nils-herrmann/clean_scirepeval huggingface dataset:
Architectural Style Classification Using Multinomial Latent Logistic Regression

Architectural style classification differs from standard classification tasks due to the rich inter-class relationships between different styles, such as re-interpretation, revival, and territoriality. In this paper, we adopt Deformable Part-based Models (DPM) to capture the morphological characteristics of basic architectural components and propose Multinomial Latent Logistic Regression (MLLR) that introduces the probabilistic analysis and tackles the multi-class problem in latent variable models. Due to the lack of publicly available datasets, we release a new large-scale architectural style dataset containing twenty-five classes. Experimentation on this dataset shows that MLLR in combination with standard global image features, obtains the best classification results. We also present interpretable probabilistic explanations for the results, such as the styles of individual buildings and a style relationship network, to illustrate inter-class relationships.
Encoding Reality: Prediction-Assisted Cortical Learning Algorithm in Hierarchical Temporal Memory

In the decade since Jeff Hawkins proposed Hierarchical Temporal Memory (HTM) as a model of neocortical computation, the theory and the algorithms have evolved dramatically. This paper presents a detailed description of HTM’s Cortical Learning Algorithm (CLA), including for the first time a rigorous mathematical formulation of all aspects of the computations. Prediction Assisted CLA (paCLA), a refinement of the CLA, is presented, which is both closer to the neuroscience and adds significantly to the computational power. Finally, we summarise the key functions of neocortex which are expressed in paCLA implementations. An Open Source project, Comportex, is the leading implementation of this evolving theory of the brain.
PESA : Phrase Pair Extraction as Sentence Splitting

Most statistical machine translation systems use phrase-to-phrase translations to capture local context information, leading to better lexical choice and more reliable local reordering. The quality of the phrase alignment is crucial to the quality of the resulting translations. Here, we propose a new phrase alignment method, not based on the Viterbi path of word alignment models. Phrase alignment is viewed as a sentence splitting task. For a given spitting of the source sentence (source phrase, left segment, right segment) find a splitting for the target sentence, which optimizes the overall sentence alignment probability. Experiments on different translation tasks show that this phrase alignment method leads to highly competitive translation results.
Neuroimaging studies of working memory: a meta-analysis.

We performed meta-analyses on 60 neuroimaging (PET and fMRI) studies of working memory (WM), considering three types of storage material (spatial, verbal, and object), three types of executive function (continuous updating of WM, memory for temporal order, and manipulation of information in WM), and interactions between material and executive function. Analyses of material type showed the expected dorsal-ventral dissociation between spatial and nonspatial storage in the posterior cortex, but not in the frontal cortex. Some support was found for left frontal dominance in verbal WM, but only for tasks with low executive demand. Executive demand increased right lateralization in the frontal cortex for spatial WM. Tasks requiring executive processing generally produce more dorsal frontal activations than do storage-only tasks, but not all executive processes show this pattern. Brodmann's areas (BAs) 6, 8, and 9, in the superior frontal cortex, respond most when WM must be continuously updated and when memory for temporal order must be maintained. Right BAs 10 and 47, in the ventral frontal cortex, respond more frequently with demand for manipulation (including dual-task requirements or mental operations). BA 7, in the posterior parietal cortex, is involved in all types of executive function. Finally, we consider a potential fourth executive function: selective attention to features of a stimulus to be stored in WM, which leads to increased probability of activating the medial prefrontal cortex (BA 32) in storage tasks.
Dynamic modeling, simulation & analysis of induction motor drives

The induction motor plays very important role in industrial sectors, primarily due to its robustness and lowcost. When the mechanical load is applied to induction motor which requires speed control, some of the drive and controlstrategies are based on the estimated axis speed of the motor. The speed measurement directly reduces its stability and increases the cost of drive implementation. This paper proposes an alternative methodology for estimating the speed of a three phase induction motor driven by a voltage source inverter, using space vector modulation under the scalar control strategy and based on artificial intelligent controller such as fuzzy, neural and adaptive neuro fuzzy system. To validate the performance of the proposed method under motor load torque and speed reference set point variations, simulation results are presented. The simulation results validates under no load, constant load as well as variable load of the proposed method and confirms ability to use induction motor speed control inpractice. The dynamic modeling, simulation and analysis of induction motor using Conventional, PI, Fuzzy Controller, ANN and Adaptive neuro fuzzy controller in open loop and closed loop have been presented.
From databases to dataspaces: a new abstraction for information management

