nils-herrmann/clean_scirepeval huggingface dataset:
A compact 4-chip package with 64 embedded dual-polarization antennas for W-band phased-array transceivers

A fully-integrated antenna-in-package (AiP) solution for W-band scalable phased-array systems is demonstrated. We present a fully operational compact W-band transceiver package with 64 dual-polarization antennas embedded in a multilayer organic substrate. This package has 12 metal layers, a size of 16.2 mm × 16.2 mm, and 292 ball-grid-array (BGA) pins with 0.4 mm pitch. Four silicon-germanium (SiGe) transceiver ICs are flip-chip attached to the package. Extensive full-wave electromagnetic simulation and radiation pattern measurements have been performed to optimize the antenna performance in the package environment, with excellent model-to-hardware correlation achieved. Enabled by detailed circuit-package co-design, a half-wavelength spacing, i.e., 1.6 mm at 94 GHz, is maintained between adjacent antenna elements to support array scalability at both the package and board level. Effective isotropic radiated power (EIRP) and radiation patterns are also measured to demonstrate the 64-element spatial power combining.
Bender: a virtual ribbon for deforming 3D shapes in biomedical and styling applications

In contrast to machined mechanical parts, the 3D shapes encountered in biomedical or styling applications contain many tubular parts, protrusions, engravings, embossings, folds, and smooth bends. It is difficult to design and edit such features using the parameterized operations or even free-form deformations available in CAD or animation systems. The Bender tool proposed here complements previous solutions by allowing a designer holding a 6 DoF 3D tracker in each hand to control the position and orientation of the ends of a stretchable virtual ribbon, which is used to grab the shape in its vicinity and to deform it in realtime, as the designer continues to move, bend, and twist the ribbon. To ensure realtime performance and intuitive control of the ribbon, we model its centerline as a circular biarc and perform adaptive refinement of the triangle-mesh approximation of the surface. To produce a natural and predictable warp, we use the initial and final shapes of the ribbon to define a one-parameter family of screw-motions. The deformation of a surface point is computed by finding its locally closest projection, or projections, on the biarc and by applying the corresponding screws, weighted by a function that decays with the distance to the projection. The combination of these solutions leads to an easy-to-use and effective tool for the direct manipulation of organic or stylized shapes.
Graph Convolutional Neural Networks for ADME Prediction in Drug Discovery

ADME in-silico methods have grown increasingly powerful over the past twenty years, driven by advances in machine learning and the abundance of high-quality training data generated by laboratory automation. Meanwhile, in the technology industry, deep-learning has taken o↵, driven by advances in topology design, computation, and data. The key premise of these methods is that the model is able to pass gradients back into the feature structure, engineering its own problem-specific representation for the data. Graph Convolutional Networks (GC-DNNs), a variation of neural fingerprints, allow for true deep-learning in the chemistry domain. We use this new approach to build human plasma protein binding, lipophilicty, and humans clearance models that significantly outperform random-forests and support-vector-regression.
Real-time flood monitoring and warning system

Flooding is one of the major disasters occurring in various parts of the world. The system for real-time monitoring of water conditions: water level; flow; and precipitation level, was developed to be employed in monitoring flood in Nakhon Si Thammarat, a southern province in Thailand. The two main objectives of the developed system is to serve 1) as information channel for flooding between the involved authorities and experts to enhance their responsibilities and collaboration and 2) as a web based information source for the public, responding to their need for information on water condition and flooding. The developed system is composed of three major components: sensor network, processing/transmission unit, and database/ application server. These real-time data of water condition can be monitored remotely by utilizing wireless sensors network that utilizes the mobile General Packet Radio Service (GPRS) communication in order to transmit measured data to the application server. We implemented a so-called VirtualCOM, a middleware that enables application server to communicate with the remote sensors connected to a GPRS data unit (GDU). With VirtualCOM, a GDU behaves as if it is a cable directly connected the remote sensors to the application server. The application server is a web-based system implemented using PHP and JAVA as the web application and MySQL as its relational database. Users can view real-time water condition as well as the forecasting of the water condition directly from the web via web browser or via WAP. The developed system has demonstrated the applicability of today’s sensors in wirelessly monitor real-time water conditions.
Exact special 2D spectral expression of the magnetic field in air gap with skewed source

This paper presents a method to analytically model the magnetic field in the air gap of rotating electrical machines with cylindrical air gap. The field is expressed as a sum of sinusoidal functions of the azimuthal coordinate. The originality of this paper is to give an exact field solution in case of skewed sources (windings or magnets). That result can then be used for torque, inductances and magnetic losses computations in machines with Faulhaber or rhombic windings. The computation of the inductances of a Faulhaber motor is treated as an example.
A robust data scaling algorithm to improve classification accuracies in biomedical data

