Posterior estimates and predictions can be obtained through Gibbs sampling, but a more efficient method for obtaining point estimates is presented, based on Stochastic Expectation Maximization and Gradient Descent. Finally, two applications illustrate the usefulness of our proposed model to model both univariate and multivariate time series: World Bank indicators for the renewable energy consumption of EU nations and the famous Gapminder dataset containing life expectancy and GDP per capita for various countries.
An integrative model of auditory phantom perception: Tinnitus as a unified percept of interacting separable subnetworks
Venue: Neuroscience Biobehavioral Reviews
Year: 2014

Tinnitus is a considered to be an auditory phantom phenomenon, a persistent conscious percept of a salient memory trace, externally attributed, in the absence of a sound source. It is perceived as a phenomenological unified coherent percept, binding multiple separable clinical characteristics, such as its loudness, the sidedness, the type (pure tone, noise) the associated distress and so on. A theoretical pathophysiological framework capable of explaining all these aspects in one model is highly needed. The model must incorporate both the deafferentation based neurophysiological models and the dysfunctional noise canceling model, and propose a 'tinnitus core' subnetwork. The tinnitus core can be defined as the minimal set of brain areas that needs to be jointly activated =subnetwork) for tinnitus to be consciously perceived, devoid of its affective components. The brain areas involved in the other separable characteristics of tinnitus can be retrieved by studies on spontaneous resting state magnetic and electrical activity in people with tinnitus, evaluated for the specific aspect investigated and controlled for other factors. By combining these functional imaging studies with neuromodulation techniques some of the correlations are turned into causal relationships. Thereof, a heuristic pathophysiological framework is constructed, integrating the tinnitus perceptual core with the other tinnitus related aspects. This phenomenological unified percept of tinnitus can be considered an emergent property of multiple, parallel, dynamically changing and partially overlapping subnetworks, each with a specific spontaneous oscillatory pattern and functional connectivity signature. Communication between these different subnetworks is proposed to occur at hubs, brain areas that are involved in multiple subnetworks simultaneously. These hubs can take part in each separable subnetwork at different frequencies. Communication between the subnetworks is proposed to occur at discrete oscillatory frequencies. As such, the brain uses multiple nonspecific networks in parallel, each with their own oscillatory signature, that adapt to the context to construct a unified percept possibly by synchronized activation integrated at hubs at discrete oscillatory frequencies.
Comprar Equine Internal Medicine 3rd Edition Debra C. Sellon 9781416056706 Saunders
Year: 2010

Tienda online donde Comprar Equine Internal Medicine 3rd Edition al precio 199,50 EUR de Debra C. Sellon Warwick M. Bayly Stephen M. Reed, tienda de Libros de Medicina, Libros de Veterinaria Anatomia/Anatomia patologica (Veterinaria)
AN INVESTIGATION OF TEACHING STRATEGY EMPLOYED BY ENGLISH TEACHER OF DEAF STUDENTS: A CASE STUDY AT AN INDONESIAN SPECIAL NEEDS SCHOOL
Year: 2019

The objectives of this research were to find out the implementation of teaching strategies by English teacher for deaf students and the challenges faced by the English teachers in using teaching strategies for deaf students. This research used qualitative method as the research method and case study as the research design. To obtain the data, observation, interview and documentation were used. The result of the research showed and infered that frequent teaching strategies applied by teacher in teaching English for deaf students were direct teaching and cooperative strategy. The reserach also revealed the challenges encountered by the teacher during teaching english to the deaf students were due to communication problems. Sometimes the students made mistakes in understanding words because of similar lip movements to other words; they got difficulties in differentiating similar words; they had limited vocabularies; and they were the lack of background knowledge. These led to the need of getting more the students' attention, adapting the material and media, and repetition in delivering the material.
Adaptive and Energy Efficient Architectures for Machine Learning: Challenges, Opportunities, and Research Roadmap
Venue: 2017 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)
Year: 2017

