nils-herrmann/clean_scirepeval huggingface dataset:
A Reconfigurable Architecture for Posit Arithmetic
Venue: 2019 22nd Euromicro Conference on Digital System Design (DSD)
Year: 2019

Physical layer of modern communication systems involves several floating point computations that require higher numerical fidelity and dynamic range for achieving the maximum data rate. Posit number format is a promising alternative for floating point computation as it provides a better dynamic range and numerical fidelity for the same number of bits used for representation. However, the Posit number format has not yet been thoroughly studied in the context of signal processing algorithms in the physical layer. In addition, a configurable Posit arithmetic hardware is essential for adapting to the ever changing communication standards and to configure the dynamic range according to algorithmic demands. The main contributions in this paper are: 1) Performance analysis of common signal processing algorithms using the Posit number format in comparison with the IEEE Standard for Single Precision Floating Point arithmetic (IEEE 754) 2) A novel reconfigurable hardware accelerator for Posit arithmetic operations and comparison with the state of the art Posit and IEEE Floating Point arithmetic architectures. Although our proposed architecture consumes over 3X energy and area compared to IEEE Single Precision arithmetic, our results show that using the Posit number format for physical layer algorithms results in significant performance gain. We achieved over 15 dB and 25 dB gain for FFT and matrix multiplication algorithms. In addition, compared to state of the art Posit arithmetic architecture, our proposed architecture resulted in over 2X speedup in operating frequency and 35% savings in energy consumption.
Guarantees for Tuning the Step Size using a Learning to Learn Approach
Venue: ICML
Year: 2021

Learning to learn (using optimization algorithms to learn a new optimizer) has successfully trained efficient optimizers in practice. This approach relies on meta gradient descent on a meta objective based on the trajectory that the optimizer generates. However, there were few theoretical guarantees on how to avoid meta gradient explosion/vanishing problems, or how to train an optimizer with good generalization performance. In this paper, we study the learning to learn approach on a simple problem of tuning the step size for quadratic loss. Our results show that although there is a way to design the meta objective so that the meta gradient remain polynomially bounded, computing the meta gradient directly using backpropagation leads to numerical issues that look similar to gradient explosion/vanishing problems. We also characterize when it is necessary to compute the meta objective on a separate validation set instead of the original training set. Finally, we verify our results empirically and show that a similar phenomenon appears even for more complicated learned optimizers parametrized by neural networks.
Clinical and Immunological Characteristics of Autoimmune Addison Disease: A Nationwide Swedish Multicenter Study
Venue: The Journal of clinical endocrinology and metabolism
Year: 2017

Studies of the clinical and immunological features of autoimmune Addison disease (AAD) are needed to understand the disease burden and increased mortality.  To provide upgraded data on autoimmune comorbidities, replacement therapy, autoantibody profiles, and cardiovascular risk factors.  A cross sectional, population based study that included 660 AAD patients from the Swedish Addison Registry (2008 2014) When analyzing the cardiovascular risk factors, 3594 individuals from the population based survey in Northern Sweden, MONICA (monitoring of trends and determinants of cardiovascular disease) served as controls.  The endpoints were the prevalence of autoimmune comorbidities and cardiovascular risk factors. Autoantibodies against 13 autoantigens were determined.  The proportion of 21 hydroxylase autoantibody positive patients was 83% and 62% of patients had =1 associated autoimmune diseases, more frequently coexisting in females (P 0.0001) AAD patients had a lower body mass index (P 0.0001) and prevalence of hypertension (P 0.027) compared with controls. Conventional hydrocortisone tablets were used by 89% of the patients, with a mean dose of 28.1 8.5 mg/d. The mean hydrocortisone equivalent dose normalized to the body surface was 14.8 4.4 mg/m2/d. A greater hydrocortisone equivalent dose was associated with a greater incidence of hypertension (P 0.046)  Careful monitoring of AAD patients is warranted to detect associated autoimmune diseases.  Swedish AAD patients did not have an increased prevalence of overweight, hypertension, type 2 diabetes mellitus, or hyperlipidemia. However, high glucocorticoid replacement doses could be a risk factor for hypertension.
Image Super Resolution Using Complex Dense Block on Generative Adversarial Networks
Venue: 2019 IEEE International Conference on Image Processing (ICIP)
Year: 2019