Machine learning models have been adapted in biomedical research and practice for knowledge discovery and decision support. While mainstream biomedical informatics research focuses on developing more accurate models, the importance of data preprocessing draws less attention. We propose the Generalized Logistic (GL) algorithm that scales data uniformly to an appropriate interval by learning a generalized logistic function to fit the empirical cumulative distribution function of the data. The GL algorithm is simple yet effective; it is intrinsically robust to outliers, so it is particularly suitable for diagnostic/classification models in clinical/medical applications where the number of samples is usually small; it scales the data in a nonlinear fashion, which leads to potential improvement in accuracy. To evaluate the effectiveness of the proposed algorithm, we conducted experiments on 16 binary classification tasks with different variable types and cover a wide range of applications. The resultant performance in terms of area under the receiver operation characteristic curve (AUROC) and percentage of correct classification showed that models learned using data scaled by the GL algorithm outperform the ones using data scaled by the Min-max and the Z-score algorithm, which are the most commonly used data scaling algorithms. The proposed GL algorithm is simple and effective. It is robust to outliers, so no additional denoising or outlier detection step is needed in data preprocessing. Empirical results also show models learned from data scaled by the GL algorithm have higher accuracy compared to the commonly used data scaling algorithms.
Dynamic projection mapping onto a deformable object with occlusion based on high-speed tracking of dot marker array

In recent years, projection mapping has attracted much attention in a variety of fields. Generally, however, the objects in projection mapping are limited to rigid and static or quasistatic objects. Dynamic projection mapping onto a deformable object could remarkably expand the possibilities. In order to achieve such a projection mapping, it is necessary to recognize the deformation of the object even when it is occluded. However, it is still a challenging problem to achieve this task in real-time with low latency. In this paper, we propose an efficient, high-speed tracking method utilizing high-frame-rate imaging. Our method is able to track an array of dot markers arranged on a deformable object even when there is external occlusion caused by the user interaction and self-occlusion caused by the deformation of the object itself. Additionally, our method can be applied to a stretchable object. Dynamic projection mapping with our method showed robust and consistent display onto a sheet of paper and cloth with a tracking performance of about 0.2 ms per frame, with the result that the projected pattern appeared to be printed on the deformable object.
DeViSE: A Deep Visual-Semantic Embedding Model

Modern visual recognition systems are often limited in their ability to scale to large numbers of object categories. This limitation is in part due to the increasing difficulty of acquiring sufficient training data in the form of labeled images as the number of object categories grows. One remedy is to leverage data from other sources – such as text data – both to train visual models and to constrain their predictions. In this paper we present a new deep visual-semantic embedding model trained to identify visual objects using both labeled image data as well as semantic information gleaned from unannotated text. We demonstrate that this model matches state-of-the-art performance on the 1000-class ImageNet object recognition challenge while making more semantically reasonable errors, and also show that the semantic information can be exploited to make predictions about tens of thousands of image labels not observed during training. Semantic knowledge improves such zero-shot predictions by up to 65%, achieving hit rates of up to 10% across thousands of novel labels never seen by the visual model.
Self-Attentive Feature-Level Fusion for Multimodal Emotion Detection

Multimodal emotion recognition is the task of detecting emotions present in user-generated multimedia content. Such resources contain complementary information in multiple modalities. A stiff challenge often faced is the complexity associated with feature-level fusion of these heterogeneous modes. In this paper, we propose a new feature-level fusion method based on self-attention mechanism. We also compare it with traditional fusion methods such as concatenation, outer-product, etc. Analyzed using textual and speech (audio) modalities, our results suggest that the proposed fusion method outperforms others in the context of utterance-level emotion recognition in videos.
Teachers' perceptions of the barriers to technology integration and practices with technology under situated professional development

This case study examines 18 elementary school teachers’ perceptions of the barriers to technology integration (access, vision, professional development, time, and beliefs) and instructional practices with technology after two years of situated professional development. Months after transitioning from mentoring to teacher-led communities of practice, teachers continued to report positive perceptions of several barriers and were observed engaging in desirable instructional practices. Interviews suggest that the situated professional development activities helped create an environment that supported teachers’ decisions to integrate technology. Implications for teacher professional development and technology integration are discussed in conjunction with the strengths and limitations of the study.
Graded Entailment for Compositional Distributional Semantics