Gigantic rates of data production in the era of Big Data, Internet of Thing (IoT) Internet of Everything (IoE) and Cyber Physical Systems (CSP) pose incessantly escalating demands for massive data processing, storage, and transmission while continuously interacting with the physical world under unpredictable, harsh, and energy /power constrained scenarios. Therefore, such systems need to support not only the high performance capabilities at tight power/energy envelop, but also need to be intelligent/cognitive, self learning, and robust. As a result, a hype in the artificial intelligence research (e.g. deep learning and other machine learning techniques) has surfaced in numerous communities. This paper discusses the challenges and opportunities for building energy efficient and adaptive architectures for machine learning. In particular, we focus on brain inspired emerging computing paradigms, such as approximate computing; that can further reduce the energy requirements of the system. First, we guide through an approximate computing based methodology for development of energy efficient accelerators, specifically for convolutional Deep Neural Networks (DNNs) We show that in depth analysis of datapaths of a DNN allows better selection of Approximate Computing modules for energy efficient accelerators. Further, we show that a multi objective evolutionary algorithm can be used to develop an adaptive machine learning system in hardware. At the end, we summarize the challenges and the associated research roadmap that can aid in developing energy efficient and adaptable hardware accelerators for machine learning.
Population based guided local search for multidimensional Knapsack problem
Venue: 2015 Fourth International Conference on Future Generation Communication Technology (FGCT)
Year: 2015

A cooperative based Guided Local Search (GLS) framework has been proposed to deal with difficult combinatorial optimization problem in   This framework is called Population Based Guided Local Search (P GLS) In P GLS, we study how GLS performance can be further enhanced thorough designing a cooperative mechanism based on the search principle Proximate Optimality Principle (POP)  The experimental results on the traveling salesman problem (TSP) shows the effectiveness of P GLS compared to original GLS. The aim of this paper is to test the PGLS on another combinatorial optimization problem, namely Multidimensional Knapsack Problem (MKP) The experimental results also confirm the effectiveness of P GLS compared to the original GLS and the parallel GLS algorithm without collaboration.
Reduced Total Energy Requirements for the Natario Warp Drive Spacetime using Heaviside Step Functions as Analytical Shape Functions
Year: 2012

Warp Drives are solutions of the Einstein Field Equations that allows superluminal travel within the framework of General Relativity. There are at the present moment two known solutions: The Alcubierre warp drive discovered in 1994 and the Natario warp drive discovered in 2001. However as stated by both Alcubierre and Natario themselves the warp drive violates all the known energy conditions because the stress energy momentum tensor(the right side of the Einstein Field Equations) for the Einstein tensor G00 is negative implying in a negative energy density. While from a classical point of view the negative energy is forbidden the Quantum Field Theory allows the existence of very small amounts of it being the Casimir effect a good example as stated by Alcubierre himself.The major drawback concerning negative energies for the warp drive are the huge negative energy density requirements to sustain a stable warp bubble configuration. Ford and Pfenning computed these negative energy and concluded that at least 10 times the mass of the Universe is required to sustain a warp bubble configuration. However both Alcubierre and Natario warp drives as members of the same family of the Einstein Field Equations requires the so called shape functions in order to be mathematically defined. We present in this work two new shape functions for the Natario warp drive spacetime based on the Heaviside step function and one of these functions allows arbitrary superluminal speeds while keeping the negative energy density at "low" and "affordable" levels.We do not violate any known law of quantum physics and we maintain the original geometry of the Natario warp drive spacetime We also discuss briefly Horizons and infinite Doppler blueshifts.
The Experimental Study on Concrete Permeability of Wireless Communication Module Embedded in Reinforced Concrete Structures
Venue: Int. J. Distributed Sens. Networks
Year: 2013

Recently, as the information industry and mobile communication technology develop, their study is conducted on the new concept of intelligent structures and maintenance techniques that apply wireless sensor network, USN (Ubiquitous Sensor Network) to social infrastructures such as civil and architectural structures on the basis of the concept of Ubiquitous Computing, which invisibly provides human life with computing, along with mutually cooperating, compromising, and connecting networks to each other by having computers within all objects around us. The purpose of this study is to investigate the capability of wireless communication of sensor node embedded in reinforced concrete structure with a basic experiment on electric wave permeability of sensor node by fabricating molding with variables of concrete thickness and steel bars that are mostly used in constructing structures to determine the feasibility of application to constructing structures with USN. By installing wireless communication module inside the structures, it is possible to communicate to measure the pitch of steel bars and permeability of concrete, by measuring in both directions horizontally and vertically. The magnitude of an electric wave in the range of used frequencies was measured by using Spectrum Analyzer. This electric wave was numerically analyzed and the effective wavelength of frequencies was analyzed by the properties of a frequency band area. As a result of constructing structures with wireless sensors, Plain concrete showed 45 cm for depth of permeability. Reinforced concretes that has pitches of 5 cm showed 37 cm and pitches of 15 cm showed 45 cm for the depth of permeability. This suggests that if the pitch of steel bars was more than 15 cm, it would not affect wireless communication.
SISTEM INFORMASI SURAT KETERANGAN PENDAMPING IJAZAH (SKPI) POLITEKNIK NEGERI PADANG BERBASIS WEB MENGGUNAKAN FRAMEWORK LARAVEL
Year: 2018

