nils-herrmann/clean_scirepeval huggingface dataset:
Automating Transliteration of Cuneiform from Parallel Lines with Sparse Data

Cuneiform tablets appertain to the oldest textual artifacts and are in extent comparable to texts written in Latin or ancient Greek. The Cuneiform Commentaries Project (CPP) from Yale University provides tracings of cuneiform tablets with annotated transliterations and translations. As a part of our work analyzing cuneiform script computationally with 3D-acquisition and word-spotting, we present a first approach for automatized learning of transliterations of cuneiform tablets based on a corpus of parallel lines. These consist of manually drawn cuneiform characters and their transliteration into an alphanumeric code. Since the Cuneiform script is only available as raster-data, we segment lines with a projection profile, extract Histogram of oriented Gradients (HoG) features, detect outliers caused by tablet damage, and align those features with the transliteration. We apply methods from part-of-speech tagging to learn a correspondence between features and transliteration tokens. We evaluate point-wise classification with K-Nearest Neighbors (KNN) and a Support Vector Machine (SVM); sequence classification with a Hidden Markov Model (HMM) and a Structured Support Vector Machine (SVM-HMM). Analyzing our findings, we reach the conclusion that the sparsity of data, inconsistent labeling and the variety of tracing styles do currently not allow for fully automatized transliterations with the presented approach. However, the pursuit of automated learning of transliterations is of great relevance as manual annotation in larger quantities is not viable, given the few experts capable of transcribing cuneiform tablets.
Crowd-based City Logistics

Cities are drivers of economic development, providing infrastructure to support countless activities and services. Today, the world’s 750 biggest cities account for more than 57% of the global GDP and this number is expected to increase to 61% by 2030. More than half of the world’s population lives in cities, or urban areas, and this share will continue to growth. Rapid urban growth has posed both challenges and opportunities for city planners, not in the least when it comes to the design of transportation and logistic systems for freight. But urbanization also fosters innovation and sharing, which have led to new models for organizing movement of goods within the city. In this chapter, we highlight one of these new models: Crowd Logistics. We define the characterizing features of crowd logistics, review applications of crowd-based services within urban environments, and discuss research opportunities in the area of crowd logistics.
A Novel Association Rule Mining Method of Big Data for Power Transformers State Parameters Based on Probabilistic Graph Model

The correlative change analysis of state parameters can provide powerful technical supports for safe, reliable, and high-efficient operation of the power transformers. However, the analysis methods are primarily based on a single or a few state parameters, and hence the potential failures can hardly be found and predicted. In this paper, a data-driven method of association rule mining for transformer state parameters has been proposed by combining the Apriori algorithm and probabilistic graphical model. In this method the disadvantage that whenever the frequent items are searched the whole data items have to be scanned cyclically has been overcame. This method is used in mining association rules of the numerical solutions of differential equations. The result indicates that association rules among the numerical solutions can be accurately mined. Finally, practical measured data of five 500 kV transformers is analyzed by the proposed method. The association rules of various state parameters have been excavated, and then the mined association rules are used in modifying the prediction results of single state parameters. The results indicate that the application of the mined association rules improves the accuracy of prediction. Therefore, the effectiveness and feasibility of the proposed method in association rule mining has been proved.
Photoelectrochemical and Impedance Spectroscopic Analysis of Amorphous Si for Light-Guided Electrodeposition and Hydrogen Evolution Reaction.

For more efficient photoelectrochemical water splitting, there is a dilemma that a photoelectrode needs both light absorption and electrocatalytic faradaic reaction. One of the promising strategies is to deposit a pattern of electrocatalysts onto a semiconductor surface, leaving sufficient bare surface for light absorption while minimizing concentration overpotential as well as resistive loss at the ultramicroelectrodes for faradaic reaction. This scheme can be successfully realized by "maskless" direct photoelectrochemical patterning of electrocatalyst onto an SiOx/amorphous Si (a-Si) surface by the light-guided electrodeposition technique. Electrochemical impedance spectroscopy at various pHs tells us much about how it works. The surface states at the SiOx/a-Si interface can mediate the photogenerated electrons for hydrogen evolution, whereas electroactive species in the solution undergo outer-sphere electron transfer, taking electrons tunneling across the SiOx layer from the conduction band. In addition to previously reported long-distance lateral electron transport behavior at a patterned catalyst/SiOx/a-Si interface, the charging process of the surface states plays a crucial role in proton reduction, leading to deeper understanding of the operation mechanisms for photoelectrochemical water splitting.
Compact wideband metasurface-based circularly polarized antenna for Ka-band phased array

