nils-herrmann/clean_scirepeval huggingface dataset:
Socio economic and health status of slum dwellers of the Kalyanpur slum in Dhaka city

This research was carried out to examine the socio economic and health condition of slum dwellers of Kalyanpur slum area. The study was both qualitative and quantitative type. In quantitative approach, primary data were used and collected from randomly selected (68 respondents) slum dwellers in the Kalyanpur slum area. The secondary data were collected from journal papers, articles, books, official documents, thesis papers, daily newspapers etc. By analyzing the survey, it is seen that slum dwellers were deprived of basic needs. The study helps to identify the slum problems such as food and housing problems, poor sanitation condition, poor quality or no sewerage and drainage, inadequate educational facilities, lack of utility services like gas facility, electricity connection, piped water supply etc. From this study it is observed that every family live in a slum in unhygienic environment in poor condition. The slum dwellers work as garments workers (24.2% household workers (13.7% rickshaw pullers (19.2% street hawkers, day labourers, masons etc. The range of household income of slum dwellers was about Tk 6,000 10,000 and they spent most of their money on food (61.39% of income) It was also seen that most of the slum dwellers were affected by various kinds of seasonal and waterborne diseases like fever, asthma, skin diseases, diarrheoa, jaundice etc. The study finds that poor socio economic status and inadequacy of urban services has had an immediate effect on urban health specially the slum poor in metropolitan cities in the country.  73 83, June 2016
Reconfigurable origami inspired acoustic waveguides
Venue: Science Advances

Researchers use reconfigurable origami inspired metamaterials to guide and redirect the propagation of sound. We combine numerical simulations and experiments to design a new class of reconfigurable waveguides based on three dimensional origami inspired metamaterials. Our strategy builds on the fact that the rigid plates and hinges forming these structures define networks of tubes that can be easily reconfigured. As such, they provide an ideal platform to actively control and redirect the propagation of sound. We design reconfigurable systems that, depending on the externally applied deformation, can act as networks of waveguides oriented along one, two, or three preferential directions. Moreover, we demonstrate that the capability of the structure to guide and radiate acoustic energy along predefined directions can be easily switched on and off, as the networks of tubes are reversibly formed and disrupted. The proposed designs expand the ability of existing acoustic metamaterials and exploit complex waveguiding to enhance control over propagation and radiation of acoustic energy, opening avenues for the design of a new class of tunable acoustic functional systems.
Towards Bayesian Deep Learning: A Survey
Venue: ArXiv

While perception tasks such as visual object recognition and text understanding play an important role in human intelligence, the subsequent tasks that involve inference, reasoning and planning require an even higher level of intelligence. The past few years have seen major advances in many perception tasks using deep learning models. For higher level inference, however, probabilistic graphical models with their Bayesian nature are still more powerful and flexible. To achieve integrated intelligence that involves both perception and inference, it is naturally desirable to tightly integrate deep learning and Bayesian models within a principled probabilistic framework, which we call Bayesian deep learning. In this unified framework, the perception of text or images using deep learning can boost the performance of higher level inference and in return, the feedback from the inference process is able to enhance the perception of text or images. This survey provides a general introduction to Bayesian deep learning and reviews its recent applications on recommender systems, topic models, and control. In this survey, we also discuss the relationship and differences between Bayesian deep learning and other related topics like Bayesian treatment of neural networks.
Towards Bayesian Deep Learning: A Framework and Some Existing Methods
Venue: IEEE Transactions on Knowledge and Data Engineering

While perception tasks such as visual object recognition and text understanding play an important role in human intelligence, subsequent tasks that involve inference, reasoning, and planning require an even higher level of intelligence. The past few years have seen major advances in many perception tasks using deep learning models. For higher level inference, however, probabilistic graphical models with their Bayesian nature are still more powerful and flexible. To achieve integrated intelligence that involves both perception and inference, it is naturally desirable to tightly integrate deep learning and Bayesian models within a principled probabilistic framework, which we call Bayesian deep learning. In this unified framework, the perception of text or images using deep learning can boost the performance of higher level inference and in return, the feedback from the inference process is able to enhance the perception of text or images. This paper proposes a general framework for Bayesian deep learning and reviews its recent applications on recommender systems, topic models, and control. In this paper, we also discuss the relationship and differences between Bayesian deep learning and other related topics such as the Bayesian treatment of neural networks.
Shifting Gender Identities and Second Language Socialization

