nils-herrmann/clean_scirepeval huggingface dataset:
Zero-shot Image Tagging by Hierarchical Semantic Embedding

Given the difficulty of acquiring labeled examples for many fine-grained visual classes, there is an increasing interest in zero-shot image tagging, aiming to tag images with novel labels that have no training examples present. Using a semantic space trained by a neural language model, the current state-of-the-art embeds both images and labels into the space, wherein cross-media similarity is computed. However, for labels of relatively low occurrence, its similarity to images and other labels can be unreliable. This paper proposes Hierarchical Semantic Embedding (HierSE), a simple model that exploits the WordNet hierarchy to improve label embedding and consequently image embedding. Moreover, we identify two good tricks, namely training the neural language model using Flickr tags instead of web documents, and using partial match instead of full match for vectorizing a WordNet node. All this lets us outperform the state-of-the-art. On a test set of over 1,500 visual object classes and 1.3 million images, the proposed model beats the current best results (18.3% versus 9.4% in hit@1).
Communication challenges in emergency response

primary challenge in responding to both natural and man-made disasters is communication. This has been highlighted by recent disasters such as the 9/11 terrorist attacks and Hurricane Katrina [2, 5, 6]. A problem frequently cited by responders is the lack of radio interoperability. Responding organizations must work in concert to form a cohesive plan of response. However, each group—fire, police, SWAT, HazMat—com-municates with radios set to orthogonal frequencies , making inter-agency communications extremely difficult. The problem is compounded as more local, state, and federal agencies become involved. The communication challenges in emergency response go far beyond simple interop-erability issues. Based on our research, practical observation of first responder exercises and drills, and workshop discussions, we have identified three categories of communication challenges: technological, sociological, and organizational. These three major areas are key to developing and maintaining healthy and effective disaster communication systems. The primary technological challenge after a disaster is rapid deployment of communication systems for first responders and disaster management workers. This is true regardless of whether the communications network has been completely destroyed (power, telephone, and/or network connectivity infrastructure), or, as in the case of some remote geographic areas, the infrastructure was previously nonex-istent. Deployment of a new system is more complicated in areas where partial communication infrastructures remain, than where no prior communication networks existed. This can be due to several factors including interference from existing partial communication networks and the dependency of people on their prior systems. Another important obstacle to overcome is the multi-organizational radio interoperability issue. To make future communication systems capable of withstanding large-or medium-scale disasters, two technological solutions can be incorporated into the design: dual-use technology and built-in architectural and protocol redundancy. Dual-use technology would enable both normal and emergency operational modes. During crises, such devices would work in a network-controlled fashion, achieved using software agents within the communication
Poisson Noise Reduction with Higher-order Natural Image Prior Model

Poisson denoising is an essential issue for various imaging applications, such as night vision, medical imaging and microscopy. State-of-the-art approaches are clearly dominated by patch-based non-local methods in recent years. In this paper, we aim to propose a local Poisson denoising model with both structure simplicity and good performance. To this end, we consider a variational modeling to integrate the so-called Fields of Experts (FoE) image prior, that has proven an effective higher-order Markov Random Fields (MRF) model for many classic image restoration problems. We exploit several feasible variational variants for this task. We start with a direct modeling in the original image domain by taking into account the Poisson noise statistics, which performs generally well for the cases of high SNR. However, this strategy encounters problem in cases of low SNR. Then we turn to an alternative modeling strategy by using the Anscombe transform and Gaussian statistics derived data term. We retrain the FoE prior model directly in the transform domain. With the newly trained FoE model, we end up with a local variational model providing strongly competitive results against state-of-the-art non-local approaches, meanwhile bearing the property of simple structure. Furthermore, our proposed model comes along with an additional advantage, that the inference is very efficient as it is well-suited for parallel computation on GPUs. For images of size 512× 512, our GPU implementation takes less ∗Corresponding author.  2016 ar X iv :1 60 9. 05 72 2v 1 [ cs .C V ] 1 9 Se p 20 16 than 1 second to produce state-of-the-art Poisson denoising performance.
Automatic Database Management System Tuning Through Large-scale Machine Learning