A metasurface-based compact wideband circularly polarized (CP) antenna is proposed. The antenna is composed of dual-layer overlapping metasurface coupled by cross-slot through single series stripline feed. A 64-element metasurface-based CP phased array with a scan angle up to 30 degree off the boresight direction shows the gain higher than 22 dBic, active reflection coefficient less than −9 dB, axial ratio less than 3 dB, and side-lobe levels less than −10 dB from 28 GHz to 31 GHz.
Technology Aware Training in Memristive Neuromorphic Systems for Nonideal Synaptic Crossbars

The advances in the field of machine learning using neuromorphic systems have paved the pathway for extensive research on possibilities of hardware implementations of neural networks. Various memristive technologies such as oxide-based devices, spintronics, and phase change materials have been explored to implement the core functional units of neuromorphic systems, namely the synaptic network, and the neuronal functionality, in a fast and energy efficient manner. However, various nonidealities in the crossbar implementations of the synaptic arrays can significantly degrade performance of neural networks, and hence, impose restrictions on feasible crossbar sizes. In this paper, we build mathematical models of various nonidealities that occur in crossbar implementations such as source resistance, neuron resistance, and chip-to-chip device variations and analyze their impact on the classification accuracy of a fully connected network (FCN) and convolutional neural network (CNN) trained with Backpropagation algorithm. We show that a network trained under ideal conditions can suffer accuracy degradation as large as 59.84% for FCNs and 62.4% for CNNs when implemented on nonideal crossbars for relevant nonideality ranges. This severely constrains the sizes for crossbars. As a solution, we propose a technology aware training algorithm, which incorporates the mathematical models of the nonidealities in the backpropagation algorithm. We demonstrate that our proposed methodology achieves significant recovery of testing accuracy within 1.9% of the ideal accuracy for FCNs and 1.5% for CNNs. We further show that our proposed training algorithm can potentially allow the use of significantly larger crossbar arrays of sizes 784 × 500 for FCNs and 4096 × 512 for CNNs with a minor or no tradeoff in accuracy.
Reconstruction and Simulation of Neocortical Microcircuitry

We present a first-draft digital reconstruction of the microcircuitry of somatosensory cortex of juvenile rat. The reconstruction uses cellular and synaptic organizing principles to algorithmically reconstruct detailed anatomy and physiology from sparse experimental data. An objective anatomical method defines a neocortical volume of 0.29 ± 0.01 mm(3) containing ~31,000 neurons, and patch-clamp studies identify 55 layer-specific morphological and 207 morpho-electrical neuron subtypes. When digitally reconstructed neurons are positioned in the volume and synapse formation is restricted to biological bouton densities and numbers of synapses per connection, their overlapping arbors form ~8 million connections with ~37 million synapses. Simulations reproduce an array of in vitro and in vivo experiments without parameter tuning. Additionally, we find a spectrum of network states with a sharp transition from synchronous to asynchronous activity, modulated by physiological mechanisms. The spectrum of network states, dynamically reconfigured around this transition, supports diverse information processing strategies.


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VIDEO ABSTRACT.
CuFusion: Accurate Real-Time Camera Tracking and Volumetric Scene Reconstruction with a Cuboid

Given a stream of depth images with a known cuboid reference object present in the scene, we propose a novel approach for accurate camera tracking and volumetric surface reconstruction in real-time. Our contribution in this paper is threefold: (a) utilizing a priori knowledge of the precisely manufactured cuboid reference object, we keep drift-free camera tracking without explicit global optimization; (b) we improve the fineness of the volumetric surface representation by proposing a prediction-corrected data fusion strategy rather than a simple moving average, which enables accurate reconstruction of high-frequency details such as the sharp edges of objects and geometries of high curvature; (c) we introduce a benchmark dataset CU3D that contains both synthetic and real-world scanning sequences with ground-truth camera trajectories and surface models for the quantitative evaluation of 3D reconstruction algorithms. We test our algorithm on our dataset and demonstrate its accuracy compared with other state-of-the-art algorithms. We release both our dataset and code as open-source (https://github.com/zhangxaochen/CuFusion) for other researchers to reproduce and verify our results.
Deep Learning Approach for Brain Machine Interface

