nils-herrmann/clean_scirepeval huggingface dataset:
An X-band SiGe LNA with 1.36 dB mean noise figure for monolithic phased array transmit/receive radar modules

This paper presents an X-band silicon-germanium (SiGe) low-noise amplifier (LNA) for a monolithically integrated phased array transmit/receive (T/R) radar module. Implemented in a 200 GHz SiGe BiCMOS technology, the LNA occupies 730 times 720 mum2 (including bondpads), and dissipates 15 mW from a 2.5 V power supply. The circuit exhibits a gain greater than 19 dB from 8.5 to 10.5 GHz, and a mean noise figure (NF) of 1.36 dB across X-band. At 10 GHz, the input 1-dB compression point (IP1-dB) and the input third-order intercept point (IIP3) are -10.0 dBm and 0.8 dBm, respectively. To our knowledge, this LNA achieves the lowest noise figure of any LNA in Si-based technology at X-band
Generating Natural Adversarial Examples

Due to their complex nature, it is hard to characterize the ways in which machine learning models can misbehave or be exploited when deployed. Recent work on adversarial examples, i.e. inputs with minor perturbations that result in substantially different model predictions, is helpful in evaluating the robustness of these models by exposing the adversarial scenarios where they fail. However, these malicious perturbations are often unnatural, not semantically meaningful, and not applicable to complicated domains such as language. In this paper, we propose a framework to generate natural and legible adversarial examples that lie on the data manifold, by searching in semantic space of dense and continuous data representation, utilizing the recent advances in generative adversarial networks. We present generated adversaries to demonstrate the potential of the proposed approach for black-box classifiers for a wide range of applications such as image classification, textual entailment, and machine translation. We include experiments to show that the generated adversaries are natural, legible to humans, and useful in evaluating and analyzing black-box classifiers.
BOUNDARY DETECTION FROM BOOKSHELF IMAGE BASED ON MODEL FITTING

Several systems based on bookshelf image analysis have been studied for automating bookshelf inspection in libraries and bookstores. In conventional systems, book boundaries are firstly detected for extracting each individual book by using some popular line detection technique, such as Hough transformation. Their detection accuracies, however, are sometimes insufficient. In this paper, we propose a model-based book boundary detection technique for higher detection accuracy. The model is represented as a finite state automaton, each of whose states corresponds to a component of bookshelves, such as boundary and title. With this model, the book boundary detection problem is formulated as a model-based optimization problem where states and local slant angles at all horizontal positions were variables to be optimized. The globally optimal solution is efficiently searched for using a dynamic programming based algorithm. The effectiveness of the proposed technique was shown by several experiments.
Ultra-Wideband Crossover Using Microstrip-to-Coplanar Waveguide Transitions

The design of an ultra-wideband crossover for use in printed microwave circuits is presented. It employs a pair of broadside-coupled microstrip-to-coplanar waveguide (CPW) transitions, and a pair of uniplanar microstrip-to-CPW transitions. A lumped-element equivalent circuit is used to explain the operation of the proposed crossover. Its performance is evaluated via full-wave electromagnetic simulations and measurements. The designed device is constructed on a single substrate, and thus, it is fully compatible with microstrip-based microwave circuits. The crossover is shown to operate across the frequency band from 3.1 to 11 GHz with more than 15 dB of isolation, less than 1 dB of insertion loss, and less than 0.1 ns of deviation in the group delay.
Investigating effects of magnetizing inductance on coupled-inductor voltage regulators

Coupled inductors are an emerging topology for computing power supplies as next generation CPU requirements for high efficiency and fast transient response pose great challenge on voltage regulator design. This paper addresses the effects of magnetizing inductance on current-balancing, dynamic response, and current-sensing of multiphase voltage regulator with coupled inductors. State Space Averaging and other mathematical analysis methods are employed. Experimental results are provided to verify the analysis.
Limitations of Co-Training for Natural Language Learning from Large Datasets

Co-Training is a weakly supervised learning paradigm in which the redundancy of the learning task is captured by training two classifiers using separate views of the same data. This enables bootstrapping from a small set of labeled training data via a large set of unlabeled data. This study examines the learning behavior of co-training on natural language processing tasks that typically require large numbers of training instances to achieve usable performance levels. Using base noun phrase bracketing as a case study, we find that co-training reduces by 36% the difference in error between co-trained classifiers and fully supervised classifiers trained on a labeled version of all available data. However, degradation in the quality of the bootstrapped data arises as an obstacle to further improvement. To address this, we propose a moderately supervised variant of cotraining in which a human corrects the mistakes made during automatic labeling. Our analysis suggests that corrected co-training and similar moderately supervised methods may help cotraining scale to large natural language learning tasks.
MEDUSA: MEtamorphic malware dynamic analysis usingsignature from API