Database management system (DBMS) configuration tuning is an essential aspect of any data-intensive application effort. But this is historically a difficult task because DBMSs have hundreds of configuration "knobs" that control everything in the system, such as the amount of memory to use for caches and how often data is written to storage. The problem with these knobs is that they are not standardized (i.e., two DBMSs use a different name for the same knob), not independent (i.e., changing one knob can impact others), and not universal (i.e., what works for one application may be sub-optimal for another). Worse, information about the effects of the knobs typically comes only from (expensive) experience.
 To overcome these challenges, we present an automated approach that leverages past experience and collects new information to tune DBMS configurations: we use a combination of supervised and unsupervised machine learning methods to (1) select the most impactful knobs, (2) map unseen database workloads to previous workloads from which we can transfer experience, and (3) recommend knob settings. We implemented our techniques in a new tool called OtterTune and tested it on two DBMSs. Our evaluation shows that OtterTune recommends configurations that are as good as or better than ones generated by existing tools or a human expert.
100 W Operation of a Cold Cathode TWT

Recent demonstration of low-voltage high-transconductance field emitter array operation holds promise for the successful development of reliable cold cathode vacuum electronics device technologies. This paper reports on the experimental studies of implementation of such field emitter arrays as the electron source for a moderate power traveling wave tube (TWT) operating in the C-band frequency regime. The cold cathode TWT has operated for over 150 h at duty factors up to 10%, beam currents up to 121 mA, and RF powers up to 100 W at 5 GHz. High cathode current densities of 15.4 A/cm2 were achieved concurrent with excellent beam control, resulting in 99.4% beam transmission under zero-RF-drive operating conditions and 97.3% transmission at maximum RF output power. The cathode is shown to operate with a 72% reduction in the operating voltage from the previous generation of emitters fabricated by SRI International, bringing the operating voltage for full current operation well below 100 V. Extensive device characterization and life testing has been performed, and interesting variation in cathode performance was observed during the high-duty high-current portion of the testing program. The results presented here represent the highest current, highest power, and highest duty factor ever reported for an RF vacuum device employing a field emission cold cathode electron source.
Vision-and-Language Navigation: Interpreting Visually-Grounded Navigation Instructions in Real Environments

A robot that can carry out a natural-language instruction has been a dream since before the Jetsons cartoon series imagined a life of leisure mediated by a fleet of attentive robot helpers. It is a dream that remains stubbornly distant. However, recent advances in vision and language methods have made incredible progress in closely related areas. This is significant because a robot interpreting a natural-language navigation instruction on the basis of what it sees is carrying out a vision and language process that is similar to Visual Question Answering. Both tasks can be interpreted as visually grounded sequence-to-sequence translation problems, and many of the same methods are applicable. To enable and encourage the application of vision and language methods to the problem of interpreting visually-grounded navigation instructions, we present the Matter-port3D Simulator - a large-scale reinforcement learning environment based on real imagery [11]. Using this simulator, which can in future support a range of embodied vision and language tasks, we provide the first benchmark dataset for visually-grounded natural language navigation in real buildings - the Room-to-Room (R2R) dataset1.
Wideband butler matrix in microstrip-slot technology

The design of a 4 × 4 Butler Matrix for use in a multi-beam antenna array operating over wide frequency band is presented. In order to achieve wideband characteristics, the matrix uses microstrip-slot technology that employs two microstrip dielectric layers with a common ground plane and a coupling slot. The designed beamforming network is compact in size and does not use crossovers. Its prototype is manufactured. The performance of the designed device is assessed via full-wave electromagnetic simulations and measurements.
Data, information, knowledge and principle: back to metabolism in KEGG

In the hierarchy of data, information and knowledge, computational methods play a major role in the initial processing of data to extract information, but they alone become less effective to compile knowledge from information. The Kyoto Encyclopedia of Genes and Genomes (KEGG) resource (http://www.kegg.jp/ or http://www.genome.jp/kegg/) has been developed as a reference knowledge base to assist this latter process. In particular, the KEGG pathway maps are widely used for biological interpretation of genome sequences and other high-throughput data. The link from genomes to pathways is made through the KEGG Orthology system, a collection of manually defined ortholog groups identified by K numbers. To better automate this interpretation process the KEGG modules defined by Boolean expressions of K numbers have been expanded and improved. Once genes in a genome are annotated with K numbers, the KEGG modules can be computationally evaluated revealing metabolic capacities and other phenotypic features. The reaction modules, which represent chemical units of reactions, have been used to analyze design principles of metabolic networks and also to improve the definition of K numbers and associated annotations. For translational bioinformatics, the KEGG MEDICUS resource has been developed by integrating drug labels (package inserts) used in society.
A 4.8 GHz RF CMOS wideband LC VCO for wireless sensor networks RFICs