of a dissertation at the University of Miami. Dissertation supervised by Professor Abhishek Prasad. No. of pages in text.  Brain machine interface (BMI) or Brain Computer Interface (BCI) provides a direct pathway between the brain and an external device to help people suffering from severely impaired motor function by decoding brain activities and translating human intentions into control signals. Conventionally, the decoding pipeline for BMIs consists of chained different stages of feature extraction, time-frequency analysis and statistical learning models. Each of these stages uses a different algorithm trained in a sequential manner, which makes the whole system difficult to be adaptive. Our goal is to create differentiable signal processing modules and plug them together to build an adaptive online system. The system could be trained with a single objective function and a single learning algorithm so that each component can be updated in parallel to increase the performance in a robust manner. We use deep neural networks to address these needs.  We predicted the finger trajectory using Electrocorticography (ECoG) signals and compared results for the Least Angle Regression (LARS), Convolutional Long Short Term Memory Network (Conv-LSTM), Random Forest (RF), and a pipeline consisting of band-pass filtering, energy extraction, feature selection and linear regression. The results showed that the deep learning models performed better than the commonly used linear model. The deep learning models not only gave smoother and more realistic trajectories but also learned the transition between movement and rest state. We also estimated the source connectivity of the brain signals using a Recurrent Neural Network (RNN) and it correctly estimated the order and sparsity level of the underlying Multivariate Auto-regressive process (MVAR). The time course of the source connectivity was also recovered.  We replace the conventional signal processing pipeline with differentiable modules so that the whole BMI system is adaptive. The study of the decoding system demonstrated a model for BMI that involved a convolutional and recurrent neural network. It integrated the feature extraction pipeline into the convolution and pooling layer and used Long Short Term Memory (LSTM) layer to capture the state transitions. The decoding network eliminated the need to separately train the model at each step in the decoding pipeline. The whole system can be jointly optimized using stochastic gradient descent and is capable of online learning. The study of the source connectivity estimation demonstrated a generative RNN model that can estimate the un-mixing matrix and the MVAR coefficients of the source activity at the same time. Our method addressed the issue of estimation and inference of the non-stationary MVAR coefficients and the un-mixing matrix in the presence of non-gaussian noise. More importantly, this model can be easily plugged into the BMI decoding system as a differentiable feature extraction module.
Online Affect Tracking with Multimodal Kalman Filters

Arousal and valence have been widely used to represent emotions dimensionally and measure them continuously in time. In this paper, we introduce a computational framework for tracking these affective dimensions from multimodal data as an entry to the Multimodal Affect Recognition Sub-Challenge of the 2016 Audio/Visual Emotion Challenge and Workshop (AVEC2016). We propose a linear dynamical system approach with a late fusion method that accounts for the dynamics of the affective state evolution (i.e., arousal or valence). To this end, single-modality predictions are modeled as observations in a Kalman filter formulation in order to continuously track each affective dimension. Leveraging the inter-correlations between arousal and valence, we use the predicted arousal as an additional feature to improve valence predictions. Furthermore, we propose a conditional framework to select Kalman filters of different modalities while tracking. This framework employs voicing probability and facial posture cues to detect the absence or presence of each input modality. Our multimodal fusion results on the development and the test set provide a statistically significant improvement over the baseline system from AVEC2016. The proposed approach can be potentially extended to other multimodal tasks with inter-correlated behavioral dimensions.
Pin assignment in automated printed circuit board design

The success of a router in automated printed circuit board design depends strongly on board organization prior to routing. One of the steps in board organization is the assignment of connections to pins, which can be made with considerable freedom since most chips have permutable circuits, gates, or gate input pins. Intelligently made pin assignment can reduce the two principal causes of router failure: (1) the crossing of connection paths and (2) the filling of available channels. The method described in this paper assigns a vector to each available chip pin and to each set of connections which are to be connected to a single pin on the chip. These vectors are used to assign the connections to the pins following a heuristic optimization procedure. This procedure was implemented as a FORTRAN program and gave a significant improvement in the number of wires routed on a large sample of typical boards. It is now incorporated into the UNIVAC automated P.C. board design program.
Highly stretchable carbon aerogels

Carbon aerogels demonstrate wide applications for their ultralow density, rich porosity, and multifunctionalities. Their compressive elasticity has been achieved by different carbons. However, reversibly high stretchability of neat carbon aerogels is still a great challenge owing to their extremely dilute brittle interconnections and poorly ductile cells. Here we report highly stretchable neat carbon aerogels with a retractable 200% elongation through hierarchical synergistic assembly. The hierarchical buckled structures and synergistic reinforcement between graphene and carbon nanotubes enable a temperature-invariable, recoverable stretching elasticity with small energy dissipation (~0.1, 100% strain) and high fatigue resistance more than 106 cycles. The ultralight carbon aerogels with both stretchability and compressibility were designed as strain sensors for logic identification of sophisticated shape conversions. Our methodology paves the way to highly stretchable carbon and neat inorganic materials with extensive applications in aerospace, smart robots, and wearable devices. Improved compressive elasticity was lately demonstrated for carbon aerogels but the problem of reversible stretchability remained a challenge. Here the authors use a hierarchical structure design and synergistic effects between carbon nanotubes and graphene to achieve high stretchability in carbon aerogels.
StackSeq2Seq: Dual Encoder Seq2Seq Recurrent Networks

