nils-herrmann/clean_scirepeval huggingface dataset:
Sparse Depth Sensing for Resource-Constrained Robots

We consider the case in which a robot has to navigate in an unknown environment but does not have enough on-board power or payload to carry a traditional depth sensor (e.g., a 3D lidar) and thus can only acquire a few (point-wise) depth measurements. We address the following question: is it possible to reconstruct the geometry of an unknown environment using sparse and incomplete depth measurements? Reconstruction from incomplete data is not possible in general, but when the robot operates in man-made environments, the depth exhibits some regularity (e.g., many planar surfaces with only a few edges); we leverage this regularity to infer depth from a small number of measurements. Our first contribution is a formulation of the depth reconstruction problem that bridges robot perception with the compressive sensing literature in signal processing. The second contribution includes a set of formal results that ascertain the exactness and stability of the depth reconstruction in 2D and 3D problems, and completely characterize the geometry of the profiles that we can reconstruct. Our third contribution is a set of practical algorithms for depth reconstruction: our formulation directly translates into algorithms for depth estimation based on convex programming. In real-world problems, these convex programs are very large and general-purpose solvers are relatively slow. For this reason, we discuss ad-hoc solvers that enable fast depth reconstruction in real problems. The last contribution is an extensive experimental evaluation in 2D and 3D problems, including Monte Carlo runs on simulated instances and testing on multiple real datasets. Empirical results confirm that the proposed approach ensures accurate depth reconstruction, outperforms interpolation-based strategies, and performs well even when the assumption of structured environment is violated.
Structural plasticity upon learning: regulation and functions

Recent studies have provided long-sought evidence that behavioural learning involves specific synapse gain and elimination processes, which lead to memory traces that influence behaviour. The connectivity rearrangements are preceded by enhanced synapse turnover, which can be modulated through changes in inhibitory connectivity. Behaviourally related synapse rearrangement events tend to co-occur spatially within short stretches of dendrites, and involve signalling pathways partially overlapping with those controlling the functional plasticity of synapses. The new findings suggest that a mechanistic understanding of learning and memory processes will require monitoring ensembles of synapses in situ and the development of synaptic network models that combine changes in synaptic function and connectivity.
Least Median of Squares Regression

Classical least squares regression consists of minimizing the sum of the squared residuals. Many authors have produced more robust versions of this estimator by replacing the square by something else, such as the absolute value. In this article a different approach is introduced in which the sum is replaced by the median of the squared residuals. The resulting estimator can resist the effect of nearly 50% of contamination in the data. In the special case of simple regression, it corresponds to finding the narrowest strip covering half of the observations. Generalizations are possible to multivariate location, orthogonal regression, and hypothesis testing in linear models.
A revolution in optical manipulation

Optical tweezers use the forces exerted by a strongly focused beam of light to trap and move objects ranging in size from tens of nanometres to tens of micrometres. Since their introduction in 1986, the optical tweezer has become an important tool for research in the fields of biology, physical chemistry and soft condensed matter physics. Recent advances promise to take optical tweezers out of the laboratory and into the mainstream of manufacturing and diagnostics; they may even become consumer products. The next generation of single-beam optical traps offers revolutionary new opportunities for fundamental and applied research.
Framing as a Theory of Media Effects

Research on framing is characterized by theoretical and empirical vagueness. This is due, in part, to the lack of a commonly shared theoretical model underlying framing research. Conceptual problems translate into operational problems, limiting the comparability of instruments and results. In this paper I systematize the fragmented approaches to framing in political communication and integrate them into a comprehensive model. I classify previous approaches to framing research along two dimensions: the type of frame examined (media frames vs. audience frames) and the way frames are operationalized (independent variable or dependent variable). I develop a process model of framing, identifying four key processes that should be addressed in future research: frame building, frame setting, individuallevel processes of framing, and a feedback loop from audiences to journalists.
Parking space detection using ultrasonic sensor in parking assistance system

This paper deals with parking space detection by using ultrasonic sensor. Using the multiple echo function, the accuracy of edge detection was increased. After inspecting effect on the multiple echo function in indoor experiment, we applied to 11 types of vehicles in real parking environment and made experiments on edge detection with various values of resolution. We can scan parking space more accurately in real parking environment. We propose the diagonal sensor to get information about the side of parking space. Our proposed method has benefit calculation and implementation is very simple.
Analysis of Feature Selection Algorithms on Classification: A Survey