Drawing on a multisite ethnographic study that spans educational, domestic, and workplace contexts in the United States and Laos, this article investigates the interplay between gender identity shifts and second language socialization, documenting the process by which working class Lao women and men redefine gender identities in the United States. Lao women in the United States experience increased opportunities for enacting their gender identities through expanded leadership roles and wage labor, but Lao men experience a narrowing of opportunities because they have lost access to traditional sources of power. Language learning both influences and is influenced by these changing identities. The author considers the impact of gender identity shifts on access to second language resources, with particular focus on workplace and domestic language events as venues for second language socialization, and discusses implications for ethnographic research on gendered second language socialization. This study highlights the need for ESL practitioners to investigate and address the complexity of the everyday language events in which adult ESL learners are engaged and raises questions regarding how adult ESL classrooms can become spaces for discussing, interpreting, and responding to gendered lives in a new land and a new language.
Gaining knowledge mediates changes in perception (without differences in attention) A case for perceptual learning
Venue: Behavioral and Brain Sciences

Firestone Scholl (F&S) assert that perceptual learning is not a top down effect, because experience mediated changes arise from familiarity with the features of the object through simple repetition and not knowledge about the environment. Emberson and Amso (2012) provide a clear example of perceptual learning that bypasses the authors' "pitfalls" and in which knowledge, not repeated experience, results in changes in perception.
A Chebotarev variant of the Brun Titchmarsh theorem and bounds for the Lang Trotter conjectures

We improve the Chebotarev variant of the Brun Titchmarsh theorem proven by Lagarias, Montgomery, and Odlyzko using the log free zero density estimate and zero repulsion phenomenon for Hecke L functions that were recently proved by the authors. Our result produces an improvement for the best unconditional bounds toward two conjectures of Lang and Trotter regarding the distribution of traces of Frobenius for elliptic curves and holomorphic cuspidal modular forms. We also obtain new results on the distribution of primes represented by positive definite integral binary quadratic forms.
'Say Hello to this Ad' The Persuasive Rhetorics of Fictive Interaction in Marketing

This chapter deals with strategically motivated discourse to show the fundamental role of the conversation frame in language for specific purposes. We focus on imagined speech acts in which advertised products and/or the 'problems' they tackle are fictively addressed through non genuine yes no responses (e.g. "Say no to wrinkles, say yes to this cream" and non actual greetings (e.g. "Hello wireless music. Goodbye wires" as well as marketing slogans and brands involving intra sentential fictive speech ascribed to a consumer, the advertiser, and/or the advertised product, service, or behavior (e.g. "I Love Art Tour" This study is based on our own database of printed advertisements, TV commercials, and brands in five different languages.
Three Kingdoms A Historical Novel

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Full Vector Signal Acquisition and Information Fusion for the Fault Prediction

Fault prediction is the key technology of the predictive maintenance. Currently, researches on fault prediction are mainly focused on the evaluation of the intensities of the failure and the remaining life of the machine. There is lack of methods on the prediction of fault locations and fault characters. To satisfy the requirement of the prediction of the fault characters, the data acquisition and fusion strategies were studied. Firstly, the traditional vibration measurement mechanism and its disadvantages were presented. Then, the full vector data acquisition and fusion model were proposed. After that, the sampling procedure and information fusion algorithm were analyzed. At last, the fault prediction method based on full vector spectrum was proposed. The methodology is that of Dr. Bently and Dr. Muszynska. On the basis of this methodology, the application study has been carried out. The uncertainty of the spectrum structure can be eliminated by the designed data acquisition and fusion method. The reliability of the diagnosis on fault character was improved. The study on full vector data acquisition system laid the technical foundation for the prediction and diagnosis research of the fault characters.
Data dimensionality reduction and data fusion for fast characterization of green coffee samples using hyperspectral sensors
Venue: Analytical and Bioanalytical Chemistry

