Miracle-dz/UniFAR-FTUs huggingface dataset:
Strong Convergence of Forward-Backward-Forward Methods for Pseudo-monotone Variational Inequalities with Applications to Dynamic User Equilibrium in Traffic Networks

In infinite-dimensional Hilbert spaces we device a class of strongly convergent primal-dual schemes for solving variational inequalities defined by a Lipschitz continuous and pseudomonote map. Our novel numerical scheme is based on Tseng's forward-backward-forward scheme, which is known to display weak convergence, unless very strong global monotonicity assumptions are made on the involved operators. We provide a simple augmentation of this algorithm which is computationally cheap and still guarantees strong convergence to a minimal norm solution of the underlying problem. We provide an adaptive extension of the algorithm, freeing us from requiring knowledge of the global Lipschitz constant. We test the performance of the algorithm in the computationally challenging task to find dynamic user equilibria in traffic networks and verify that our scheme is at least competitive to state-of-the-art solvers, and in some case even improve upon them.
Learning from failures in robot-assisted feeding: Using online learning to develop manipulation strategies for bite acquisition

Successful robot-assisted feeding requires bite acquisition of a wide variety of food items. Different food items may require different manipulation actions for successful bite acquisition. Therefore, a key challenge is to handle previously-unseen food items with very different action distributions. By leveraging contexts from previous bite acquisition attempts, a robot should be able to learn online how to acquire those previously-unseen food items. In this ongoing work, we construct a contextual bandit framework for this problem setting. We then propose variants of the $\epsilon$-greedy and LinUCB contextual bandit algorithms to minimize cumulative regret within that setting. In future, we expect empirical estimates of cumulative regret for each algorithm on robot bite acquisition trials as well as updated theoretical regret bounds that leverage the more structured context of this problem setting.
Energy-Efficient and Quality-Assured Approximate Computing Framework Using a Co-Training Method

Approximate computing is a promising design paradigm that introduces a new dimension—error—into the original design space. By allowing the inexact computation in error-tolerance applications, approximate computing can gain both performance and energy efficiency. A neural network (NN) is a universal approximator in theory and possesses a high level of parallelism. The emerging deep neural network accelerators deployed with NN-based approximator is thereby a promising candidate for approximate computing. Nevertheless, the approximation result must satisfy the users’ requirement, and the approximation result varies across different applications. We normally deploy an NN-based classifier to ensure the approximation quality. Only the inputs predicted to meet the quality requirement can be executed by the approximator. The potential of these two NNs, however, is fully explored; the involving of two NNs in approximate computing imposes critical optimization questions, such as two NNs’ distinct views of the input data space, how to train the two correlated NNs, and what are their topologies. In this article, we propose a novel NN-based approximate computing framework with quality insurance. We advocate a co-training approach that trains the classifier and the approximator alternately to maximize the agreement of the two NNs on the input space. In each iteration, we coordinate the training of the two NNs with a judicious selection of training data. Next, we explore different selection policies and propose to select training data from multiple iterations, which can enhance the invocation of the approximate accelerator. In addition, we optimize the classifier by integrating a dynamic threshold tuning algorithm to improve the invocation of the approximate accelerator further. The increased invocation of accelerator leads to higher energy efficiency under the same quality requirement. We propose two efficient algorithms to explore the smallest topology of the NN-based approximator and the classifier to achieve the quality requirement. The first algorithm straightforward searches the minimum topology using a greedy strategy. However, the first algorithm incurs too much training overhead. To solve this issue, the second one gradually grows the topology of NNs to match the quality requirement by transferring the learned parameters. Experimental results show significant improvement on the quality and the energy efficiency compared to the existing NN-based approximate computing frameworks.
Automated web development: theme detection and code generation using Mix-NLP

A website helps a business to grow by using different marketing strategies. This paper describes a novel approach to develop a website by just providing the text (description of the website) or an image as input. Using Text Input it will suggest template (screenshots) after identifying the theme of the site inferred from the input. Those templates are converted into code for further customizations for their personal use. Current problem was that a web developer will take more than 15 days only to just make the basic structure of a website. This issue is resolved by our work which will generate the complete code of the webpage/ website in less amount of time. In this paper, it will tokenize each word to find their synonyms and then mapped it with root words for the theme identification and uses deep learning model to convert templates into code.
Fair Optimal Power Allocation for Non-orthogonal Multiple Access Heterogeneous Networks