The aim of this paper is to discuss about various feature selection algorithms applied on different datasets to select the relevant features to classify data into binary and multi class in order to improve the accuracy of the classifier. Recent researches in medical diagnose uses the different kind of classification algorithms to diagnose the disease. For predicting the disease, the classification algorithm produces the result as binary class. When there is a multiclass dataset, the classification algorithm reduces the dataset into a binary class for simplification purpose by using any one of the data reduction methods and the algorithm is applied for prediction. When data reduction on original dataset is carried out, the quality of the data may degrade and the accuracy of an algorithm will get affected. To maintain the effectiveness of the data, the multiclass data must be treated with its original form without maximum reduction, and the algorithm can be applied on the dataset for producing maximum accuracy. Dataset with maximum number of attributes like thousands must incorporate the best feature selection algorithm for selecting the relevant features to reduce the space and time complexity. The performance of Classification algorithm is estimated by how accurately it predicts the individual class on particular dataset. The accuracy constrain mainly depends on the selection of appropriate features from the original dataset. The feature selection algorithms play an important role in classification for better performance. The feature selection is one of
How to scale distributed deep learning?

Training time on large datasets for deep neural networks is the principal workflow bottleneck in a number of important applications of deep learning, such as object classification and detection in automatic driver assistance systems (ADAS). To minimize training time, the training of a deep neural network must be scaled beyond a single machine to as many machines as possible by distributing the optimization method used for training. While a number of approaches have been proposed for distributed stochastic gradient descent (SGD), at the current time synchronous approaches to distributed SGD appear to be showing the greatest performance at large scale. Synchronous scaling of SGD suffers from the need to synchronize all processors on each gradient step and is not resilient in the face of failing or lagging processors. In asynchronous approaches using parameter servers, training is slowed by contention to the parameter server. In this paper we compare the convergence of synchronous and asynchronous SGD for training a modern ResNet network architecture on the ImageNet classification problem. We also propose an asynchronous method, gossiping SGD, that aims to retain the positive features of both systems by replacing the all-reduce collective operation of synchronous training with a gossip aggregation algorithm. We find, perhaps counterintuitively, that asynchronous SGD, including both elastic averaging and gossiping, converges faster at fewer nodes (up to about 32 nodes), whereas synchronous SGD scales better to more nodes (up to about 100 nodes).
The effect of folder structure on personal file navigation

Folder navigation is the main way that personal computer users retrieve their own files. People dedicate considerable time to creating systematic structures to facilitate such retrieval. Despite the prevalence of both manual organization and navigation, there is very little systematic data about how people actually carry out navigation, or about the relation between organization structure and retrieval parameters.The aims of our research were therefore to study users’ folder structure, personal file navigation, and the relations between them. We asked 296 participants to retrieve 1,131 of their active files and analyzed each of the 5,035 navigation steps in these retrievals. Folder structures were found to be shallow (files were retrieved from mean depth of 2.86 folders),with small folders (a mean of 11.82 files per folder) containing many subfolders (M = 10.64). Navigation was largely successful and efficient with participants successfully accessing 94% of their files and taking 14.76 seconds to do this on average. Retrieval time and success depended on folder size and depth. We therefore found the users’ decision to avoid both deep structure and large folders to be adaptive. Finally, we used a predictive model to
Prototype-Based Discriminative Feature Learning for Kinship Verification

In this paper, we propose a new prototype-based discriminative feature learning (PDFL) method for kinship verification. Unlike most previous kinship verification methods which employ low-level hand-crafted descriptors such as local binary pattern and Gabor features for face representation, this paper aims to learn discriminative mid-level features to better characterize the kin relation of face images for kinship verification. To achieve this, we construct a set of face samples with unlabeled kin relation from the labeled face in the wild dataset as the reference set. Then, each sample in the training face kinship dataset is represented as a mid-level feature vector, where each entry is the corresponding decision value from one support vector machine hyperplane. Subsequently, we formulate an optimization function by minimizing the intraclass samples (with a kin relation) and maximizing the neighboring interclass samples (without a kin relation) with the mid-level features. To better use multiple low-level features for mid-level feature learning, we further propose a multiview PDFL method to learn multiple mid-level features to improve the verification performance. Experimental results on four publicly available kinship datasets show the superior performance of the proposed methods over both the state-of-the-art kinship verification methods and human ability in our kinship verification task.
Information extraction from full text scientific articles: Where are the keywords?