AbstractHyperspectral sensors represent a powerful tool for chemical mapping of solid state samples, since they provide spectral information localized in the image domain in very short times and without the need of sample pretreatment. However, due to the large data size of each hyperspectral image, data dimensionality reduction (DR) is necessary in order to develop hyperspectral sensors for real time monitoring of large sets of samples with different characteristics. In particular, in this work, we focused on DR methods to convert the three dimensional data array corresponding to each hyperspectral image into a one dimensional signal (1D DR) which retains spectral and/or spatial information. In this way, large datasets of hyperspectral images can be converted into matrices of signals, which in turn can be easily processed using suitable multivariate statistical methods. Obviously, different 1D DR methods highlight different aspects of the hyperspectral image dataset. Therefore, in order to investigate their advantages and disadvantages, in this work, we compared three different 1D DR methods: average spectrum (AS) single space hyperspectrogram (SSH) and common space hyperspectrogram (CSH) In particular, we have considered 370 NIR hyperspectral images of a set of green coffee samples, and the three 1D DR methods were tested for their effectiveness in sensor fault detection, data structure exploration and sample classification according to coffee variety and to coffee processing method. Principal component analysis and partial least squares discriminant analysis were used to compare the three separate DR methods. Furthermore, low level and mid level data fusion was also employed to test the advantages of using AS, SSH and CSH altogether. Graphical AbstractKey steps in hyperspectral data dimenionality reduction
The Look of Fear from the Eyes Varies with the Dynamic Sequence of Facial Actions

Most research on the ability to interpret expressions from the eyes has utilized static information. This research investigates whether the dynamic sequence of facial actions in the eye region influences the judgments of perceivers. Dynamic fear expressions involving the eye region and eyebrows were created which systematically differed in the sequential occurrence of facial actions. Participants rated the intensity of sequential fear expressions, either in addition to a simultaneous, full blown expression (Experiment 1) or in combination with different levels of eye gaze (Experiment 2) The results showed that the degree of attributed emotion and the appraisal ratings differed as a function of the sequence of facial expressions of fear, with direct gaze resulting in stronger subjective responses. The findings challenge current notions surrounding the study of static facial displays from the eyes and suggest that emotion perception is a dynamic process shaped by the time course of the facial actions of an expression. Possible implications for the field of affective computing and clinical research are discussed.
Hidden Markov Model for a self learning of Simulated Annealing cooling law
Venue: 2016 5th International Conference on Multimedia Computing and Systems (ICMCS)

The Simulated Annealing (SA) is a stochastic local search algorithm. It is an adaptation of the Metropolis Hastings Monte Carlo algorithm. SA mimics the annealing process in metallurgy to approximate the global optimum of an optimization problem and uses a temperature parameter to control the search. The efficiency of the simulated annealing algorithm involves the adaptation of the cooling schedule. In this paper, we integrate Hidden Markov Model (HMM) in SA to iteratively predict the best cooling law according to the search history. Experiments performed on many benchmark functions show that our proposed scheme outperforms other SA variants in term of quality of solutions.
From Word Embeddings to Item Recommendation
Venue: ArXiv

Social network platforms can use the data produced by their users to serve them better. One of the services these platforms provide is recommendation service. Recommendation systems can predict the future preferences of users using their past preferences. In the recommendation systems literature there are various techniques, such as neighborhood based methods, machine learning based methods and matrix factorization based methods. In this work, a set of well known methods from natural language processing domain, namely Word2Vec, is applied to recommendation systems domain. Unlike previous works that use Word2Vec for recommendation, this work uses non textual features, the check ins, and it recommends venues to visit/check in to the target users. For the experiments, a Foursquare check in dataset is used. The results show that use of continuous vector space representations of items modeled by techniques of Word2Vec is promising for making recommendations.
Architecture and Design of Cray DataWarp

This paper describes the architecture, design, use, and performance of Cray DataWarp. DataWarp is an infrastructure that uses direct attached solid state disk (SSD) storage to provide more cost effective bandwidth than an external parallel file system (PFS) allowing DataWarp to be provisioned for bandwidth and the PFS to be provisioned for capacity and resiliency. Placing this storage between the application and the PFS allows application I/O to be decoupled from PFS I/O and in some cases avoiding it altogether. This reduces the time required for the application to do I/O, while also increasing the overlap of computation with PFS I/O, typically reducing application elapsed time. DataWarp allocates and configures SSD backed storage for jobs and users on demand, providing many of the benefits of both software defined storage and storage virtualization.  Filesystems I/O, DataWarp, SSD
Palladium Catalyzed Direct b Arylation of Ketones with Diaryliodonium Salts: A Stoichiometric Heavy Metal Free and User Friendly Approach.