A 4.8GHz LC voltage controlled oscillator (VCO) for Wireless Sensor Network (WSN) SoC RFIC chipset is designed based on SMIC 0.18 μm 1P6M RF CMOS process. The core circuit adopts complementary differential negative resistance structure with resistor biasing which achieves good phase noise performance. The 2 bit switched capacitor array provides extra tuning range. The chip size is 600μm×475μm with testing pads. With a 1.8V supply voltage, the post-simulation and chipset measured results show that the achieved maximum 40% tuning range can perfectly compensating the deviation due to process corners. And the measured phase noise is −96dBc/Hz@3MHz with the carrier be 4.8GHz. Besides, the operating current of the whole circuit is less than 7mA.
Not As Smart As We Think: A Study of Collective Intelligence in Virtual Groups

Cross-disciplinary research on collective intelligence considers that groups, like individuals, have a certain level of intelligence. For example, the study by Woolley et al. (2010) indicates that groups which perform well on one type of task will perform well on others. In a pair of empirical studies of groups interacting face-to-face, they found evidence of a collective intelligence factor, a measure of consistent group performance across a series of tasks, which was highly predictive of performance on a subsequent, more complex task. This collective intelligence factor differed from the individual intelligence of group members, and was significantly predicted by members’ social sensitivity – the ability to understand the emotions of others based on visual facial cues (Baron-Cohen et al. 2001).
Ten years of a model of aesthetic appreciation and aesthetic judgments : The aesthetic episode - Developments and challenges in empirical aesthetics.

About a decade ago, psychology of the arts started to gain momentum owing to a number of drives: technological progress improved the conditions under which art could be studied in the laboratory, neuroscience discovered the arts as an area of interest, and new theories offered a more comprehensive look at aesthetic experiences. Ten years ago, Leder, Belke, Oeberst, and Augustin (2004) proposed a descriptive information-processing model of the components that integrate an aesthetic episode. This theory offered explanations for modern art's large number of individualized styles, innovativeness, and for the diverse aesthetic experiences it can stimulate. In addition, it described how information is processed over the time course of an aesthetic episode, within and over perceptual, cognitive and emotional components. Here, we review the current state of the model, and its relation to the major topics in empirical aesthetics today, including the nature of aesthetic emotions, the role of context, and the neural and evolutionary foundations of art and aesthetics.
Reach and grasp by people with tetraplegia using a neurally controlled robotic arm

Paralysis following spinal cord injury, brainstem stroke, amyotrophic lateral sclerosis and other disorders can disconnect the brain from the body, eliminating the ability to perform volitional movements. A neural interface system could restore mobility and independence for people with paralysis by translating neuronal activity directly into control signals for assistive devices. We have previously shown that people with long-standing tetraplegia can use a neural interface system to move and click a computer cursor and to control physical devices. Able-bodied monkeys have used a neural interface system to control a robotic arm, but it is unknown whether people with profound upper extremity paralysis or limb loss could use cortical neuronal ensemble signals to direct useful arm actions. Here we demonstrate the ability of two people with long-standing tetraplegia to use neural interface system-based control of a robotic arm to perform three-dimensional reach and grasp movements. Participants controlled the arm and hand over a broad space without explicit training, using signals decoded from a small, local population of motor cortex (MI) neurons recorded from a 96-channel microelectrode array. One of the study participants, implanted with the sensor 5 years earlier, also used a robotic arm to drink coffee from a bottle. Although robotic reach and grasp actions were not as fast or accurate as those of an able-bodied person, our results demonstrate the feasibility for people with tetraplegia, years after injury to the central nervous system, to recreate useful multidimensional control of complex devices directly from a small sample of neural signals.
Investigations on the tensile force at the multi-wire needle winding process

The electric motor is the main component in an electrical vehicle. Its power density is directly influenced by the winding. For this reason, it is relevant to investigate the influences of coil production on the quality of the stator. The examined stator in this article is wound with the multi-wire needle winding technique. With this method, the placing of the wires can be precisely guided leading to small winding heads. To gain a high winding quality with small winding resistances, the control of the tensile force during the winding process is essential. The influence of the tensile force on the winding resistance during the winding process with the multiple needle winding technique will be presented here. To control the tensile force during the winding process, the stress on the wire during the winding process needs to be examined first. Thus a model will be presented to investigate the tensile force which realizes a coupling between the multibody dynamics simulation and the finite element methods with the software COMSOL Multiphysics®. With the results of the simulation, a new winding-trajectory based wire tension control can be implemented. Therefore, new strategies to control the tensile force during the process using a CAD/CAM approach will be presented in this paper.
Segmentation of the Proximal Femur from MR Images using Deep Convolutional Neural Networks