Surat Keterangan Pendamping Ijazah (SKPI) merupakan produk pendidikan yang tidak bisa dilepaskan dari kerangka Kualifikasi Nasional Indonesia (KKNI) SKPI merupakan dokumen yang memuat kompetensi yang dimiliki oleh lulusan, dengan SKPI diharapkan calon pengguna lulusan (stakeholder) lebih mengetahui kecakapan (kompetensi) apa yang dimiliki calon karyawannya. Dari hasil survey peniliti saat ini masih banyak perguruan tinggi yang belum menggunakan SKPI sedangkan kampus yang sudah menggunakan SKPI masih menggunakan sistem blanko (manual) dalam mendata atau mendapatkan kompetensi yang dimiliki oleh calon lulusan untuk dimasukkan dalam SKPI. Sistem ini tentu masih memiliki kekurangan seperti butuh waktu yang cukup lama untuk dikembalikan oleh calon lulusan dan kehilangan dokumen yang telah dikumpulkan. Untuk mengatasi kekurangan tersebut maka dapat dirancang sebuah system SKPI dengan memanfaatkan Sistem Informasi. Penelitian ini menggunakan metode Research and Development. dengan model 4D (define design develop disseminate) RnD merupakan metode penelitian yang menekankan pada hasil atau produk akhir penelitian, sementara dalam pengembangan produk yang akan dihasilkan digunakan model pengembangan system SDLC (system development life cycle) dengan pendekatan waterfall. Produk akhir dari penelitian ini adalah system informasi SKPI yang dirancang dengan pemanfaatan Web. Sebelum SI ini didesiminasi terlebih dahulu dilakukan uji produk yang meliputi uji validitas, uji praktikalitas dan uji efektivitas. Berdasarkan ketiga uji produk tersebut didapat bahwa SI SKPI ini valid, praktis dan efektif digunakan dalam merancang SKPI lulusan perguruan Tinggi.
Agricultural Prices, Selection, and the Evolution of the Food Industry
Year: 2014

We present a model that explains the relationship between low input prices, high exit rates, and industrial concentration. We argue that falling input prices force less productive firms to exit the market, and lead to the expansion of more efficient incumbents at the expense of less productive producers. Our model helps reconcile some well established empirical results regarding the food processing industry. Indeed, agricultural prices fell between the early 1900s and 2006, and over the same period there was a trend towards higher concentration in the food industry, with an increase in average productivity.
Improved Fusion of Visual and Language Representations by Dense Symmetric Co attention for Visual Question Answering
Venue: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Year: 2018

A key solution to visual question answering (VQA) exists in how to fuse visual and language features extracted from an input image and question. We show that an attention mechanism that enables dense, bi directional interactions between the two modalities contributes to boost accuracy of prediction of answers. Specifically, we present a simple architecture that is fully symmetric between visual and language representations, in which each question word attends on image regions and each image region attends on question words. It can be stacked to form a hierarchy for multi step interactions between an image question pair. We show through experiments that the proposed architecture achieves a new state of the art on VQA and VQA 2.0 despite its small size. We also present qualitative evaluation, demonstrating how the proposed attention mechanism can generate reasonable attention maps on images and questions, which leads to the correct answer prediction.
Lifted Inference in 2 Variable Markov Logic Networks with Function and Cardinality Constraints Using Discrete Fourier Transform
Venue: ArXiv
Year: 2020