We herein report a new protocol for the Pd catalyzed b arylation of ketones without stoichiometric heavy metals. Widely accessible diaryliodonium salts are used as both the oxidant and aryl source. This tandem redox catalysis merges ketone dehydrogenation and conjugate addition without an additional oxidant or reductant. This transformation features the use of a unique bis N tosylsulfilimine ligand and the combination of potassium trifluoroacetate/trifluoroacetic acid to maintain an appropriate acidity of the reaction medium. The reaction tolerates both air and moisture, and shows a broad substrate scope. Kinetics studies, along with filtration and poisoning tests, support the involvement of palladium nanoparticles in the catalysis.
A pilot study evaluating the use of ABCD2 score in pre hospital assessment of patients with suspected transient ischaemic attack: experience and lessons learned
Venue: Experimental Translational Stroke Medicine

BackgroundSuspected transient ischaemic attack (TIA) is a common presentation to emergency medical services (EMS) in the United Kingdom (UK) Several EMS systems have adopted the ABCD2 score to aid pre hospital risk stratification and decision making on patient disposition, such as direct referral to an Emergency Department or specialist TIA clinic. However, the ABCD2 score, developed for hospital use, has not been validated for use in the pre hospital context of EMS care.MethodsWe conducted a pilot study to assess eligibility criteria, recruitment rates, protocol compliance, consent and follow up procedures to inform the development of a definitive study to validate the ABCD2 tool in pre hospital evaluation of patients with suspected TIA.ResultsFrom 1st May 1st September 2013, nine patients with an EMS suspected diagnosis of TIA had the TIA diagnosis later confirmed by a specialist from five participating sites. This recruitment rate is comparable to stroke trials in the EMS setting. Bureaucratic obstacles and duplication of approval processes across participating sites took 13 months to resolve before recruitment commenced. Due to the initial difficulty in recruitment, a substantial amendment was approved to modify inclusion criteria, allowing patients with atrial fibrillation and/or taking anticoagulant therapy to participate in the study.ConclusionsIt is possible to identify, recruit and follow up patients with suspected TIA in the EMS setting. Training large numbers of EMS staff is required as exposure to TIA patients is infrequent. Significant insight was gained into the complexity of NHS research governance mechanisms in the UK. This knowledge will facilitate the planning of a future adequately powered study to validate the ABCD2 tool in a pre hospital setting.
A GABAergic nigrotectal pathway for coordination of drinking behavior
Venue: Nature Neuroscience

The contribution of basal ganglia outputs to consummatory behavior remains poorly understood. We recorded from the substantia nigra pars reticulata (SNR) the major basal ganglia output nucleus, during self initiated drinking in mice. The firing rates of many lateral SNR neurons were time locked to individual licks. These neurons send GABAergic projections to the deep layers of the orofacial region of the lateral tectum (superior colliculus, SC) Many tectal neurons were also time locked to licking, but their activity was usually in antiphase with that of SNR neurons, suggesting inhibitory nigrotectal projections. We used optogenetics to selectively activate the GABAergic nigrotectal afferents in the deep layers of the SC. Photo stimulation of the nigrotectal projections transiently inhibited the activity of the lick related tectal neurons, disrupted their licking related oscillatory pattern and suppressed self initiated drinking. These results demonstrate that GABAergic nigrotectal projections have a crucial role in coordinating drinking behavior.
A High Gain Broadband Gradient Refractive Index Metasurface Lens Antenna
Venue: IEEE Transactions on Antennas and Propagation

The design, simulation, and measurement results of a high gain broadband gradient refractive index (GRIN) planar lens fed by an antipodal exponential taper slot antenna (ATSA) are presented. As a constituent part of this lens, a novel nonresonant metamaterial unit cell, composed of bilayer triple rectangular rings, is proposed and its equivalent circuit model is developed and described. It is shown that, by utilizing this element, stronger capacitive couplings between adjacent metallic layers are realized resulting in a large refractive index variation of about 2.5, and hence, a thin lens with a thickness of 0.38l0, where l0 is the wavelength at 9.5 GHz. In addition, since the unit cell is designed to resonate at higher frequencies, its refractive index response is smoothly increased over a broad frequency range and this considerably enhances the operating bandwidth of the lens. The achieved measured results demonstrate a broad matching and 3 dB gain bandwidths of 52% (7 12 GHz) and 65% (7 13.2 GHz) respectively. Furthermore, this lens offers a high aperture efficiency of 50% (21.2 dB gain) at the center frequency, and its sidelobe and cross polarization levels are less than 20 dB and 26 dB across the entire matched band, respectively.