The recent super resolution (SR) techniques are divided into two directions. One is to improve PSNR and the other is to improve visual quality. We believe improving visual quality is more important and practical than blindly improving PSNR. In this paper we employ a generative adversarial network (GAN) and a new perceptual loss function for photo realistic single image super resolution (SISR) Our main contributions are as follows: we propose a new dense block which uses complex connections between each layer to build a more powerful generator. Next, to improve the perceptual quality, we found a new set of feature maps to compute the perceptual loss, which would make the output image look more real and natural. Finally, we compare our results with other methods by subjective evaluation. The subjects rank the image generated by various methods from good to bad. The final results show that our method can generate a more natural and realistic SR image than other state of the art methods.
Kernel Density Estimation, Kernel Methods, and Fast Learning in Large Data Sets
Venue: IEEE Transactions on Cybernetics
Year: 2014

Kernel methods such as the standard support vector machine and support vector regression trainings take O(N3) time and O(N2) space complexities in their naive implementations, where N is the training set size. It is thus computationally infeasible in applying them to large data sets, and a replacement of the naive method for finding the quadratic programming (QP) solutions is highly desirable. By observing that many kernel methods can be linked up with kernel density estimate (KDE) which can be efficiently implemented by some approximation techniques, a new learning method called fast KDE (FastKDE) is proposed to scale up kernel methods. It is based on establishing a connection between KDE and the QP problems formulated for kernel methods using an entropy based integrated squared error criterion. As a result, FastKDE approximation methods can be applied to solve these QP problems. In this paper, the latest advance in fast data reduction via KDE is exploited. With just a simple sampling strategy, the resulted FastKDE method can be used to scale up various kernel methods with a theoretical guarantee that their performance does not degrade a lot. It has a time complexity of O(m3) where m is the number of the data points sampled from the training set. Experiments on different benchmarking data sets demonstrate that the proposed method has comparable performance with the state of art method and it is effective for a wide range of kernel methods to achieve fast learning in large data sets.
Exploring Indonesian Interference on Morpho Syntactic Properties by Javanese Speakers: A Case Study of English Lecturers and Students' Interaction in Two Colleges in East Java, Indonesia
Year: 2018

This paper presents an analysis of integrating morpho syntactic theory in Indonesian interference which is influenced by lexical terms in L1 (Javanese) and L2 (Indonesian) The data were collected through questionnaires with a set of 12 open ended questions, 14 durative texts containing interference in lecturer student interaction, observation, interviews, and discussions with some experts in related research. This study involves 249 Indonesian undergraduate students who were majoring in English, and took speaking class, and joined presentations in content courses, together with their 10 English lecturers. The students and English lecturers conducted communicative classroom interactions in English teaching and learning process in IAIN and STKIP PGRI Tulungagung, East Java Indonesia. The data was analysed using a descriptive qualitative approach. The finding revealed that nine morpho syntactic types and patterns of Indonesian interference are developed significantly with morpho syntactic classification and properties including number, case, tense, mood, diathesis, agreement, comparative degree, aspect, and word class; three main patterns consisting of phrases (NP, VP, Adj P, Adv P) clauses and sentences. Interference is defined as the tendency to misuse English, which is students' L3 (third language) in studentstudent and student lecturer interaction due to the influence of Indonesian and Javanese structures. The study also showed that both L1 and L2 could not be the predicting factors Dwi Astuti Wahyu Nurhayati, Djatmika, Riyadi Santosa and Tri Wiratno 258 Pertanika  (T) 257 282 (2018) in interference occurrence. This may be attributed to their competence in acquiring a foreign language such as English. The results highlight that contextual English learning efforts based on students' needs and English lecturers' competence should be created. As English teachers and lecturers, they can make an effort by fulfilling the requirements as professional advanced models.
A New Competitive Binary Grey Wolf Optimizer to Solve the Feature Selection Problem in EMG Signals Classification
Venue: Comput.
Year: 2018

Features extracted from the electromyography (EMG) signal normally consist of irrelevant and redundant features. Conventionally, feature selection is an effective way to evaluate the most informative features, which contributes to performance enhancement and feature reduction. Therefore, this article proposes a new competitive binary grey wolf optimizer (CBGWO) to solve the feature selection problem in EMG signals classification. Initially, short time Fourier transform (STFT) transforms the EMG signal into time frequency representation. Ten time frequency features are extracted from the STFT coefficient. Then, the proposed method is used to evaluate the optimal feature subset from the original feature set. To evaluate the effectiveness of proposed method, CBGWO is compared with binary grey wolf optimization (BGWO1 and BGWO2) binary particle swarm optimization (BPSO) and genetic algorithm (GA) The experimental results show the superiority of CBGWO not only in classification performance, but also feature reduction. In addition, CBGWO has a very low computational cost, which is more suitable for real world application.
Efficient Markov Blanket Discovery and Its Application
Venue: IEEE Transactions on Cybernetics
Year: 2017