Malware detection and prevention methods are increasingly becoming necessary for computer systems connected to the Internet. The traditional signature based detection of malware fails for metamorphic malware which changes its code structurally while maintaining functionality at time of propagation. This category of malware is called metamorphic malware. In this paper we dynamically analyze the executables produced from various metamorphic generators through an emulator by tracing API calls. A signature is generated for an entire malware class (each class representing a family of viruses generated from one metamorphic generator) instead of for individual malware sample. We show that most of the metamorphic viruses of same family are detected by the same base signature. Once a base signature for a particular metamorphic generator is generated, all the metamorphic viruses created from that tool are easily detected by the proposed method. A Proximity Index between the various Metamorphic generators has been proposed to determine how similar two or more generators are.
Reward-Motivated Learning: Mesolimbic Activation Precedes Memory Formation

We examined anticipatory mechanisms of reward-motivated memory formation using event-related FMRI. In a monetary incentive encoding task, cues signaled high- or low-value reward for memorizing an upcoming scene. When tested 24 hr postscan, subjects were significantly more likely to remember scenes that followed cues for high-value rather than low-value reward. A monetary incentive delay task independently localized regions responsive to reward anticipation. In the encoding task, high-reward cues preceding remembered but not forgotten scenes activated the ventral tegmental area, nucleus accumbens, and hippocampus. Across subjects, greater activation in these regions predicted superior memory performance. Within subject, increased correlation between the hippocampus and ventral tegmental area was associated with enhanced long-term memory for the subsequent scene. These findings demonstrate that brain activation preceding stimulus encoding can predict declarative memory formation. The findings are consistent with the hypothesis that reward motivation promotes memory formation via dopamine release in the hippocampus prior to learning.
Performance Analysis of Authentication Protocols in Vehicular Ad Hoc Networks (VANET)

Traditionally traffic safety was addressed by traffic awareness and passive safety measures like solid chassis, seat belts, air bags etc. With the recent breakthroughs in the domain of mobile ad hoc networks, the concept of vehicular ad hoc networks (VANET) was realised. Safety messaging is the most important aspect of VANETs, where the passive safety (accident readiness) in vehicles was reinforced with the idea of active safety (accident prevention). In safety messaging vehicles will message each other over wireless media, updating each other on traffic conditions and hazards. Security is an important aspect of safety messaging, that aims to prevent participants spreading wrong information in the network that are likely to cause mishaps. Equally important is the fact that secure communication protocols should satisfy the communication constraints of VANETs. VANETs are delay intolerant. Features like high speeds, large network size, constant mobility etc. induce certain limitations in the way messaging can be carried out in VANETs. This thesis studies the impact of total message size on VANET messaging system performance, and conducts an analysis of secure communication protocols to measure how they perform in a VANET messaging system.
Combination of Recurrent Neural Networks and Factored Language Models for Code-Switching Language Modeling

In this paper, we investigate the application of recurrent neural network language models (RNNLM) and factored language models (FLM) to the task of language modeling for Code-Switching speech. We present a way to integrate partof-speech tags (POS) and language information (LID) into these models which leads to significant improvements in terms of perplexity. Furthermore, a comparison between RNNLMs and FLMs and a detailed analysis of perplexities on the different backoff levels are performed. Finally, we show that recurrent neural networks and factored language models can be combined using linear interpolation to achieve the best performance. The final combined language model provides 37.8% relative improvement in terms of perplexity on the SEAME development set and a relative improvement of 32.7% on the evaluation set compared to the traditional n-gram language model.
NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing

Semantic hashing has become a powerful paradigm for fast similarity search in many information retrieval systems. While fairly successful, previous techniques generally require two-stage training, and the binary constraints are handled ad-hoc. In this paper, we present an end-to-end Neural Architecture for Semantic Hashing (NASH), where the binary hashing codes are treated as Bernoulli latent variables. A neural variational inference framework is proposed for training, where gradients are directly backpropagated through the discrete latent variable to optimize the hash function. We also draw connections between proposed method and rate-distortion theory, which provides a theoretical foundation for the effectiveness of the proposed framework. Experimental results on three public datasets demonstrate that our method significantly outperforms several state-of-the-art models on both unsupervised and supervised scenarios.
A Planar Magic-T Structure Using Substrate Integrated Circuits Concept and Its Mixer Applications

In this paper, a planar 180 phase-reversal T-junction and a modified magic-T using substrate integrated waveguide (SIW) and slotline are proposed and developed for RF/microwave applications on the basis of the substrate integrated circuits concept. In this case, slotline is used to generate the odd-symmetric field pattern of the SIW in the phase-reverse T-junction. Measured results indicate that 0.3-dB amplitude imbalance and 3 phase imbalance can be achieved for the proposed 180 phase-reversal T-junction over the entire -band. The modified narrowband and optimized wideband magic-T are developed and fabricated, respectively. Measured results of all those circuits agree well with their simulated ones. Finally, as an application demonstration of our proposed magic-T, a singly balanced mixer based on this structure is designed and measured with good performances.
Language Encodes Geographical Information