In this paper we show that inference in 2 variable Markov logic networks (MLNs) with cardinality and function constraints is domain liftable. To obtain this result we use existing domain lifted algorithms for weighted first order model counting (Van den Broeck et al, KR 2014) together with discrete Fourier transform of certain distributions associated to MLNs.
Searching for More Than Just a House? The Extent to Which Government Provided RDP Housing Compared to Self help Housing Empower Poor Communities in South Africa
Year: 2019

For the past 20 years, the government in South Africa provided low income communities with RDP (subsidy) houses to improve their quality of life and the places in which they reside. These houses have, however, been criticised regarding their location, construction locality, user participation and overall sustainability, while self help housing has been highlighted as a method to ensure greater community involvement and choice. This paper reports on a study to determine to which extent government provided RDP housing compared to self help housing empower poor communities in South Africa. The study compared a RDP and a self help (People's Housing Process) project in Botshabelo, a large settlement in the central parts of the country, by utilising a set of indicators and determinants of empowerment. The findings indicate that while some determinants lacked, others were equally evident in both projects. Yet, the self help housing project allowed greater opportunities to the residents for participation, skills development, improved self confidence and control over the final product (house) Consequently, it becomes important for planning and housing policies to also incorporate social development, in the form of community empowerment, into their desired outcomes.
The need for a system view to regulate artificial intelligence/machine learning based software as medical device
Venue: npj Digital Medicine
Year: 2020

Artificial intelligence (AI) and Machine learning (ML) systems in medicine are poised to significantly improve health care, for example, by offering earlier diagnoses of diseases or recommending optimally individualized treatment plans. However, the emergence of AI/ML in medicine also creates challenges, which regulators must pay attention to. Which medical AI/ML based products should be reviewed by regulators? What evidence should be required to permit marketing for AI/ML based software as a medical device (SaMD) How can we ensure the safety and effectiveness of AI/ML based SaMD that may change over time as they are applied to new data? The U.S. Food and Drug Administration (FDA) for example, has recently proposed a discussion paper to address some of these issues. But it misses an important point: we argue that regulators like the FDA need to widen their scope from evaluating medical AI/ML based products to assessing systems. This shift in perspective from a product view to a system view is central to maximizing the safety and efficacy of AI/ML in health care, but it also poses significant challenges for agencies like the FDA who are used to regulating products, not systems. We offer several suggestions for regulators to make this challenging but important transition.
IMPLEMENTATION OF ISO 50001 ENERGY MANAGEMENT SYSTEM WITH THE ADVANTAGE OF ARCHIVE VIEWER IN NSRRC
Year: 2020

Due to the limited energy resources in Taiwan, energy conservation is always a big issue for everyone who lives in this country. According to the data from the related departments, nearly 98% of energy is imported from abroad for more than a decade. Despite the strong dependency on foreign fuel imports, the energy subsidy policy leads to a relatively low cost of energy for end users, while it is not reasonable. In order to resolve the energy resource shortage and pursue a more efficient energy use, the implementation of ISO 50001 energy management system (EnMS) is activated with the advantage of the Archive Viewer in NSRRC this year. The energy management system will build up an overall energy usage model and a certain number of energy performance indicators to help us achieve efficient energy usage.
Emerging trends in IoT and big data analytics for biomedical and health care technologies
Year: 2020

The recent revolutions in Internet of Things (IoT) and big data analytics have unlocked promising possibilities in biomedical and health care technologies. The current chapter deals with theoretical, methodological, validated, and empirical concepts with exciting examples related to the subject. First, the application of IoT and big data in the analysis of a vast image database generated every day from various sources by big data combined with machine learning algorithms and other forms of artificial intelligence, to generate structured information for application in remote diagnostics, is explained with examples. Further emerging trends of telemedicine using artificial intelligence technique in robotics health care have been discussed. The flexibility and adaptability of AI based telemedicine provide the endless supports for health care development can be observed in the literature. The advancement of telerobotic surgery and the importance of the Internet of Robotic Things (IoRT) have been discussed briefly. Furthermore, details on wearable devices already available targeting the biomedical and health care applications that are capable of collecting, analyzing by standard protocols by machine intelligence for predictions of health related issues are described. Lastly, the final expansion and evolution of these technologies in the modern health care system and biomedical research, starting with personalized drug designing to targeted drug delivery and beyond it, is described.