To date, many of the methods for information extraction of biological information from scientific articles are restricted to the abstract of the article. However, full text articles in electronic version, which offer larger sources of data, are currently available. Several questions arise as to whether the effort of scanning full text articles is worthy, or whether the information that can be extracted from the different sections of an article can be relevant. In this work we addressed those questions showing that the keyword content of the different sections of a standard scientific article (abstract, introduction, methods, results, and discussion) is very heterogeneous. Although the abstract contains the best ratio of keywords per total of words, other sections of the article may be a better source of biologically relevant data.
The Gridfit algorithm: an efficient and effective approach to visualizing large amounts of spatial data

In a large number of applications, data is collected and referenced by their spatial locations. Visualizing large amounts of spatially referenced data on a limited-size screen display often results in poor visualizations due to the high degree of overplotting of neighboring datapoints. We introduce a new approach to visualizing large amounts of spatially referenced data. The basic idea is to intelligently use the unoccupied pixels of the display instead of overplotting data points. After formally describing the problem, we present two solutions which are based on: placing overlapping data points on the nearest unoccupied pixel; and shifting data points along a screen-filling curve (e.g., Hilbert-curve). We then develop a more sophisticated approach called Gridfit, which is based on a hierarchical partitioning of the data space. We evaluate all three approaches with respect to their efficiency and effectiveness and show the superiority of the Gridfit approach. For measuring the effectiveness, we not only present the resulting visualizations but also introduce mathematical effectiveness criteria measuring properties of the generated visualizations with respect to the original data such as distance- and position-preservation.
Push vs. pull: data movement for linked data structures

As the performance gap between the CPU and main memory continues to grow, techniques to hide memory latency are essential to deliver a high performance computer system. Prefetching can often overlap memory latency with computation for array-based numeric applications. However, prefetching for pointer-intensive applications still remains a challenging problem. Prefetching linked data structures (LDS) is difficult because the address sequence of LDS traversal does not present the same arithmetic regularity as array-based applications and the data dependence of pointer dereferences can serialize the address generation process.
In this paper, we propose a cooperative hardware/software mechanism to reduce memory access latencies for linked data structures. Instead of relying on the past address history to predict future accesses, we identify the load instructions that traverse the LDS, and execute them ahead of the actual computation. To overcome the serial nature of the LDS address generation, we attach a prefetch controller to each level of the memory hierarchy and push, rather than pull, data to the CPU. Our simulations, using four pointer-intensive applications, show that the push model can achieve between 4% and 30% larger reductions in execution time compared to the pull model.
An examination of verbal working memory capacity in children with specific language impairment.

This study investigated verbal working memory capacity in children with specific language impairment (SLI). The task employed in this study was the Competing Language Processing Task (CLPT) developed by Gaulin and Campbell (1994). A total of 40 school-age children participated in this investigation, including 20 with SLI and 20 normal language (NL) age-matched controls. Results indicated that the SLI and NL groups performed similarly in terms of true/false comprehension items, but that the children with SLI evidenced significantly poorer word recall than the NL controls, even when differences in nonverbal cognitive scores were statistically controlled. Distinct patterns of word-recall errors were observed for the SLI and NL groups, as well as different patterns of associations between CLPT word recall and performance on nonverbal cognitive and language measures. The findings are interpreted within the framework of a limited-capacity model of language processing.
Automatic identification of quasi-experimental designs for discovering causal knowledge

Researchers in the social and behavioral sciences routinely rely on quasi-experimental designs to discover knowledge from large data-bases. Quasi-experimental designs (QEDs) exploit fortuitous circumstances in non-experimental data to identify situations (sometimes called "natural experiments") that provide the equivalent of experimental control and randomization. QEDs allow researchers in domains as diverse as sociology, medicine, and marketing to draw reliable inferences about causal dependencies from non-experimental data. Unfortunately, identifying and exploiting QEDs has remained a painstaking manual activity, requiring researchers to scour available databases and apply substantial knowledge of statistics. However, recent advances in the expressiveness of databases, and increases in their size and complexity, provide the necessary conditions to automatically identify QEDs. In this paper, we describe the first system to discover knowledge by applying quasi-experimental designs that were identified automatically. We demonstrate that QEDs can be identified in a traditional database schema and that such identification requires only a small number of extensions to that schema, knowledge about quasi-experimental design encoded in first-order logic, and a theorem-proving engine. We describe several key innovations necessary to enable this system, including methods for automatically constructing appropriate experimental units and for creating aggregate variables on those units. We show that applying the resulting designs can identify important causal dependencies in real domains, and we provide examples from academic publishing, movie making and marketing, and peer-production systems. Finally, we discuss the integration of QEDs with other approaches to causal discovery, including joint modeling and directed experimentation.
Creating Trustworthy Robots : Lessons and Inspirations from Automated Systems