Magnetic resonance imaging (MRI) has been proposed as a complimentary method to measure bone quality and assess fracture risk. However, manual segmentation of MR images of bone is time-consuming, limiting the use of MRI measurements in the clinical practice. The purpose of this paper is to present an automatic proximal femur segmentation method that is based on deep convolutional neural networks (CNNs). This study had institutional review board approval and written informed consent was obtained from all subjects. A dataset of volumetric structural MR images of the proximal femur from 86 subjects were manually-segmented by an expert. We performed experiments by training two different CNN architectures with multiple number of initial feature maps, layers and dilation rates, and tested their segmentation performance against the gold standard of manual segmentations using four-fold cross-validation. Automatic segmentation of the proximal femur using CNNs achieved a high dice similarity score of 0.95 ± 0.02 with precision = 0.95 ± 0.02, and recall = 0.95 ± 0.03. The high segmentation accuracy provided by CNNs has the potential to help bring the use of structural MRI measurements of bone quality into clinical practice for management of osteoporosis.
Training convolutional neural networks with megapixel images

To train deep convolutional neural networks, the input data and the intermediate activations need to be kept in memory to calculate the gradient descent step. Given the limited memory available in the current generation accelerator cards, this limits the maximum dimensions of the input data. We demonstrate a method to train convolutional neural networks holding only parts of the image in memory while giving equivalent results. We quantitatively compare this new way of training convolutional neural networks with conventional training. In addition, as a proof of concept, we train a convolutional neural network with 64 megapixel images, which requires 97% less memory than the conventional approach.
Dank Learning: Generating Memes Using Deep Neural Networks

We introduce a novel meme generation system, which given any image can produce a humorous and relevant caption. Furthermore, the system can be conditioned on not only an image but also a user-defined label relating to the meme template, giving a handle to the user on meme content. The system uses a pretrained Inception-v3 network to return an image embedding which is passed to an attention-based deep-layer LSTM model producing the caption inspired by the widely recognized Show and Tell Model. We implement a modified beam search to encourage diversity in the captions. We evaluate the quality of our model using perplexity and human assessment on both the quality of memes generated and whether they can be differentiated from real ones. Our model produces original memes that cannot on the whole be differentiated from real ones.
FACE: Automated digital evidence discovery and correlation

Digital forensic tools are being developed at a brisk pace in response to the ever increasing variety of forensic targets. Most tools are created for specific tasks – filesystem analysis, memory analysis, network analysis, etc. – and make little effort to interoperate with one another. This makes it difficult and extremely time-consuming for an investigator to build a wider view of the state of the system under investigation. In this work, we present FACE, a framework for automatic evidence discovery and correlation from a variety of forensic targets. Our prototype implementation demonstrates the integrated analysis and correlation of a disk image, memory image, network capture, and configuration log files. The results of this analysis are presented as a coherent view of the state of a target system, allowing investigators to quickly understand it. We also present an advanced open-source memory analysis tool, ramparser, for the automated analysis of Linux systems. a 2008 Digital Forensic Research Workshop.
How do designers and user experience professionals actually perceive and use personas?

Personas are a critical method for orienting design and development teams to user experience. Prior work has noted challenges in justifying them to developers. In contrast, it has been assumed that designers and user experience professionals - whose goal is to focus designs on targeted users - will readily exploit personas. This paper examines that assumption. We present the first study of how experienced user-centered design (UCD) practitioners with prior experience deploying personas, use and perceive personas in industrial software design. We identify limits to the persona approach in the context studied. Practitioners used personas almost exclusively for communication, but not for design. Participants identified four problems with personas, finding them abstract, impersonal, misleading and distracting. Our findings argue for a new approach to persona deployment and construction. Personas cannot replace immersion in actual user data. And rather than focusing on creating engaging personas, it is critical to avoid persona attributes that mislead or distract.
Leader personality traits and employee voice behavior: mediating roles of ethical leadership and work group psychological safety.

The antecedents and consequences of ethical leadership were examined in a study of 894 employees and their 222 immediate supervisors in a major financial institution in the United States. The leader personality traits of agreeableness and conscientiousness were positively related to direct reports' ratings of the leader's ethical leadership, whereas neuroticism was unrelated to these ratings. Ethical leadership influenced followers' voice behavior as rated by followers' immediate supervisors. This relationship was partially mediated by followers' perceptions of psychological safety. Implications for research on ethical leadership and means to enhance ethical behavior among leaders and nonleaders are discussed.