The categorical compositional distributional model of natural language provides a conceptually motivated procedure to compute the meaning of sentences, given grammatical structure and the meanings of its words. This approach has outperformed other models in mainstream empirical language processing tasks. However, until now it has lacked the crucial feature of lexical entailment – as do other distributional models of meaning. In this paper we solve the problem of entailment for categorical compositional distributional semantics. Taking advantage of the abstract categorical framework allows us to vary our choice of model. This enables the introduction of a notion of entailment, exploiting ideas from the categorical semantics of partial knowledge in quantum computation. The new model of language uses density matrices, on which we introduce a novel robust graded order capturing the entailment strength between concepts. This graded measure emerges from a general framework for approximate entailment, induced by any commutative monoid. Quantum logic embeds in our graded order. Our main theorem shows that entailment strength lifts compositionally to the sentence level, giving a lower bound on sentence entailment. We describe the essential properties of graded entailment such as continuity, and provide a procedure for calculating entailment strength.
Monalytics: online monitoring and analytics for managing large scale data centers

To effectively manage large-scale data centers and utility clouds, operators must understand current system and application behaviors. This requires continuous monitoring along with online analysis of the data captured by the monitoring system. As a result, there is a need to move to systems in which both tasks can be performed in an integrated fashion, thereby better able to drive online system management. Coining the term 'monalytics' to refer to the combined monitoring and analysis systems used for managing large-scale data center systems, this paper articulates principles for monalytics systems, describes software approaches for implementing them, and provides experimental evaluations justifying principles and implementation approach. Specific technical contributions include consideration of scalability across both 'space' and 'time', the ability to dynamically deploy and adjust monalytics functionality at multiple levels of abstraction in target systems, and the capability to operate across the range of application to hypervisor layers present in large-scale data center or cloud computing systems. Our monalytics implementation targets virtualized systems and cloud infrastructures, via the integration of its functionality into the Xen hypervisor.
SoundNet: investigating a language composed of environmental sounds

Auditory displays have been used in both human-machine and computer interfaces. However, the use of non-speech audio in assistive communication for people with language disabilities, or in other applications that employ visual representations, is still under-investigated. In this paper, we introduce SoundNet, a linguistic database that associates natural environmental sounds with words and concepts. A sound labeling study was carried out to verify SoundNet associations and to investigate how well the sounds evoke concepts. A second study was conducted using the verified SoundNet data to explore the power of environmental sounds to convey concepts in sentence contexts, compared with conventional icons and animations. Our results show that sounds can effectively illustrate (especially concrete) concepts and can be applied to assistive interfaces.
Linear codes over F2+uF2+vF2+uvF2

In this work, we investigate linear codes over the ring F2 + uF2 + vF2 + uvF2. We first analyze the structure of the ring and then define linear codes over this ring which turns out to be a ring that is not finite chain or principal ideal contrary to the rings that have hitherto been studied in coding theory. Lee weights and Gray maps for these codes are defined by extending on those introduced in works such as Betsumiya et al. ( 2004) and Dougherty et al. ( 1999). We then characterize the F2 +uF2 +vF2 +uvF2-linearity of binary codes under the Gray map and give a main class of binary codes as an example of F2 + uF2 + vF2 + uvF2-linear codes. The duals and the complete weight enumerators for F2 + uF2 + vF2 + uvF2-linear codes are also defined after which MacWilliams-like identities for complete and Lee weight enumerators as well as for the ideal decompositions of linear codes over F2 + uF2 + vF2 + uvF2 are obtained.
Semi-supervised Sequence Learning

We present two approaches that use unlabeled data to improve sequ nce learning with recurrent networks. The first approach is to predict wha t comes next in a sequence, which is a conventional language model in natural language processing. The second approach is to use a sequence autoencoder, which r eads the input sequence into a vector and predicts the input sequence again. T hese two algorithms can be used as a “pretraining” step for a later supervised seq uence learning algorithm. In other words, the parameters obtained from the unsu pervised step can be used as a starting point for other supervised training model s. In our experiments, we find that long short term memory recurrent networks after b eing pretrained with the two approaches are more stable and generalize bette r. With pretraining, we are able to train long short term memory recurrent network s up to a few hundred timesteps, thereby achieving strong performance in ma ny text classification tasks, such as IMDB, DBpedia and 20 Newsgroups.
Prediction of Microwave Absorption Behavior of Grading Honeycomb Composites Based on Effective Permittivity Formulas