A widely studied non-deterministic polynomial time (NP) hard problem lies in nding a route between the two nodes of a graph. Oen meta-heuristics algorithms such asA∗ are employed on graphs with a large number of nodes. Here, we propose a deep recurrent neural network architecture based on the Sequence-2-Sequence (Seq2Seq) model, widely used, for instance in text translation. Particularly, we illustrate that utilising a context vector that has been learned from two dierent recurrent networks enables increased accuracies in learning the shortest route of a graph. Additionally, we show that one can boost the performance of the Seq2Seq network by smoothing the loss function using a homotopy continuation of the decoder’s loss function.
Security metrics for software systems

Security metrics for software products provide quantitative measurement for the degree of trustworthiness for software systems. This paper proposes a new approach to define software security metrics based on vulnerabilities included in the software systems and their impacts on software quality. We use the Common Vulnerabilities and Exposures (CVE), an industry standard for vulnerability and exposure names, and the Common Vulnerability Scoring System (CVSS), a vulnerability scoring system designed to provide an open and standardized method for rating software vulnerabilities, in our metric definition and calculation. Examples are provided in the paper, which show that our definition of security metrics is consistent with the common practice and real-world experience about software quality in trustworthiness.
Analysis of drug combinations: current methodological landscape

Combination therapies exploit the chances for better efficacy, decreased toxicity, and reduced development of drug resistance and owing to these advantages, have become a standard for the treatment of several diseases and continue to represent a promising approach in indications of unmet medical need. In this context, studying the effects of a combination of drugs in order to provide evidence of a significant superiority compared to the single agents is of particular interest. Research in this field has resulted in a large number of papers and revealed several issues. Here, we propose an overview of the current methodological landscape concerning the study of combination effects. First, we aim to provide the minimal set of mathematical and pharmacological concepts necessary to understand the most commonly used approaches, divided into effect-based approaches and dose-effect-based approaches, and introduced in light of their respective practical advantages and limitations. Then, we discuss six main common methodological issues that scientists have to face at each step of the development of new combination therapies. In particular, in the absence of a reference methodology suitable for all biomedical situations, the analysis of drug combinations should benefit from a collective, appropriate, and rigorous application of the concepts and methods reviewed here.
Neural Models Comparison in Natural Language Generation

In the process of learning Machine Learning, we realized that natural language processing is a difficult task in Machine Learning in general due to the nature complexity of text process. Recurrent Neural Networks (RNNs) is current stateof-art model for language generation, whereas images can now be well generated by generative adversarial networks (GANs) method. Under this status quo, We would like to implement the two methods to extract the characteristics and generate language. We used the scripts of The Big Bang Theory since every character in the comedy has their own characteristics. So we expect that the two models will be able to extract their unique characteristics through training enough dataset and then generate new languages that may further generate a new episode. So far, due to the limit of time, we have trained Sheldon’s lines, and RNN has achieve a good result, however, GAN model has only trained 100 iterations, we are expecting better result for a larger iteration.
Entrapment neuropathy about the foot and ankle: an update.

Occurrences of entrapment neuropathies of the lower extremity are relatively infrequent; therefore, these conditions may be underappreciated and difficult to diagnose. Understanding the anatomy of the peripheral nerves and their potential entrapment sites is essential. A detailed physical examination and judicious use of imaging modalities are also vital when establishing a diagnosis. Once an accurate diagnosis is obtained, treatment is aimed at reducing external pressure, minimizing inflammation, correcting any causative foot and ankle deformities, and ultimately releasing any constrictive tissues.
Help to find the needle in a haystack: integrating recommender systems in an IT supported staff recruitment system

Despite the benefits of information technology for corporations in staff recruitment (reduced time and costs per hire) the increased use also led to glut of applications especially in major enterprises. Therefore the companies forced to find the best candidate in times of a "War for Talent" need help to find this needle in a haystack. This help could be provided by recommender systems predominately used in e-commerce to recommend products or services to customers purchasing specific products. Recommender systems could assist the recruiter to find the adequate candidate within the applicant's database. In order to support this search and selection process we conduct a design science approach to integrate recommender systems in a holistic e-recruiting architecture and therewith provide a complete and new solution for IT support in staff recruitment.