nils-herrmann/clean_scirepeval huggingface dataset:
Automated synaptic connectivity inference for volume electron microscopy

Teravoxel volume electron microscopy data sets from neural tissue can now be acquired in weeks, but data analysis requires years of manual labor. We developed the SyConn framework, which uses deep convolutional neural networks and random forest classifiers to infer a richly annotated synaptic connectivity matrix from manual neurite skeleton reconstructions by automatically identifying mitochondria, synapses and their types, axons, dendrites, spines, myelin, somata and cell types. We tested our approach on serial block-face electron microscopy data sets from zebrafish, mouse and zebra finch, and computed the synaptic wiring of songbird basal ganglia. We found that, for example, basal-ganglia cell types with high firing rates in vivo had higher densities of mitochondria and vesicles and that synapse sizes and quantities scaled systematically, depending on the innervated postsynaptic cell types.
Adaptive Bitonic Sorting: An Optimal Parallel Algorithm for Shared-Memory Machines

A parallel algorithm, called adaptive bitonic sorting, that runs on a PRAC (parallel random access computer), a shared-memory multiprocessor where fetch and store conflicts are disallowed, is proposed. On a P processors PRAC, the algorithm presented here achieves optimal performance TP O(N log N), for any computation time T in the range (log N)<-_ T<= O(N log N). Adaptive bitonic sorting also has a small constant factor, since it performs less than 2N log N comparisons, and only a handful of operations per comparison. Key words, sorting, parallel computation, shared-memory machines, bitonic sequence, time processors optimality AMS(MOS) subject classifications. 68Q20, 68Q25, 68Q10
Learning to Predict Code-Switching Points

Predicting possible code-switching points can help develop more accurate methods for automatically processing mixed-language text, such as multilingual language models for speech recognition systems and syntactic analyzers. We present in this paper exploratory results on learning to predict potential codeswitching points in Spanish-English. We trained different learning algorithms using a transcription of code-switched discourse. To evaluate the performance of the classifiers, we used two different criteria: 1) measuring precision, recall, and F-measure of the predictions against the reference in the transcription, and 2) rating the naturalness of artificially generated code-switched sentences. Average scores for the code-switched sentences generated by our machine learning approach were close to the scores of those generated by humans.
Design and fabrication of Schottky diodes in 1.2 µm CMOS process

The I-V curves for Schottky diodes with two different contact areas and geometries fabricated through 1.2 μm CMOS process are presented. These curves are described applying the analysis and practical layout design. It takes into account the resistance, capacitance and reverse breakdown voltage in the semiconductor structure and the dependence of these parameters to improve its operation. The described diodes are used for a charge pump circuit implementation.
Dynamic load balancing with handover in hybrid Li-Fi and Wi-Fi networks

In this paper, a hybrid network combining visible light communication (VLC) with a radio frequency (RF) wireless local area network (WLAN) is considered. In indoor scenarios, a light fidelity (Li-Fi) access point (AP) can provide very high throughput and satisfy any illumination demands while wireless fidelity (Wi-Fi) offers basic coverage. Such a hybrid network with both fixed and mobile users has the problem of variable user locations, and thus large fluctuations in spatially distributed traffic demand. Generally, a handover occurs in such a method when a user is allocated by the central controller unit to a different AP which is better placed to serve the user. In order to be representative of real deployments, this paper studies the problem of load balancing of a dynamic system where we consider the signalling overhead for handover. We propose a scheme for dynamic allocation of resources to users, where the utility function takes into account both throughput and fairness. The simulation results show that there is a trade off between the aggregate throughput and user fairness when handover overhead is considered. The proposed dynamic scheme always outperforms the considered benchmarks in terms of fairness and can achieve better aggregate throughput in the case of low user density.
Mining Fuzzy Weighted Association Rules

The paper combines and extends the technologies of fuzzy sets and association rules, considering users' differential emphasis on each attribute through fuzzy regions. A fuzzy data mining algorithm is proposed to discovery fuzzy association rules for weighted quantitative data. This is expected to be more realistic and practical than crisp association rules. Discovered rules are expressed in natural language that is more understandable to humans. The paper demonstrates the performance of the proposed approach using a synthetic but realistic dataset
Randomised clinical trial: addition of alginate-antacid (Gaviscon Double Action) to proton pump inhibitor therapy in patients with breakthrough symptoms.

Symptomatic breakthrough in proton pump inhibitor (PPI)-treated gastro-oesophageal reflux disease (GERD) patients is a common problem with a range of underlying causes. The nonsystemic, raft-forming action of alginates may help resolve symptoms.