In wireless communication transmission system, it is difficult to obtain the perfect channel state information (CSI) due to the stochastic channel condition and link delay, but most of studies for non-orthogonal multiple access (NOMA) heterogeneous networks always assume that the base station (BS) has perfect CSI. Therefore, in this paper, we study the energy efficient power allocation with a full consideration of users fairness for the NOMA heterogeneous networks based on the imperfect CSI. The probabilistic optimization problem with outage probability is transformed to a non-probabilistic problem via the approximation of inequality. The binary search method is designed to solve the power allocation problem of small cells. The simulation results show that the proposed algorithm with different parameters can greatly improve the energy efficiency of the system.
On a sampling expansion with partial derivatives for functions of several variables

Let $B^p_{\sigma}$, $1\le p 0$, denote the space of all $f\in L^p(\mathbb{R})$ such that the Fourier transform of $f$ (in the sense of distributions) vanishes outside $[-\sigma,\sigma]$. The classical sampling theorem states that each $f\in B^p_{\sigma}$ may be reconstructed exactly from its sample values at equispaced sampling points $\{\pi m/\sigma\}_{m\in\mathbb{Z}} $ spaced by $\pi /\sigma$. Reconstruction is also possible from sample values at sampling points $\{\pi \theta m/\sigma\}_m $ with certain $1< \theta\le 2$ if we know $f(\theta\pi m/\sigma) $ and $f'(\theta\pi m/\sigma)$, $m\in\mathbb{Z}$. In this paper we present sampling series for functions of several variables. These series involves samples of functions and their partial derivatives.
Muscle mitochondria and oxidative metabolism as targets against cancer cachexia

Cancer cachexia is a debilitating syndrome mainly characterized by muscle and fat wasting, leading to the progressive loss of body weight and complicating the management of cancer patient. In particular, the loss of muscle weight is a negative prognostic factor, being associated with chemotherapy toxicity and reduced survival. Increased inflammation and protein dysmetabolism are some of the impairments that lead to muscle wasting in cancer patients. Together with these alterations, tumor growth and chemotherapy administration may affect mitochondrial function, impinging on the muscle energy metabolism. Indeed, therapeutic approaches poised to correct both hypercatabolism and mitochondrial alterations could be effective in preventing cancer-induced muscle wasting. Among the non-pharmacological approaches, exercise training is one of the best modulator of muscle physiology able to impinge on both protein and energy metabolism. However, the wasting phenotype that characterizes cancer patients could be not compatible with physical training, prompting the development of different strategies to improve muscle metabolism. The aim of this mini-review is to discuss both the beneficial effects and the limitations of exercise training in cancer cachexia and the adoption of drugs able to modulate exercise-induced pathways.
UvA-DARE ( Digital Academic Repository ) ‘ Slum ’ and the City Exploring relations of informal settlements comparatively in Chennai , India and Durban , South Africa Saharan

As South Asia urbanizes, urban poverty is becoming more important in the development agenda. Many slums in India have existed for decades, despite multiple policies and programmes for reducing urban poverty. This article reveals the diversity of slums as outcomes linked to their internal development and their relations to the city’s macro-economic policies and institutional landscape. The focus is on the way these relations shape opportunities and constraints of households’ livelihoods over time in two slum settlements of Chennai. The research uses mixed methods to show how outcomes for households are closely linked to the settlement history, institutions and city region of which they are a part of. The article argues that slum development models and policies should promote relational understandings of slum areas to influence policies towards more effective support for reducing poverty among residents.
Post Thermal Sciatic Nerve Injury Successfully Repaired with a Sural Nerve Graft: Case Report

Loss of the sciatic nerve may result in paralysis and sensory loss of the leg. Among indications for lower limb amputation is blunt trauma to the nerve that is feared to have no chances of recovery. Injuries of thermal in nature are thought to have an extensive damage far from the site of injury and are thus unlikely to recover. The injured nerve is likely to heal by fibrosis and scarring making it difficult for any axons to go through it. The only hope for such recovery is removal of all injured sections followed by the nerve repair with a nerve graft or nerve transfer. With advances in microsurgery more and more injured nerves that were otherwise considered unsalvageable are now routinely reconstructed with relatively good outcomes. A good proportion of this are nerve injuries of the upper limbs such brachial plexus, median, ulnar and radial nerve injuries. Compared to the upper limb nerve injuries there is little data on the outcomes of lower limb nerve injuries. The tendency towards amputation of the lower limb following blunt trauma to the sciatic nerve is much higher than the upper limb. We present a case of post traumatic thermal sciatic nerve injury that was repaired with a sural nerve graft with good outcomes.
The UTexas system for TAC SM-KBP task 3: Probabilistic generation of coherent hypotheses

