nils-herrmann/clean_scirepeval huggingface dataset:
An end-to-end workflow for engineering of biological networks from high-level specifications.

We present a workflow for the design and production of biological networks from high-level program specifications. The workflow is based on a sequence of intermediate models that incrementally translate high-level specifications into DNA samples that implement them. We identify algorithms for translating between adjacent models and implement them as a set of software tools, organized into a four-stage toolchain: Specification, Compilation, Part Assignment, and Assembly. The specification stage begins with a Boolean logic computation specified in the Proto programming language. The compilation stage uses a library of network motifs and cellular platforms, also specified in Proto, to transform the program into an optimized Abstract Genetic Regulatory Network (AGRN) that implements the programmed behavior. The part assignment stage assigns DNA parts to the AGRN, drawing the parts from a database for the target cellular platform, to create a DNA sequence implementing the AGRN. Finally, the assembly stage computes an optimized assembly plan to create the DNA sequence from available part samples, yielding a protocol for producing a sample of engineered plasmids with robotics assistance. Our workflow is the first to automate the production of biological networks from a high-level program specification. Furthermore, the workflow's modular design allows the same program to be realized on different cellular platforms simply by swapping workflow configurations. We validated our workflow by specifying a small-molecule sensor-reporter program and verifying the resulting plasmids in both HEK 293 mammalian cells and in E. coli bacterial cells.
Eigenvalue spectra of random matrices for neural networks.

The dynamics of neural networks is influenced strongly by the spectrum of eigenvalues of the matrix describing their synaptic connectivity. In large networks, elements of the synaptic connectivity matrix can be chosen randomly from appropriate distributions, making results from random matrix theory highly relevant. Unfortunately, classic results on the eigenvalue spectra of random matrices do not apply to synaptic connectivity matrices because of the constraint that individual neurons are either excitatory or inhibitory. Therefore, we compute eigenvalue spectra of large random matrices with excitatory and inhibitory columns drawn from distributions with different means and equal or different variances.
Striving for work-life balance.

WORK-LIFE BALANCE means bringing work, whether done on the job or at home, and leisure time into balance to live life to its fullest. It doesn’t mean that you spend half of your life working and half of it playing; instead, it means balancing the two to achieve harmony in physical, emotional, and spiritual health. In today’s economy, can nurses achieve work-life balance? Although doing so may be difficult, the consequences to our health can be enormous if we don’t try. This article describes some of the stresses faced by nurses and tips for attaining a healthy balance of work and leisure.
Using Target-side Monolingual Data for Neural Machine Translation through Multi-task Learning

The performance of Neural Machine Translation (NMT) models relies heavily on the availability of sufficient amounts of parallel data, and an efficient and effective way of leveraging the vastly available amounts of monolingual data has yet to be found. We propose to modify the decoder in a neural sequence-to-sequence model to enable multi-task learning for two strongly related tasks: target-side language modeling and translation. The decoder predicts the next target word through two channels, a target-side language model on the lowest layer, and an attentional recurrent model which is conditioned on the source representation. This architecture allows joint training on both large amounts of monolingual and moderate amounts of bilingual data to improve NMT performance. Initial results in the news domain for three language pairs show moderate but consistent improvements over a baseline trained on bilingual data only.
Modified Microstrip Franklin Array Antenna for Automotive Short-Range Radar Application in Blind Spot Information System

A modified microstrip Franklin array antenna (MFAA) is proposed for short-range radar applications in vehicle blind spot information systems (BLIS). It is shown that the radiating performance [i.e., absolute gain value and half-power beamwidth (HPBW) angle] can be improved by increasing the number of radiators in the MFAA structure and assigning appropriate values to the antenna geometry parameters. The MFAA possesses a high absolute gain value (>10 dB), good directivity (HPBW <20°) in the E-plane, and large-range coverage (HPBW >80°) in the H-plane at an operating frequency of 24 GHz. Moreover, the 10-dB impedance bandwidth of the proposed antenna is around 250 MHz. The MFAA is, thus, an ideal candidate for automotive BLIS applications.
Enhanced creativity in bipolar disorder patients: a controlled study.

Associations between eminent creativity and bipolar disorders have been reported, but there are few data relating non-eminent creativity to bipolar disorders in clinical samples. We assessed non-eminent creativity in euthymic bipolar (BP) and unipolar major depressive disorder (MDD) patients, creative discipline controls (CC), and healthy controls (HC).



49 BP, 25 MDD, 32 CC, and 47 HC (all euthymic) completed four creativity measures yielding six parameters: the Barron-Welsh Art Scale (BWAS-Total, and two subscales, BWAS-Dislike and BWAS-Like), the Adjective Check List Creative Personality Scale (ACL-CPS), and the Torrance Tests of Creative Thinking--Figural (TTCT-F) and Verbal (TTCT-V) versions. Mean scores on these instruments were compared across groups.