To assess alginate-antacid (Gaviscon Double Action, RB, Slough, UK) as add-on therapy to once-daily PPI for suppression of breakthrough reflux symptoms.



In two randomised, double-blind studies (exploratory, n=52; confirmatory, n=262), patients taking standard-dose PPI who had breakthrough symptoms, assessed by Heartburn Reflux Dyspepsia Questionnaire (HRDQ), were randomised to add-on Gaviscon or placebo (20 mL after meals and bedtime). The exploratory study endpoint was change in HRDQ score during treatment vs run-in. The confirmatory study endpoint was "response" defined as ≥3 days reduction in the number of "bad" days (HRDQ [heartburn/regurgitation] >0.70) during treatment vs run-in.



In the exploratory study, significantly greater reductions in HRDQ scores (heartburn/regurgitation) were observed in the Gaviscon vs placebo (least squares mean difference [95% CI] -2.10 [-3.71 to -0.48]; P=.012). Post hoc "responder" analysis of the exploratory study also revealed significantly more Gaviscon patients (75%) achieved ≥3 days reduction in "bad" days vs placebo patients (36%), P=.005. In the confirmatory study, symptomatic improvement was observed with add-on Gaviscon (51%) but there was no significant difference in response vs placebo (48%) (OR (95% CI) 1.15 (0.69-1.91), P=.5939).



Adding Gaviscon to PPI reduced breakthrough GERD symptoms but a nearly equal response was observed for placebo. Response to intervention may vary according to whether symptoms are functional in origin.
Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders

Deep generative models have achieved remarkable success in various data domains, including images, time series, and natural languages. There remain, however, substantial challenges for combinatorial structures, including graphs. One of the key challenges lies in the difficulty of ensuring semantic validity in context. For example, in molecular graphs, the number of bonding-electron pairs must not exceed the valence of an atom; whereas in protein interaction networks, two proteins may be connected only when they belong to the same or correlated gene ontology terms. These constraints are not easy to be incorporated into a generative model. In this work, we propose a regularization framework for variational autoencoders as a step toward semantic validity. We focus on the matrix representation of graphs and formulate penalty terms that regularize the output distribution of the decoder to encourage the satisfaction of validity constraints. Experimental results confirm a much higher likelihood of sampling valid graphs in our approach, compared with others reported in the literature.
A novel XML document structure comparison framework based-on sub-tree commonalities and label semantics

XML similarity evaluation has become a central issue in the database and information communities, its applications ranging over document clustering, version control, data integration and ranked retrieval. Various algorithms for comparing hierarchically structured data, XML documents in particular, have been proposed in the literature. Most of them make use of techniques for finding the edit distance between tree structures, XML documents being commonly modeled as Ordered Labeled Trees. Yet, a thorough investigation of current approaches led us to identify several similarity aspects, i.e., sub-tree related structural and semantic similarities, which are not sufficiently addressed while comparing XML documents. In this paper, we provide an integrated and fine-grained comparison framework to deal with both structural and semantic similarities in XML documents (detecting the occurrences and repetitions of structurally and semantically similar sub-trees), and to allow the end-user to adjust the comparison process according to her requirements. Our framework consists of four main modules for i) discovering the structural commonalities between sub-trees, ii) identifying sub-tree semantic resemblances, iii) computing tree-based edit operations costs, and iv) computing tree edit distance. Experimental results demonstrate higher comparison accuracy with respect to alternative methods, while timing experiments reflect the impact of semantic similarity on overall system performance.
A Survey on Selective Forwarding Attacks in Wireless Sensor Networks

Wireless sensor network has become an emerging technology due its wide range of applications in object tracking and monitoring, military commands, smart homes, forest fire control, surveillance, etc. Wireless sensor network consists of thousands of miniature devices which are called sensors but as it uses wireless media for communication, so security is the major issue. There are number of attacks on wireless of which selective forwarding attack is one of the harmful attacks. This paper describes selective forwarding attack and detection techniques against selective forwarding attacks which have been proposed by different researchers. In selective forwarding attacks, malicious nodes act like normal nodes and selectively drop packets. The selective forwarding attack is a serious threat in WSN. Identifying such attacks is very difficult and sometimes impossible. This paper also presents qualitative analysis of detection techniques in tabular form. Keywordswireless sensor network, attacks, selective forwarding attacks, malicious nodes.
Exploiting linguistic knowledge to infer properties of neologisms