Population counts and longitude and latitude coordinates were estimated for the 50 largest cities in the United States by computational linguistic techniques and by human participants. The mathematical technique Latent Semantic Analysis applied to newspaper texts produced similarity ratings between the 50 cities that allowed for a multidimensional scaling (MDS) of these cities. MDS coordinates correlated with the actual longitude and latitude of these cities, showing that cities that are located together share similar semantic contexts. This finding was replicated using a first-order co-occurrence algorithm. The computational estimates of geographical location as well as population were akin to human estimates. These findings show that language encodes geographical information that language users in turn may use in their understanding of language and the world.
From the Virtual to the RealWorld: Referring to Objects in Real-World Spatial Scenes

Predicting the success of referring expressions (RE) is vital for real-world applications such as navigation systems. Traditionally, research has focused on studying Referring Expression Generation (REG) in virtual, controlled environments. In this paper, we describe a novel study of spatial references from real scenes rather than virtual. First, we investigate how humans describe objects in open, uncontrolled scenarios and compare our findings to those reported in virtual environments. We show that REs in real-world scenarios differ significantly to those in virtual worlds. Second, we propose a novel approach to quantifying image complexity when complete annotations are not present (e.g. due to poor object recognition capabitlities), and third, we present a model for success prediction of REs for objects in real scenes. Finally, we discuss implications for Natural Language Generation (NLG) systems and future directions.
Long-term Outcome After Surgical Treatment of Lipedema.

Lipedema is a condition characterized by abnormal deposition of adipose tissue in the lower extremities leading to circumferential bilateral lower extremity enlargement typically seen extending from the hips to the ankles. Diagnosis of the condition is often challenging, and patients frequently undergo a variety of unsuccessful therapies before receiving the proper diagnosis and appropriate management. Patients may experience pain and aching in the lower extremity in addition to distress from the cosmetic appearance of their legs and the resistance of the fatty changes to diet and exercise. We report a case of a patient with lipedema who was treated with suction-assisted lipectomy and use of compression garments, with successful treatment of the lipodystrophy and maintenance of improved aesthetic results at 4-year postoperative follow-up.
Experiments with Computational Creativity

Neurocognitive model inspired by the putative processes in the brain has been applied to invention of novel words. This domain is proposed as the simplest way to understand creativity using experimental and computational means. Three factors are essential for creativity in this domain: knowledge of the statistical language properties, imagination constrained by this knowledge, and filtering of results that selects most interesting novel words. These principles are implemented using a simple correlation-based algorithm for auto-associative memory that learns the statistical properties of language. Results are surprisingly similar to those created by humans. Perspectives on computational models of creativity are discussed. Creativity, brain, language processing, higher cognitive functions, neural modeling.
KinectFusion: real-time dynamic 3D surface reconstruction and interaction

We present KinectFusion, a system that takes live depth data from a moving Kinect camera and in real-time creates high-quality, geometrically accurate, 3D models. Our system allows a user holding a Kinect camera to move quickly within any indoor space, and rapidly scan and create a fused 3D model of the whole room and its contents within seconds. Even small motions, caused for example by camera shake, lead to new viewpoints of the scene and thus refinements of the 3D model, similar to the effect of image super-resolution. As the camera is moved closer to objects in the scene more detail can be added to the acquired 3D model.
Learning to Transduce with Unbounded Memory

Where memory-enhanced models converge to perfect accuracy (most tasks), perfect accuracy is maintained on the test set (longer sequences). Memory-enhanced models converge order(s) of magnitude before LSTMs. Memory-enhanced models require relatively little data to generalise properly, vs. LSTMs, which require of the order of 1e6 minibatches of size 1000, i.e. millions of training examples, to converge on near-perfect accuracy.
The memory effect and its implications on Web QoE modeling

Quality of Experience (QoE) has gained enormous attention during the recent years. So far, most of the existing QoE research has focused on audio and video streaming applications, although HTTP traffic carries the majority of traffic in the residential broadband Internet. However, existing QoE models for this domain do not consider temporal dynamics or historical experiences of the user's satisfaction while consuming a certain service. This psychological influence factor of past experience is referred to as the memory effect. The first contribution of this paper is the identification of the memory effect as a key influence factor for Web QoE modeling based on subjective user studies. As second contribution, three different QoE models are proposed which consider the implications of the memory effect and imply the required extensions of the basic models. The proposed Web QoE models are described with a) support vector machines, b) iterative exponential regressions, and c) two-dimensional hidden Markov models.
Resource Allocation in Downlink Non-Orthogonal Multiple Access (NOMA) for Future Radio Access