As an event unfolds, such as a crime, natural disaster, or conflict, it is often initially unclear what happened. Information is spread over many sources in different modalities and languages, and is often conflicting. For example, when flight MH-17 crashed in Ukraine in 2014, there were initially many theories of what happened, including a missile strike initiated by Russia-affiliated militants, a missile strike by the Ukrainian military, and a terrorist attack. Streaming Multimedia Knowledge Base Population (SM-KBP) is a new track at TAC 2018 that addresses this scenario through multiple tasks. Task 1 is about the extraction of information from multimodal and multi-lingual sources. Task 2 combines document-level knowledge graphs into a single multidocument knowledge graph and performs cross-document coreference. The resulting knowledge graph will contain incompatible narratives on what happened. Task 3 constructs “hypotheses”, internally consistent narratives, out of the overall incompatible knowledge base. Task 3 can thus be viewed as a “worst case” of multi-document summarization (Christensen et al., 2013; Wities et al., 2017) in which the documents tell incompatible stories. The UTexas team participated in Task 3, hypothesis generation. We view hypothesis generation as a clustering task based on narrative coherence (Wang et al., 2018). However, it is not a standard clustering task, but clustering combined with inference, where the inference incorporates background knowledge on the compatibility or incompatibility of knowledge subgraphs. The UTexas system for TAC 2018 uses probabilistic programming (Goodman and Stuhlmüller, 2014) to implement a probabilistic generative process that selects connected and compatible subgraphs.
Development of an On-Line Wall-Fouling Sensor for Pipeline Transportation of Heavy Oil-Water Mixtures

A beneficial method for transporting highly viscous hydrocarbons (e.g. heavy oil and bitumen) through a pipeline is known as Lubricated Pipe Flow (LPF). A major challenge for this technology is flow instability caused by the formation of a wall-coating of oil or the thinning and/or loss of the lubricating water layer in the pipe. This issue can be addressed by using capacitance sensors to measure thicknesses of both the oil and water layers. The objective of our research was to test the responsiveness of these sensors to typical LPF flow conditions. Two laboratory-scale capacitance sensors were tested. The experimental results were verified with simulations produced using COMSOL Multiphysics. COMSOL simulations were also used to determine the sensitivity of a capacitance sensor. Our research suggests that such a sensor can be used for commercial LPF installations.
Study Skills and Its RelatedFactors inJahrom University of Medical Sciences in 2014

Study skills are the hide and reveal in the area of cognitive and emotional behaviors that are associated with individual learning. Do not use principles of study skills are reduced learning and students progress. So the evaluation of their Study Skills is essential. The aim of this study was to determine study skills in students of In this descriptive-analytical cross-sectional study, 453 students were selected through stratified random sampling. Congos learning skills scale including six subscales (Reading ability of texts ،Taking notes ، Enhance memory ،Preparation for exam ،Increased focus and time management)was employed for gathering data. Data analysis was performed using SPSS-ver.22 and descriptive and analytic statistics including independent t-test and ANOVA, Tukey post hoc, Pearson correlation coefficient and Chi-Square. The mean scores of students’ study skills were 2.3±0.5 out of 4. Study skills of 292 (64.5%) of the students was weak, 145 (32%)were moderate and only 16 (3.5%) were in good skills. There were statistically significant differences between study skills and sex and students’ Grade Point Mean(GPA)(p<0.001). Significant statistical differences were not found between age, field of study and academic semester with study skills.Considering that study skills in most of students were too weak to moderate, it is recommended to teach study skills to students.
Divided in Morals , United in Selfishness an Experiment

People use moral wiggle room to behave selfish. But does a narrow wiggle room necessarily produce better social outcomes? When people disagree on normative goals, economic theories of self-image predict that narrowing the moral wiggle room will make choices not only less selfish but also even more heterogeneous, and thus potentially more conflictive. I test this conjecture experimentally in a modified dictator game with and without moral introspection. My results confirm the theoretical predictions, revealing a large disparity of moral valuations of efficiency vs. equality. (C91, D03, D63, A13, Z13)
Customer Switching Behavior dalam Membeli Batik dari UKM

Abstrak– Tujuan penelitian ini adalah untuk menganalisis customer switching behavior dalam membeli batik dari UKM . Desain penelitian ini adalah cross sectional, menggunakan pendekatan deskriptif dengan metode explanatory survei. Unit analisis konsumen sebanyak 106 orang. Pengumpulan data dengan menggunakan kuesioner. Teknik analisis yang digunakan adalah teknik deskriptif dengan menggunakan distribusi frekuensi. Berdasarkan hasil penelitian menggunakan analisis deskriptif, didapatkan hasil bahwa customer switching behavior berkatagori baik. Perbedaan penelitian ini dengan penelitian sebelumnya adalah terletak pada variabel independen, yaitu layanan purna jual pada produk otomotif, serta menggunakan teori atau referensi yang berbeda dengan peneliti sebelumnya.
An Environmental Scan of Use and Users of Digital Resources in Humanities and Social Sciences Undergraduate Education September 2006