Microwave absorbing honeycomb composites with grading coating thickness are prepared by impregnation of aramid paper frame within water solutions of conductive carbon blacks. Based on effective medium method, grading impedance in the composites can be theoretically expressed by a linear approximation of the inversely tapered effective permittivity at wave propagation direction, which provides a feasible way to optimize the design of microwave absorbing honeycomb structures. The consistent result of calculations and experimental measurements for positive impedance grading design confirms the broadband absorption effect in high frequencies.
Design and implementation of FROST: Digital forensic tools for the OpenStack cloud computing platform

We describe the design, implementation, and evaluation of FROSTdthree new forensic tools for the OpenStack cloud platform. Our implementation for the OpenStack cloud platform supports an Infrastructure-as-a-Service (IaaS) cloud and provides trustworthy forensic acquisition of virtual disks, API logs, and guest firewall logs. Unlike traditional acquisition tools, FROST works at the cloud management plane rather than interacting with the operating system inside the guest virtual machines, thereby requiring no trust in the guest machine. We assume trust in the cloud provider, but FROST overcomes nontrivial challenges of remote evidence integrity by storing log data in hash trees and returning evidence with cryptographic hashes. Our tools are user-driven, allowing customers, forensic examiners, and law enforcement to conduct investigations without necessitating interaction with the cloud provider. We demonstrate how FROST’s new features enable forensic investigators to obtain forensically-sound data from OpenStack clouds independent of provider interaction. Our preliminary evaluation indicates the ability of our approach to scale in a dynamic cloud environment. The design supports an extensible set of forensic objectives, including the future addition of other data preservation, discovery, real-time monitoring, metrics, auditing, and acquisition capabilities. a 2013 Josiah Dykstra and Alan T. Sherman.
Development of an exoskeleton haptic interface for virtual task training

An exoskeleton haptic interface is developed for functional training in virtual environments. A composite control scheme enables a variety of tasks to be implemented, and a “Qt” graphics library is used to generate the virtual environment for the haptic interface at the hand and graphical user interfaces for input and telemetry. Inter-process communications convert telemetry from the exoskeleton into motion commands for objects in the virtual environment. A second haptic interface at the upper arm is used to control the elbow orbit self-motion of the arm during tasks. Preliminary results are reviewed for a wall-painting task in which the virtual wall stiffness and viscosity are generated using an admittance controller.
Kernel-Based Approximate Dynamic Programming for Real-Time Online Learning Control: An Experimental Study

In the past decade, there has been considerable research interest in learning control methods based on reinforcement learning (RL) and approximate dynamic programming (ADP). As an important class of function approximation techniques, kernel methods have been recently applied to improve the generalization ability of RL and ADP methods but most previous works were only based on simulation. This paper focuses on experimental studies of real-time online learning control for nonlinear systems using kernel-based ADP methods. Specifically, the kernel-based dual heuristic programming (KDHP) method is applied and tested on real-time control systems. Two kernel-based online learning control schemes are presented for uncertain nonlinear systems by using simulation data and online sampling data, respectively. Learning control experiments were performed on a single-link inverted pendulum system as well as a double-link inverted pendulum system. From the experimental results, it is shown that both online learning control schemes, either using simulation data or using real sampling data, are effective for approximating near-optimal control policies of nonlinear dynamical systems with model uncertainties. In addition, it is demonstrated that KDHP can achieve better performance than conventional DHP, which uses multilayer perceptron neural networks.
A Secure Sharding Protocol For Open Blockchains

Cryptocurrencies, such as Bitcoin and 250 similar alt-coins, embody at their core a blockchain protocol --- a mechanism for a distributed network of computational nodes to periodically agree on a set of new transactions. Designing a secure blockchain protocol relies on an open challenge in security, that of designing a highly-scalable agreement protocol open to manipulation by byzantine or arbitrarily malicious nodes. Bitcoin's blockchain agreement protocol exhibits security, but does not scale: it processes 3--7 transactions per second at present, irrespective of the available computation capacity at hand.
 In this paper, we propose a new distributed agreement protocol for permission-less blockchains called ELASTICO. ELASTICO scales transaction rates almost linearly with available computation for mining: the more the computation power in the network, the higher the number of transaction blocks selected per unit time. ELASTICO is efficient in its network messages and tolerates byzantine adversaries of up to one-fourth of the total computational power. Technically, ELASTICO uniformly partitions or parallelizes the mining network (securely) into smaller committees, each of which processes a disjoint set of transactions (or "shards"). While sharding is common in non-byzantine settings, ELASTICO is the first candidate for a secure sharding protocol with presence of byzantine adversaries. Our scalability experiments on Amazon EC2 with up to $1, 600$ nodes confirm ELASTICO's theoretical scaling properties.