Exploiting linguistic knowledge to infer properties of neologisms C. Paul Cook Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2010 Neologisms, or newly-coined words, pose problems for natural language processing (NLP) systems. Due to the recency of their coinage, neologisms are typically not listed in computational lexicons—dictionary-like resources that many NLP applications depend on. Therefore when a neologism is encountered in a text being processed, the performance of an NLP system will likely suffer due to the missing word-level information. Identifying and documenting the usage of neologisms is also a challenge in lexicography, the making of dictionaries. The traditional approach to these tasks has been to manually read a lot of text. However, due to the vast quantities of text being produced nowadays, particularly in electronic media such as blogs, it is no longer possible to manually analyze it all in search of neologisms. Methods for automatically identifying and inferring syntactic and semantic properties of neologisms would therefore address problems encountered in both natural language processing and lexicography. Because neologisms are typically infrequent due to their recent addition to the language, approaches to automatically learning word-level information relying on statistical distributional information are in many cases inappropriate. Moreover, neologisms occur in many domains and genres, and therefore approaches relying on domain-specific resources are also inappropriate. The hypothesis of this thesis is that knowledge about etymology—including word formation processes and types of semantic change—can be exploited for the acquisition of aspects of the syntax and semantics of neologisms. Evidence supporting this hypothesis is found
Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms

Guarantee of TWO-PHASE ALGORITHM. max f x , f z ≥\ ( x − x∗ $ + z − z∗ $)+ ^ _ ` x ∗ abcd e^ f^g^ , i^ abcd eX fXg^ , iX , where z∗ ≔ x ∨ x∗ − x NON-MONOTONE FRANK-WOLFE VARIANT Input: step size γ ∈ (0,1] x(o) ← 0, k ← 0, t(o) ← 0 // t: cumulative step size While t(q) < 1 do: v(q) ← argmaxv∈P,vsu0ax(t) v, ∇f(x (q)) // shrunken LMO γq ← min γ, 1 − t(q) x(qC") ← x(q) + γqv, t(qC") ← t(q) + γq, k + + Output: x(w)
Current status of percutaneous vertebroplasty and percutaneous kyphoplasty – a review

Percutaneous vertebroplasty (PV) and kyphoplasty (PK) are the 2vertebral augmentation procedures that have emerged as minimally invasive surgical options to treat painful vertebral compression fractures (VCF) during the last 2 decades. VCF may either be osteoporotic or tumor-associated. Two hundred million women are affected by osteoporosis globally. Vertebral fracture may result in acute pain around the fracture site, loss of vertebral height due to vertebral collapse, spinal instability, and kyphotic deformity. The main goal of the PV and PK procedures is to give immediate pain relief to patients and restore the vertebral height lost due to fracture. In percutaneous vertebroplasty, bone cement is injected through a minimal incision into the fractured site. Kyphoplasty involves insertion of a balloon into the fractured site, followed by inflation-deflation to create a cavity into which the filler material is injected, and the balloon is taken out prior to cement injection. This literature review presents a qualitative overview on the current status of vertebral augmentation procedures,especially PV and PK, and compares the efficacy and safety of these 2 procedures. The review consists of a brief history of the development of these 2 techniques, a discussion on the current research on the bone cement, clinical outcome of the 2 procedures, and it also sheds light on ongoing and future research to maximize the efficacy and safety of vertebral augmentation procedures.
Low-cost photovoltaic microinverter with ultra-wide MPPT voltage range

This paper presents a novel low-cost photovoltaic (PV) microinverter with an ultra-wide MPPT voltage range. This approach is based on the two-stage energy conversion where the front-end boost half-bridge DC-DC converter is used to step up the fluctuating PV voltage to a stabilized high DC voltage, which is next transformed to a grid compliant AC voltage by the grid-tied inverter. The operation principle of the microinverter is explained by the help of the steady-state analysis. To verify theoretical assumptions and evaluate the performance of the proposed concept, a 250 W experimental prototype of a photovoltaic microinverter was assembled and tested. Finally, its shade-tolerant operation is demonstrated with the partially shaded 60-cell Si PV module.
Transfer Learning with Deep Convolutional Neural Network for SAR Target Classification with Limited Labeled Data

Tremendous progress has been made in object recognition with deep convolutional neural networks (CNNs), thanks to the availability of large-scale annotated dataset. With the ability of learning highly hierarchical image feature extractors, deep CNNs are also expected to solve the Synthetic Aperture Radar (SAR) target classification problems. However, the limited labeled SAR target data becomes a handicap to train a deep CNN. To solve this problem, we propose a transfer learning based method, making knowledge learned from sufficient unlabeled SAR scene images transferrable to labeled SAR target data. We design an assembled CNN architecture consisting of a classification pathway and a reconstruction pathway, together with a feedback bypass additionally. Instead of training a deep network with limited dataset from scratch, a large number of unlabeled SAR scene images are used to train the reconstruction pathway with stacked convolutional auto-encoders (SCAE) at first. Then, these pre-trained convolutional layers are reused to transfer knowledge to SAR target classification tasks, with feedback bypass introducing the reconstruction loss simultaneously. The experimental results demonstrate that transfer learning leads to a better performance in the case of scarce labeled training data and the additional feedback bypass with reconstruction loss helps to boost the capability of classification pathway.
Framework for Generation of Synthetic Ground Truth Data for Driver Assistance Applications