The development of relational database management systems served to focus the data management community for decades, with spectacular results. In recent years, however, the rapidly-expanding demands of "data everywhere" have led to a field comprised of interesting and productive efforts, but without a central focus or coordinated agenda. The most acute information management challenges today stem from organizations (e.g., enterprises, government agencies, libraries, "smart" homes) relying on a large number of diverse, interrelated data sources, but having no way to manage their dataspaces in a convenient, integrated, or principled fashion. This paper proposes dataspaces and their support systems as a new agenda for data management. This agenda encompasses much of the work going on in data management today, while posing additional research objectives.
Semi-Supervised Learning of the Electronic Health Record with Denoising Autoencoders for Phenotype Stratification

Patient interactions with health care providers result in entries to electronic health records (EHRs). EHRs were built for clinical and billing purposes but contain many data points about an individual. Mining these records provides opportunities to extract electronic phenotypes that can be paired with genetic data to identify genes underlying common human diseases. This task remains challenging: high quality phenotyping is costly and requires physician review; many fields in the records are sparsely filled; and our definitions of diseases are continuing to improve over time. Here we develop and evaluate a semi-supervised learning method for EHR phenotype extraction using denoising autoencoders for phenotype stratification. By combining denoising autoencoders with random forests we find classification improvements across simulation models, particularly in cases where only a small number of patients have high quality phenotype. This situation is commonly encountered in research with EHRs. Denoising autoencoders perform dimensionality reduction allowing visualization and clustering for the discovery of new subtypes of disease. This method represents a promising approach to clarify disease subtypes and improve genotype-phenotype association studies that leverage EHRs.
On the Economics of Anonymity

Decentralized anonymity infrastructures are still not in wide use today. While there are technical barriers to a secure robust design, our lack of understanding of the incentives to participate in such systems remains a major roadblock. Here we explore some reasons why anonymity systems are particularly hard to deploy, enumerate the incentives to participate either as senders or also as nodes, and build a general model to describe the effects of these incentives. We then describe and justify some simplifying assumptions to make the model manageable, and compare optimal strategies for participants based on a variety of
Synthetic aperture techniques with a virtual source element

A new imaging technique has been proposed that combines conventional B-mode and synthetic aperture imaging techniques to overcome the limited depth of field for a highly focused transducer. The new technique improves lateral resolution beyond the focus of the transducer by considering the focus a virtual element and applying synthetic aperture focusing techniques. In this paper, the use of the focus as a virtual element is examined, considering the issues that are of concern when imaging with an array of actual elements: the tradeoff between lateral resolution and sidelobe level, the tradeoff between system complexity (channel count/amount of computation) and the appearance of grating lobes, and the issue of signal to noise ratio (SNR) of the processed image. To examine these issues, pulse-echo RF signals were collected for a tungsten wire in degassed water, monofilament nylon wires in a tissue-mimicking phantom, and cyst targets in the phantom. Results show apodization lowers the sidelobes, but only at the expense of lateral resolution, as is the case for classical synthetic aperture imaging. Grating lobes are not significant until spatial sampling is more than one wavelength, when the beam is not steered. Resolution comparable to the resolution at the transducer focus can be achieved beyond the focal region while obtaining an acceptable SNR. Specifically, for a 15-MHz focused transducer, the 6-dB beamwidth at the focus is 157 /spl mu/m, and with synthetic aperture processing the 6-dB beamwidths at 3, 5, and 7 mm beyond the focus are 189 /spl mu/m, 184 /spl mu/m, and 215 /spl mu/m, respectively. The image SNR is 38.6 dB when the wire is at the focus, and it is 32.8 dB, 35.3 dB, and 38.1 dB after synthetic aperture processing when the wire is 3, 5, and 7 mm beyond the focus, respectively. With these experiments, the virtual source has been shown to exhibit the same behavior as an actual transducer element in response to synthetic aperture processing techniques.
A simple music/voice separation method based on the extraction of the repeating musical structure

Repetition is a core principle in music. This is especially true for popular songs, generally marked by a noticeable repeating musical structure, over which the singer performs varying lyrics. On this basis, we propose a simple method for separating music and voice, by extraction of the repeating musical structure. First, the period of the repeating structure is found. Then, the spectrogram is segmented at period boundaries and the segments are averaged to create a repeating segment model. Finally, each time-frequency bin in a segment is compared to the model, and the mixture is partitioned using binary time-frequency masking by labeling bins similar to the model as the repeating background. Evaluation on a dataset of 1,000 song clips showed that this method can improve on the performance of an existing music/voice separation method without requiring particular features or complex frameworks.
Development and control of a three DOF spherical induction motor

This paper reports, to our knowledge, the first spherical induction motor (SIM) operating with closed loop control. The motor can produce up to 4 Nm of torque along arbitrary axes with continuous speeds up to 300 rpm. The motor's rotor is a two-layer copper-over-iron spherical shell. The stator has four independent inductors that generate thrust forces on the rotor surface. The motor is also equipped with four optical mouse sensors that measure surface velocity to estimate the rotor's angular velocity, which is used for vector control of the inductors and control of angular velocity and orientation. Design considerations including torque distribution for the inductors, angular velocity sensing, angular velocity control, and orientation control are presented. Experimental results show accurate tracking of velocity and orientation commands.
Energy-efficient work-stealing language runtimes