One of the most significant challenges of human-robot interaction research is designing systems which foster an appropriate level of trust in their users: in order to use a robot effectively and safely, a user must place neither too little nor too much trust in the system. In order to better understand the factors which influence trust in a robot, we present a survey of prior work on trust in automated systems. We also discuss issues specific to robotics which pose challenges not addressed in the automation literature, particularly related to reliability, capability, and adjustable autonomy. We conclude with the results of a preliminary web-based questionnaire which illustrate some of the biases which autonomous robots may need to overcome in order to promote trust in users.
EEG signal classification using PCA, ICA, LDA and support vector machines

0957-4174/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.eswa.2010.06.065 * Corresponding author.  In this work, we proposed a versatile signal processing and analysis framework for Electroencephalogram (EEG). Within this framework the signals were decomposed into the frequency sub-bands using DWT and a set of statistical features was extracted from the sub-bands to represent the distribution of wavelet coefficients. Principal components analysis (PCA), independent components analysis (ICA) and linear discriminant analysis (LDA) is used to reduce the dimension of data. Then these features were used as an input to a support vector machine (SVM) with two discrete outputs: epileptic seizure or not. The performance of classification process due to different methods is presented and compared to show the excellent of classification process. These findings are presented as an example of a method for training, and testing a seizure prediction method on data from individual petit mal epileptic patients. Given the heterogeneity of epilepsy, it is likely that methods of this type will be required to configure intelligent devices for treating epilepsy to each individual’s neurophysiology prior to clinical operation.
Millimeter-Wave Continuous Transverse Stub (CTS) Antenna Array Using Substrate Integrated Waveguide (SIW) Technology

A new continuous transverse stub (CTS) antenna array is presented in this paper. It is built using the substrate integrated waveguide (SIW) technology and designed for beam steering applications in the millimeter waveband. The proposed CTS antenna array consists of 18 stubs that are arranged in the SIW perpendicular to the wave propagation. The performance of the proposed CTS antenna array is demonstrated through simulation and measurement results. From the experimental results, the peak gain of 11.63-16.87 dBi and maximum radiation power of 96.8% are achieved in the frequency range 27.06-36 GHz with low cross-polarization level. In addition, beam steering capability is achieved in the maximum radiation angle range varying from -43° to 3 ° depending on frequency.
Toward brief “Red Flags” for autism screening: The Short Autism Spectrum Quotient and the Short Quantitative Checklist for Autism in toddlers in 1,000 cases and 3,000 controls [corrected].

Frontline health professionals need a "red flag" tool to aid their decision making about whether to make a referral for a full diagnostic assessment for an autism spectrum condition (ASC) in children and adults. The aim was to identify 10 items on the Autism Spectrum Quotient (AQ) (Adult, Adolescent, and Child versions) and on the Quantitative Checklist for Autism in Toddlers (Q-CHAT) with good test accuracy.



A case sample of more than 1,000 individuals with ASC (449 adults, 162 adolescents, 432 children and 126 toddlers) and a control sample of 3,000 controls (838 adults, 475 adolescents, 940 children, and 754 toddlers) with no ASC diagnosis participated. Case participants were recruited from the Autism Research Centre's database of volunteers. The control samples were recruited through a variety of sources. Participants completed full-length versions of the measures. The 10 best items were selected on each instrument to produce short versions.



At a cut-point of 6 on the AQ-10 adult, sensitivity was 0.88, specificity was 0.91, and positive predictive value (PPV) was 0.85. At a cut-point of 6 on the AQ-10 adolescent, sensitivity was 0.93, specificity was 0.95, and PPV was 0.86. At a cut-point of 6 on the AQ-10 child, sensitivity was 0.95, specificity was 0.97, and PPV was 0.94. At a cut-point of 3 on the Q-CHAT-10, sensitivity was 0.91, specificity was 0.89, and PPV was 0.58. Internal consistency was >0.85 on all measures.



The short measures have potential to aid referral decision making for specialist assessment and should be further evaluated.
Two-stage fully-differential inverter-based self-biased CMOS amplifier with high efficiency

This paper describes a novel two-stage fully-differential CMOS amplifier comprising two self-biased inverter stages, with optimum compensation and high efficiency. Although it relies on a class A topology, it is shown through simulations, that it achieves the highest efficiency of its class and comparable to the best class AB amplifiers. Due to the self-biasing, a low variability in the DC gain over process, temperature, and supply is achieved. A detailed circuit analysis, a design methodology for optimization and the most relevant simulation results are presented, together with a final comparison among state-of-the-art amplifiers.