High precision ground truth data is a very important factor for the development and evaluation of computer vision algorithms and especially for advanced driver assistance systems. Unfortunately, some types of data, like accurate optical flow and depth as well as pixel-wise semantic annotations are very difficult to obtain. In order to address this problem, in this paper we present a new framework for the generation of high quality synthetic camera images, depth and optical flow maps and pixel-wise semantic annotations. The framework is based on a realistic driving simulator called VDrift , which allows us to create traffic scenarios very similar to those in real life. We show how we can use the proposed framework to generate an extensive dataset for the task of multi-class image segmentation. We use the dataset to train a pairwise CRF model and to analyze the effects of using various combinations of features in different image modalities.
Operational TomoSAR processing using TerraSAR-X high resolution spotlight stacks from multiple view angles

With the availability of meter resolution space-borne SAR systems, urban monitoring using SAR Tomography (TomoSAR) becomes increasingly popular, because of its layover separation capability. However, compared to Persistent Scatterer Interferometry (PSI), TomoSAR applications are much more computationally expensive. This article introduces a TomoSAR processing system for long-term large urban area mapping and monitoring. Two new features were introduced: 1. PSI was integrated into the currently available TomoSAR algorithms (e.g. TSVD, SVD-Wiener, and SL1MMER) to increase the overall computational efficiency; 2. results from multiple view angles were fused to provide full coverage of each building façade. This processing system handles an entire TerraSAR-X high resolution spotlight scene in an affordable time, achieving scatterer density up to 1 million scatterer/km2 from a single stack, comparing to 60 thousand to 100 thousand scatterer/km2 for PSI.
Does Overconfidence Affect Corporate Investment ? CEO Overconfidence Measures Revisited

This article presents the growing research area of Behavioural Corporate Finance in the context of one specific example: distortions in corporate investment due to CEO overconfidence. We first review the relevant psychology and experimental evidence on overconfidence. We then summarise the results of Malmendier and Tate (2005a) on the impact of overconfidence on corporate investment. We present supplementary evidence on the relationship betweenCEOs’ press portrayals and overconfident investment decisions. This alternative approach to measuring overconfidence, developed in Malmendier and Tate (2005b), relies on the perception of outsiders rather than the CEO’s own actions. The robustness of the results across such diverse proxies jointly corroborates previous findings and suggests new avenues to measuring executive overconfidence.
A Novel Electricity Transaction Mode of Microgrids Based on Blockchain and Continuous Double Auction

The installed capacity of distributed generation (DG) based on renewable energy sources has increased continuously in power systems, and its market-oriented transaction is imperative. However, traditional transaction management based on centralized organizations has many disadvantages, such as high operation cost, low transparency, and potential risk of transaction data modification. Therefore, a decentralized electricity transaction mode for microgrids is proposed in this study based on blockchain and continuous double auction (CDA) mechanism. A buyer and seller initially complete the transaction matching in the CDA market. In view of the frequent price fluctuation in the CDA market, an adaptive aggressiveness strategy is used to adjust the quotation timely according to market changes. DG and consumer exchange digital certificate of power and expenditure on the blockchain system and the interests of consumers are then guaranteed by multi-signature when DG cannot generate power due to failure or other reasons. The digital certification of electricity assets is replaced by the sequence number with specific tags in the transaction script, and the size of digital certification can be adjusted according to transaction energy quantity. Finally, the feasibility of market mechanism through specific microgrid case and settlement process is also provided.
Autonomous decision making for a driver-less car

Autonomous driving has been a hot topic with companies like Google, Uber, and Tesla because of the complexity of the problem, seemingly endless applications, and capital gain. The technology's brain child is DARPA's autonomous urban challenge from over a decade ago. Few companies have had some success in applying algorithms to commercial cars. These algorithms range from classical control approaches to Deep Learning. In this paper, we will use Deep Learning techniques and the Tensor flow framework with the goal of navigating a driverless car through an urban environment. The novelty in this system is the use of Deep Learning vs. traditional methods of real-time autonomous operation as well as the application of the Tensorflow framework itself. This paper provides an implementation of AlexNet's Deep Learning model for identifying driving indicators, how to implement them in a real system, and any unforeseen drawbacks to these techniques and how these are minimized and overcome.