In a Bayesian network (BN) a target node is independent of all other nodes given its Markov blanket (MB) and finding the MB has many applications, including feature selection and BN structure learning. We propose a new MB discovery algorithm, simultaneous MB (STMB) to improve the efficiency of the existing topology based MB discovery algorithms. The proposed method removes the necessity of enforcing the symmetry constraint that is prevalent in existing algorithms, by exploiting the coexisting property between spouses and descendants of the target node. Since STMB mainly reduces the number of independence tests needed to complete the MB set after finding the parents and children set, it is applicable to all previous topology based methods. STMB is both sound and complete. Experiments show that STMB has a comparable accuracy but much better efficiency than state of the art methods. An application on benchmark feature selection datasets further demonstrates the excellent performance of STMB.
Effective degradation of sulfur mustard simulant using novel sulfur doped mesoporous zinc oxide under ambient conditions.
Venue: Journal of hazardous materials
Year: 2021

Sulfur doped metal oxides were synthesized using a two step precipitation method. When reacted against neat 2 CEES (2 chloroethyl ethyl sulfide, a mustard gas simulant) under ambient conditions, sulfur doped mesoporous zinc oxide (MS Zn) showed higher catalytic activity than the other metal oxides with 92.7% overall conversion in 24 h for a 2.5 mL neat 2 CEES droplet added on top of 2 x 2 cm large 400 mg catalyst layer. The reaction proceeded mainly by hydrolysis and further solvolysis reaction also occurred depending on the extracting solvents. Cyclic sulfonium ion intermediate reaction was thought to be involved in this reaction, and metal oxide surfaces were thought to facilitate the formation of sulfonium ions from adsorbed 2 CEES. All other by products were also found to form via sulfonium ions, reconfirming the well known importance of this intermediate species for the degradation reaction to proceed. The sulfur content for MS Zn was varied and tested for degradation of neat 2 CEES. This modification showed that there is an optimal amount of sulfur content for the peak catalytic activity of MS Zn for 2 CEES degradation. Adsorption energy of a 2 CEES molecule was calculated on model sulfur doped and non doped zinc oxide surfaces and the different adsorption energy levels were correlated with the catalytic activity of sulfur doped zinc oxide.
Methods of teaching foreign students to understand texts at the stage of pre university preparation (Russian as a foreign language)
Year: 2020

This article is devoted to the methods of teaching to work with text in such innovative technologies as a problematic technology and project technology as well as in technology of functional reading for the purpose to build skills and abilities of understanding and interpretation of the texts. The article is devoted to instructional techniques that are used when working with text in such innovative technologies as a problem and design technology, as well as technology training functional reading. These techniques make it possible to teach cognitive strategies to understand text information, necessary for the formation of skills and abilities of text activity in the target language. Most effectively they are used in the methods of teaching functional reading, in which the basic elements and principles of innovative technologies are implemented in the system and on a fundamentally new basis. The terms "functional literacy" and "minimum literacy" which were introduced in 1975, UNESCO designated range of skills and abilities, providing the ability to understand, evaluate and interpret texts, as well as the use of textual information in daily, academic and professional activities. Functional reading is one of the components of the minimum (functional) literacy. In the PISA study and PIRLS, dedicated to the analysis of the level of functional literacy in reading skills are defined as the ability to the comprehension of written texts and reflection, to extract the textual information and its use for their own purposes. In the modern methodological literature under the functional reading is understood reading to search for information to solve a particular problem, perform certain tasks, which uses methods of viewing, analytical, studying reading. In this article reveal indicates content of teaching the concept of functional reading and substantiates its effectiveness already in the elementary and basic levels of mastery of Russian as a foreign language including language specialty. The publication suggested possible forms of learning strategies of text comprehension and skills functional reading on the example of the organization of work with educational texts on language specialty "Jurisprudence" Basic level (A2) is shown system tasks, which use before the texts and after texts justified their didactic expediency and describes a method of implementation. In an e textbook has been used as a didactic material "Russian language (as a foreign language)" (Anokhina, Antipova, Grivina et al. 2018) in development of which the author participated.
Light Speed, Absolute Motion and Quantum Phenomena Does Nature Have a Foreknowledge of Observer's Motions and Actions? Scientific Proof of God
Year: 2020