This paper investigates a new strategy for radio resource allocation applying a non-orthogonal multiple access (NOMA) scheme. It calls for the cohabitation of users in the power domain at the transmitter side and for successive interference canceller (SIC) at the receiver side. Taking into account multi-user scheduling, subband assignment and transmit power allocation, a hybrid NOMA scheme is introduced. Adaptive switching to orthogonal signaling (OS) is performed whenever the non-orthogonal cohabitation in the power domain does not improve the achieved data rate per subband. In addition, a new power allocation technique based on waterfilling is introduced to improve the total achieved system throughput. We show that the proposed strategy for resource allocation improves both the spectral efficiency and the cell-edge user throughput. It also proves to be robust in the case of communications in crowded areas.
Forming interactivity: a tool for rapid prototyping of physical interactive products

The current practice used in the design of physical interactive products (such as handheld devices), often suffers from a divide between exploration of form and exploration of interactivity. This can be attributed, in part, to the fact that working prototypes are typically expensive, take a long time to manufacture, and require specialized skills and tools not commonly available in design studios.We have designed a prototyping tool that, we believe, can significantly reduce this divide. The tool allows designers to rapidly create functioning, interactive, physical prototypes early in the design process using a collection of wireless input components (buttons, sliders, etc.) and a sketch of form. The input components communicate with Macromedia Director to enable interactivity.We believe that this tool can improve the design practice by: a) Improving the designer's ability to explore both the form and interactivity of the product early in the design process, b) Improving the designer's ability to detect problems that emerge from the combination of the form and the interactivity, c) Improving users' ability to communicate their ideas, needs, frustrations and desires, and d) Improving the client's understanding of the proposed design, resulting in greater involvement and support for the design.
A 14.4nW 122KHz dual-phase current-mode relaxation oscillator for near-zero-power sensors

This paper presents a novel ultra-low-power dual-phase current-mode relaxation oscillator, which produces a 122 kHz digital clock and has total power consumption of 14.4 nW at 0.6 V. Its frequency dependence is 327 ppm/°C over a temperature range of -20° C to 100° C, and its supply voltage coefficient is ±3.0%/V from 0.6 V to 1.8 V. The proposed oscillator is fabricated in 0.18 μm CMOS technology and occupies 0.03 mm2. At room temperature it achieves a figure of merit of 120 pW/kHz, making it one of the most efficient relaxation oscillators reported to date.
Large-Scale Event Extraction from Literature with Multi-Level Gene Normalization

Text mining for the life sciences aims to aid database curation, knowledge summarization and information retrieval through the automated processing of biomedical texts. To provide comprehensive coverage and enable full integration with existing biomolecular database records, it is crucial that text mining tools scale up to millions of articles and that their analyses can be unambiguously linked to information recorded in resources such as UniProt, KEGG, BioGRID and NCBI databases. In this study, we investigate how fully automated text mining of complex biomolecular events can be augmented with a normalization strategy that identifies biological concepts in text, mapping them to identifiers at varying levels of granularity, ranging from canonicalized symbols to unique gene and proteins and broad gene families. To this end, we have combined two state-of-the-art text mining components, previously evaluated on two community-wide challenges, and have extended and improved upon these methods by exploiting their complementary nature. Using these systems, we perform normalization and event extraction to create a large-scale resource that is publicly available, unique in semantic scope, and covers all 21.9 million PubMed abstracts and 460 thousand PubMed Central open access full-text articles. This dataset contains 40 million biomolecular events involving 76 million gene/protein mentions, linked to 122 thousand distinct genes from 5032 species across the full taxonomic tree. Detailed evaluations and analyses reveal promising results for application of this data in database and pathway curation efforts. The main software components used in this study are released under an open-source license. Further, the resulting dataset is freely accessible through a novel API, providing programmatic and customized access . Finally, to allow for large-scale bioinformatic analyses, the entire resource is available for bulk download from http://evexdb.org/download/, under the Creative Commons - Attribution - Share Alike (CC BY-SA) license.
Design on mixed-voltage-tolerant I/O interface with novel tracking circuits in a 0.13-µm CMOS technology

This paper presents a 1.2V/2.5V tolerant I/O buffer design with only thin gate-oxide devices. The novel floating N-well and gate-tracking circuits in mixed-voltage I/O buffer are proposed to overcome the problem of leakage current, which will occur in the conventional CMOS I/O buffer when using in the mixedvoltage I/O interfaces. The new proposed 1.2V/2.5V tolerant I/O buffer design has been successfully verified in a 0.13-μm salicided CMOS process, which can be also applied in other CMOS processes to serve different mixed-voltage I/O interfaces.