Work stealing is a promising approach to constructing multithreaded program runtimes of parallel programming languages.  The key insight is that threads in a work-stealing environment -- thieves and victims - have varying impacts on the overall program running time, and a coordination of their execution "tempo" can lead to energy efficiency with minimal performance loss. The centerpiece of HERMES is two complementary algorithms to coordinate thread tempo: the workpath-sensitive algorithm determines tempo for each thread based on thief-victim relationships on the execution path, whereas the workload-sensitive algorithm selects appropriate tempo based on the size of work-stealing deques. We construct HERMES on top of Intel Cilk Plus's runtime, and implement tempo adjustment through standard Dynamic Voltage and Frequency Scaling (DVFS). Benchmarks running on HERMES demonstrate an average of 11-12% energy savings with an average of 3-4% performance loss through meter-based measurements over commercial CPUs.
Octaband Internal Antenna for 4G Mobile Handset

A compact internal antenna that is composed of a coupled loop with two branch lines is presented. The proposed antenna operates at two wide frequency bands (698 ~ 960 MHz/1710 ~ 2690 MHz) to cover octabands LTE 700/GSM 850/GSM 900/DCS 1800/PCS 1900/WCDMA 2100/LTE 2300/LTE 2500 for the 4G mobile handset. The proposed antenna is designed and fabricated on a support (polyester; εr=3.2) with a limited volume, i.e., 7×11×46 mm3. The design methods and the measured results of the proposed antenna are presented in detail.
Using GOOSE messages in a main-tie-main scheme

Main-tie-main (MTM) transfer schemes increase reliability in a power system by switching a load bus to a secondary power source when a power interruption occurs on the primary source. Traditionally, the large number of physical I/O lines required makes main-tie-main schemes expensive to design and implement. Using Ethernet-based IEC 61850, these hardwired I/O lines can be removed and replaced with generic object-oriented substation event (GOOSE) messages. Adjusting the scheme for optimal performance is done via software which saves redesign time and rewiring time. Special attention is paid to change-of-state GOOSE only; no analog GOOSE messages are used, making the scheme fast and easy to configure, maintain, and troubleshoot. Applications such as fast motor-bus transfer are discussed with synchronization remaining at each breaker relay. Simulation test results recorded GOOSE message latencies on a system configured for a main-tie-main scheme. This paper presents details of open and closed, manual and automatic transfers.
Think to move: a neuromagnetic brain-computer interface (BCI) system for chronic stroke.

Stroke is a leading cause of long-term motor disability among adults. Present rehabilitative interventions are largely unsuccessful in improving the most severe cases of motor impairment, particularly in relation to hand function. Here we tested the hypothesis that patients experiencing hand plegia as a result of a single, unilateral subcortical, cortical or mixed stroke occurring at least 1 year previously, could be trained to operate a mechanical hand orthosis through a brain-computer interface (BCI).



Eight patients with chronic hand plegia resulting from stroke (residual finger extension function rated on the Medical Research Council scale=0/5) were recruited from the Stroke Neurorehabilitation Clinic, Human Cortical Physiology Section of the National Institute for Neurological Disorders and Stroke (NINDS) (n=5) and the Clinic of Neurology of the University of Tübingen (n=3). Diagnostic MRIs revealed single, unilateral subcortical, cortical or mixed lesions in all patients. A magnetoencephalography-based BCI system was used for this study. Patients participated in between 13 to 22 training sessions geared to volitionally modulate micro rhythm amplitude originating in sensorimotor areas of the cortex, which in turn raised or lowered a screen cursor in the direction of a target displayed on the screen through the BCI interface. Performance feedback was provided visually in real-time. Successful trials (in which the cursor made contact with the target) resulted in opening/closing of an orthosis attached to the paralyzed hand.



Training resulted in successful BCI control in 6 of 8 patients. This control was associated with increased range and specificity of mu rhythm modulation as recorded from sensors overlying central ipsilesional (4 patients) or contralesional (2 patients) regions of the array. Clinical scales used to rate hand function showed no significant improvement after training.