Physicists have endeavored for decades to 'reconcile' Einstein's theory of relativity and quantum mechanics, and have failed. This paper reveals that this 'reconciliation' should have occurred in the genesis of the two theories, not after the two theories have been developed separately and dogmatized. The two theories developed separately from apparently unrelated problems in the behavior of light, namely: 1. The problem of absolute motion and the speed of light 2. Quantum phenomena. The mainstream view is that these two theories have addressed and solved all the puzzles in physics. However, experimental evidences increasingly challenge and disprove this view. I have been searching for an explanation for each of these apparently unrelated problems, by considering them as separate problems, and have gained crucial insights that have eluded physicists for centuries. However, these insights did not lead to a complete solution of the problems. I was finally able to find the missing link for the quantum puzzles and, profoundly, that turned out to be the missing link for the light speed problem also. The mystery is that nature acts as if it had a foreknowledge of the observer's motions and actions, which is another way of saying that God exists and directly intervenes in the operation of the universe. In the "Which Way" and Quantum Experiments, God foresees the experimental set up and, literally, aims each photon accordingly. In the case of light speed experiments, from a foreknowledge of the observer's motion, God emits a photon from the right point in space and time, with the right velocity of the center of the wave fronts so that the behavior of light speed in the different experiments and observations is the way it is. One of the long standing puzzles in physics is whether the speed of gravity is finite (light speed) or infinite. Suppose that the Sun disappeared suddenly. Will gravity of the Sun on Earth disappear instantaneously or with the delay of the speed of light? Nature emits zero gravitational field towards the Earth 8.3 minutes before the actual disappearance of the Sun. The zero gravitational field travels at the speed of light and reaches the Earth exactly at the instant the Sun disappears. By apparent violation of causality, nature allows the speed of gravity to be both finite and infinite.
Hydrogen vs. Battery in the Long term Operation. A Comparative Between Energy Management Strategies for Hybrid Renewable Microgrids
Year: 2020

The growth of the world's energy demand over recent decades in relation to energy intensity and demography is clear. At the same time, the use of renewable energy sources is pursued to address decarbonization targets, but the stochasticity of renewable energy systems produces an increasing need for management systems to supply such energy volume while guaranteeing, at the same time, the security and reliability of the microgrids. Locally distributed energy storage systems (ESS) may provide the capacity to temporarily decouple production and demand. In this sense, the most implemented ESS in local energy districts are small medium scale electrochemical batteries. However, hydrogen systems are viable for storing larger energy quantities thanks to its intrinsic high mass energy density. To match generation, demand and storage, energy management systems (EMSs) become crucial. This paper compares two strategies for an energy management system based on hydrogen priority vs. battery priority for the operation of a hybrid renewable microgrid. The overall performance of the two mentioned strategies is compared in the long term operation via a set of evaluation parameters defined by the unmet load, storage efficiency, operating hours and cumulative energy. The results show that the hydrogen priority strategy allows the microgrid to be led towards island operation because it saves a higher amount of energy, while the battery priority strategy reduces the energy efficiency in the storage round trip. The main contribution of this work lies in the demonstration that conventional EMS for microgrids' operation based on battery priority strategy should turn into hydrogen priority to keep the reliability and independence of the microgrid in the long term operation.
Translating without in domain corpus: Machine translation post editing with online learning techniques
Venue: Comput. Speech Lang.
Year: 2015

HighlightsWe present a method to customize machine translation systems when in domain data is not available.For that we perform an online learning automatic post editing from ready to use generic machine translation systems.The results show that the method is very effective on rule based machine translation systems.On statistical machine translation systems the method performs well if no in domain data was used in the training.Finally, if there is not enough repetition our method has limited use. Globalization has dramatically increased the need of translating information from one language to another. Frequently, such translation needs should be satisfied under very tight time constraints. Machine translation (MT) techniques can constitute a solution to this overly complex problem. However, the documents to be translated in real scenarios are often limited to a specific domain, such as a particular type of medical or legal text. This situation seriously hinders the applicability of MT, since it is usually expensive to build a reliable translation system, no matter what technology is used, due to the linguistic resources that are required to build them, such as dictionaries, translation memories or parallel texts. In order to solve this problem, we propose the application of automatic post editing in an online learning framework. Our proposed technique allows the human expert to translate in a specific domain by using a base translation system designed to work in a general domain whose output is corrected (or adapted to the specific domain) by means of an automatic post editing module. This automatic post editing module learns to make its corrections from user feedback in real time by means of online learning techniques. We have validated our system using different translation technologies to implement the base translation system, as well as several texts involving different domains and languages. In most cases, our results show significant improvements in terms of BLEU (up to 16 points) with respect to the baseline systems. The proposed technique works effectively when the n grams of the document to be translated presents a certain rate of repetition, situation which is common according to the document internal repetition property.