This paper presents an overview of a two-year study funded by the Andrew W. Mellon and the William and Flora Hewlett Foundations that (1) mapped the universe of digital resources available to undergraduate educators in the humanities and social sciences (H/SS) and (2) examined how a better understanding of the variation in use and users can benefit the integration of these resources into undergraduate teaching. In order to address questions around user demand and resource sustainability, we used a variety of methodologies that included an extensive literature review; discussions with and surveys of faculty from different disciplines and institutions; and discussions and interviews with site owners, use researchers, librarians, and educational technology professionals. Our results suggest that faculty use a vast array of online materials from both educational and “non-educational” sources, including their own personal collections and the ubiquitous Google-type search. Individual characteristics, including disciplinary and institutional affiliation, affected patterns of use. Many faculty, however, do not use digital resources for a host of reasons including the lack of direct relevance to their preferred pedagogical approaches and insufficient time and classroom resources. Our discussions with digital resource providers confirmed that resources created by higher education institutions will continue to proliferate despite a lack of formal knowledge about users and/or clear models for financial sustainability. A more precise understanding of the diversity of use and user behavior, and the ability to share findings from user studies, will demand that the digital resource development community make typologies, standards of data and data collection, and results more transparent. * † Diane Harley, Ph.D., the Principal Investigator of this project, is a senior researcher at the Center for Studies in Higher Education at the University of California, Berkeley, and directs the Higher Education in the Digital Age (HEDA) Project. * The full report is available at http://repositories.cdlib. org/cshe/CSHE-11-06. Harley et al., USE AND USERS OF DIGITAL RESOURCES 2
Alternative Pricing Methods for Shout Call Options

Shout call options are exotic options that give the investor the ability to 'shout' during the life of the option, thus locking in a profit and resetting the strike price to the prevailing spot price. We look at two approaches to value such options. The first approach makes use of canonical variables of the classical heat equation and results in a series solution. In the second approach an integral formulation is used, which can be more amenable to pricing when there is more than one 'shout' allowed. Disciplines Engineering | Science and Technology Studies Publication Details Goard, J. (2019). Alternative Pricing Methods for Shout Call Options. Lecture Notes in Engineering and Computer Science, 2240 221-227. Proceedings of the World Congress on Engineering 2019 WCE 2019, July 3-5, 2019, London, U.K This journal article is available at Research Online: https://ro.uow. edu.au/eispapers1/3003 Alternative Pricing Methods for Shout Call Options Joanna Goard ∗ Abstract—Shout call options are exotic options that give the investor the ability to ‘shout’ during the life of the option, thus locking in a profit and resetting the strike price to the prevailing spot price. We look at two approaches to value such options. The first approach makes use of canonical variables of the classical heat equation and results in a series solution. In the second approach an integral formulation is used, which can be more amenable to pricing when there is more than one ‘shout’ allowed.Shout call options are exotic options that give the investor the ability to ‘shout’ during the life of the option, thus locking in a profit and resetting the strike price to the prevailing spot price. We look at two approaches to value such options. The first approach makes use of canonical variables of the classical heat equation and results in a series solution. In the second approach an integral formulation is used, which can be more amenable to pricing when there is more than one ‘shout’ allowed.
Photoswitching property of hairpin ODNs with azobenzene derivatives in the loop

Please note that technical editing may introduce minor changes to the text and/or graphics, which may alter content. The journal’s standard Terms & Conditions and the Ethical guidelines still apply. In no event shall the Royal Society of Chemistry be held responsible for any errors or omissions in this Accepted Manuscript or any consequences arising from the use of any information it contains. Accepted Manuscript MedChemComm
Hermod: principled and practical scheduling for serverless functions

Serverless computing has seen rapid growth due to the ease-of-use and cost-efficiency it provides. However, function scheduling, a critical component of serverless systems, has been overlooked. In this paper, we take a fist-principles approach toward designing a scheduler that caters to the unique characteristics of serverless functions as seen in real-world deployments. We first create a taxonomy of scheduling policies along three dimensions. Next, we use simulation to explore the scheduling policy space and show that frequently used features such as late binding and random load balancing are sub-optimal for common execution time distributions and load ranges. We use these insights to design Hermod, a scheduler for serverless functions with two key characteristics. First, to avoid head-of-line blocking due to high function execution time variability, Hermod uses a combination of early binding and processor sharing for scheduling at individual worker machines. Second, Hermod is cost, load, and locality-aware. It improves consolidation at low load, it employs least-loaded balancing at high load to retain high performance, and it reduces the number of cold starts compared to pure load-based policies. We implement Hermod for Apache OpenWhisk and demonstrate that, for the case of the function patterns observed in real-world traces, it achieves up to 85% lower function slowdown and 60% higher throughput compared to existing production and state-of-the-art research schedulers.