These results suggest that volitional control of neuromagnetic activity features recorded over central scalp regions can be achieved with BCI training after stroke, and used to control grasping actions through a mechanical hand orthosis.
Privacy-Preserving Ridge Regression on Hundreds of Millions of Records

Ridge regression is an algorithm that takes as input a large number of data points and finds the best-fit linear curve through these points. The algorithm is a building block for many machine-learning operations. We present a system for privacy-preserving ridge regression. The system outputs the best-fit curve in the clear, but exposes no other information about the input data. Our approach combines both homomorphic encryption and Yao garbled circuits, where each is used in a different part of the algorithm to obtain the best performance. We implement the complete system and experiment with it on real data-sets, and show that it significantly outperforms pure implementations based only on homomorphic encryption or Yao circuits.
Continuous Manifold Based Adaptation for Evolving Visual Domains

We pose the following question: what happens when test data not only differs from training data, but differs from it in a continually evolving way? The classic domain adaptation paradigm considers the world to be separated into stationary domains with clear boundaries between them. However, in many real-world applications, examples cannot be naturally separated into discrete domains, but arise from a continuously evolving underlying process. Examples include video with gradually changing lighting and spam email with evolving spammer tactics. We formulate a novel problem of adapting to such continuous domains, and present a solution based on smoothly varying embeddings. Recent work has shown the utility of considering discrete visual domains as fixed points embedded in a manifold of lower-dimensional subspaces. Adaptation can be achieved via transforms or kernels learned between such stationary source and target subspaces. We propose a method to consider non-stationary domains, which we refer to as Continuous Manifold Adaptation (CMA). We treat each target sample as potentially being drawn from a different subspace on the domain manifold, and present a novel technique for continuous transform-based adaptation. Our approach can learn to distinguish categories using training data collected at some point in the past, and continue to update its model of the categories for some time into the future, without receiving any additional labels. Experiments on two visual datasets demonstrate the value of our approach for several popular feature representations.
Parsing Clothes in Unrestricted Images

Parsing for clothes in images and videos is a critical step towards understanding the human appearance. In this work, we propose a method to segment clothes in settings where there is no restriction on number and type of clothes, pose of the person, viewing angle, occlusion and number of people. This is a challenging task as clothes, even of the same category, have large variations in color and texture. The presence of human joints is the best indicator for cloth types as most of the clothes are consistently worn around the joints. We incorporate the human joint prior by estimating the body joint distributions using the detectors and learning the cloth-joint co-occurrences of different cloth types with respect to body joints. The cloth-joint and cloth-cloth co-occurrences are used as a part of the conditional random field framework to segment the image into different clothing. Our results indicate that we have outperformed the recent attempt [16] on H3D , a fairly complex dataset.
Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling

Recurrent neural networks (RNN), convolutional neural networks (CNN) and selfattention networks (SAN) are commonly used to produce context-aware representations. RNN can capture long-range dependency but is hard to parallelize and not time-efficient. CNN focuses on local dependency but does not perform well on some tasks. SAN can model both such dependencies via highly parallelizable computation, but memory requirement grows rapidly in line with sequence length. In this paper, we propose a model, called “bi-directional block self-attention network (Bi-BloSAN)”, for RNN/CNN-free sequence encoding. It requires as little memory as RNN but with all the merits of SAN. Bi-BloSAN splits the entire sequence into blocks, and applies an intra-block SAN to each block for modeling local context, then applies an inter-block SAN to the outputs for all blocks to capture long-range dependency. Thus, each SAN only needs to process a short sequence, and only a small amount of memory is required. Additionally, we use feature-level attention to handle the variation of contexts around the same word, and use forward/backward masks to encode temporal order information. On nine benchmark datasets for different NLP tasks, Bi-BloSAN achieves or improves upon state-of-the-art accuracy, and shows better efficiency-memory trade-off than existing RNN/CNN/SAN.
Webpage Saliency

Webpage is becoming a more and more important visual input to us. While there are few studies on saliency in webpage, we in this work make a focused study on how humans deploy their attention when viewing webpages and for the first time propose a computational model that is designed to predict webpage saliency. A dataset is built with 149 webpages and eye tracking data from 11 subjects who free-view the webpages. Inspired by the viewing patterns on webpages, multi-scale feature maps that contain object blob representation and text representation are integrated with explicit face maps and positional bias. We propose to use multiple kernel learning (MKL) to achieve a robust integration of various feature maps. Experimental results show that the proposed model outperforms its counterparts in predicting webpage saliency.
Emotional behavior in long-term marriage.

In exploring the emotional climate of long-term marriages, this study used an observational coding system to identify specific emotional behaviors expressed by middle-aged and older spouses during discussions of a marital problem. One hundred and fifty-six couples differing in age and marital satisfaction were studied. Emotional behaviors expressed by couples differed as a function of age, gender, and marital satisfaction. In older couples, the resolution of conflict was less emotionally negative and more affectionate than in middle-aged marriages. Differences between husbands and wives and between happy and unhappy marriages were also found. Wives were more affectively negative than husbands, whereas husbands were more defensive than wives, and unhappy marriages involved greater exchange of negative affect than happy marriages.