BP and CC (but not MDD) compared to HC scored significantly higher on BWAS-Total (45% and 48% higher, respectively) and BWAS-Dislike (90% and 88% higher, respectively), but not on BWAS-Like. CC compared to MDD scored significantly higher (12% higher) on TTCT-F. For all other comparisons, creativity scores did not differ significantly between groups.



We found BP and CC (but not MDD) had similarly enhanced creativity on the BWAS-Total (driven by an increase on the BWAS-Dislike) compared to HC. Further studies are needed to determine the mechanisms of enhanced creativity and how it relates to clinical (e.g. temperament, mood, and medication status) and preclinical (e.g. visual and affective processing substrates) parameters.
A protein interaction network for pluripotency of embryonic stem cells

Embryonic stem (ES) cells are pluripotent and of therapeutic potential in regenerative medicine. Understanding pluripotency at the molecular level should illuminate fundamental properties of stem cells and the process of cellular reprogramming. Through cell fusion the embryonic cell phenotype can be imposed on somatic cells, a process promoted by the homeodomain protein Nanog, which is central to the maintenance of ES cell pluripotency. Nanog is thought to function in concert with other factors such as Oct4 (ref. 8) and Sox2 (ref. 9) to establish ES cell identity. Here we explore the protein network in which Nanog operates in mouse ES cells. Using affinity purification of Nanog under native conditions followed by mass spectrometry, we have identified physically associated proteins. In an iterative fashion we also identified partners of several Nanog-associated proteins (including Oct4), validated the functional relevance of selected newly identified components and constructed a protein interaction network. The network is highly enriched for nuclear factors that are individually critical for maintenance of the ES cell state and co-regulated on differentiation. The network is linked to multiple co-repressor pathways and is composed of numerous proteins whose encoding genes are putative direct transcriptional targets of its members. This tight protein network seems to function as a cellular module dedicated to pluripotency.
idtracker.ai: Tracking all individuals in large collectives of unmarked animals

Our understanding of collective animal behavior is limited by our ability to track each of the individuals. We describe an algorithm and software, idtracker.ai, that extracts from video all trajectories with correct identities at a high accuracy for collectives of up to 100 individuals. It uses two deep networks, one detecting when animals touch or cross and another one for animal identi cation, trained adaptively to conditions and di culty of the video.
Trot pattern generation for quadruped robot based on the ZMP stability margin

Quadruped robot is expected to serve in complex conditions such as mountain road, grassland, etc., therefore we desire a walking pattern generation that can guarantee both the speed and the stability of the quadruped robot. In order to solve this problem, this paper focuses on the stability for the tort pattern and proposes trot pattern generation for quadruped robot on the basis of ZMP stability margin. The foot trajectory is first designed based on the work space limitation. Then the ZMP and stability margin is computed to achieve the optimal trajectory of the midpoint of the hip joint of the robot. The angles of each joint are finally obtained through the inverse kinematics calculation. Finally, the effectiveness of the proposed method is demonstrated by the results from the simulation and the experiment on the quadruped robot in BIT.
Design and implementation of a novel interleaved flyback converter with leakage energy recycled

A novel interleaved flyback converter with leakage energy recycled is proposed. The proposed converter is combined with dual-switch dual-transformer flyback topology. Two clamping diodes are used to reduce the voltage stress on power switches to the input voltage level and also to recycle leakage inductance energy to the input voltage and capacitor. Besides, the interleaved control is implemented to reduce the output current ripple. In addition, the voltage on the primary windings is reduced to the half of the input voltage and thus reducing the turns ratio of transformers to improve efficiency. The operating principle and the steady state analysis of the proposed converter are discussed in detail. Finally, an experimental prototype is implemented with 400V input voltage, 24V/300W output to verify the feasibility of the proposed converter. The experimental results reveals that the highest efficiency of the proposed converter is 94.42%, the full load efficiency is 92.7%, and the 10% load efficiency is 92.61%.
Predictive Embeddings for Hate Speech Detection on Twitter

We present a neural-network based approach to classifying online hate speech in general, as well as racist and sexist speech in particular. Using pre-trained word embeddings and max/mean pooling from simple, fullyconnected transformations of these embeddings, we are able to predict the occurrence of hate speech on three commonly used publicly available datasets. Our models match or outperform state of the art F1 performance on all three datasets using significantly fewer parameters and minimal feature preprocessing compared to previous methods.
Multiple Sclerosis: MicroRNA Expression Profiles Accurately Differentiate Patients with Relapsing-Remitting Disease from Healthy Controls

Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system, which is heterogenous with respect to clinical manifestations and response to therapy. Identification of biomarkers appears desirable for an improved diagnosis of MS as well as for monitoring of disease activity and treatment response. MicroRNAs (miRNAs) are short non-coding RNAs, which have been shown to have the potential to serve as biomarkers for different human diseases, most notably cancer. Here, we analyzed the expression profiles of 866 human miRNAs. In detail, we investigated the miRNA expression in blood cells of 20 patients with relapsing-remitting MS (RRMS) and 19 healthy controls using a human miRNA microarray and the Geniom Real Time Analyzer (GRTA) platform. We identified 165 miRNAs that were significantly up- or downregulated in patients with RRMS as compared to healthy controls. The best single miRNA marker, hsa-miR-145, allowed discriminating MS from controls with a specificity of 89.5%, a sensitivity of 90.0%, and an accuracy of 89.7%. A set of 48 miRNAs that was evaluated by radial basis function kernel support vector machines and 10-fold cross validation yielded a specificity of 95%, a sensitivity of 97.6%, and an accuracy of 96.3%. While 43 of the 165 miRNAs deregulated in patients with MS have previously been related to other human diseases, the remaining 122 miRNAs are so far exclusively associated with MS. The implications of our study are twofold. The miRNA expression profiles in blood cells may serve as a biomarker for MS, and deregulation of miRNA expression may play a role in the pathogenesis of MS.
Comp 322: Fundamentals of Parallel Programming Module 2: Concurrency

5 Mutual Exclusion 2 5.1 Critical Sections and the “isolated” statement . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 5.2 Object-based Isolation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 5.3 Parallel Spanning Tree Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 5.4 Atomic Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 5.5 Serialized Computation Graph for Isolated Statements . . . . . . . . . . . . . . . . . . . . . .
Design of a Notched-Band Vivaldi Antenna With High Selectivity

In this letter, a notched-band Vivaldi antenna with high-frequency selectivity is designed and investigated. To obtain two notched poles inside the stopband, an open-circuited half-wavelength resonator and a short-circuited stepped impedance resonator are properly introduced into the traditional Vivaldi antenna. By theoretically calculating the resonant frequencies of the two loaded resonators, the frequency locations of the two notched poles can be precisely determined, thus achieving a wideband antenna with a desired notched band. To validate the feasibility of this new approach, a notched band antenna with a fractional bandwidth of 145.8% is fabricated and tested. Results indicate that good frequency selectivity of the notched band from 4.9 to 6.6 GHz is realized, and the antenna exhibits good impedance match, high radiation gain, and excellent radiation directivity in the passband. Both the simulation and measurement results are provided with good agreement.
Phosphor Deposits of β-Sialon:Eu2+ Mixed with SnO2 Nanoparticles Fabricated by the Electrophoretic Deposition (EPD) Process

The phosphor deposits of the β-sialon:Eu2+ mixed with various amounts (0-1 g) of the SnO₂ nanoparticles were fabricated by the electrophoretic deposition (EPD) process. The mixed SnO₂ nanoparticles was observed to cover onto the particle surfaces of the β-sialon:Eu2+ as well as fill in the voids among the phosphor particles. The external and internal quantum efficiencies (QEs) of the prepared deposits were found to be dependent on the mixing amount of the SnO₂: by comparing with the deposit without any mixing (48% internal and 38% external QEs), after mixing the SnO₂ nanoparticles, the both QEs were improved to 55% internal and 43% external QEs at small mixing amount (0.05 g); whereas, with increasing the mixing amount to 0.1 and 1 g, they were reduced to 36% and 29% for the 0.1 g addition and 15% and 12% l QEs for the 1 g addition. More interestingly, tunable color appearances of the deposits prepared by the EPD process were achieved, from yellow green to blue, by varying the addition amount of the SnO₂, enabling it as an alternative technique instead of altering the voltage and depositing time for the color appearance controllability.
Detecting Latent User Properties in Social Media

The ability to identify user attributes such as gender, age, regional origin, and political orientation solely from user language in social media such as Twitter or similar highly informal content has important applications in advertising, personalization, and recommendation. This paper includes a novel investigation of stacked-SVM-based classification algorithms over a rich set of original features, applied to classifying these four user attributes. We propose new sociolinguisticsbased features for classifying user attributes in Twitter-style informal written genres, as distinct from the other primarily spoken genres previously studied in the user-property classification literature. Our models, singly and in ensemble, significantly outperform baseline models in all cases.
ADML : Aircraft Design Markup Language for Multidisciplinary Aircraft Design and Analysis

The process of conceptual aircraft design has advanced tremendously in the past few decades due to rapidly developing computer technology. Today’s modern aerospace systems exhibit strong, interdisciplinary coupling and require a multidisciplinary, collaborative approach. Efficient transfer, sharing, and manipulation of aircraft design and analysis data in such a collaborative environment demands a formal structured representation of data. XML, a W3C recommendation, is one such standard concomitant with a number of powerful capabilities that alleviate interoperability issues in a collaborative environment. A compact, generic, and comprehensive XML schema for an aircraft design markup language (ADML) is proposed here to represent aircraft conceptual design and analysis data. The purpose of this unified data format is to provide a common language for data communication, and to improve efficiency and productivity within a multidisciplinary, collaborative aricraft design environment. An important feature of the proposed schema is the very expressive and efficient low level schemata (raw data, mathematical objects, and basic geometry). As a proof of concept the schema is used to encode an entire Convair B58. As the complexity of models and number of disciplines increases, the reduction in effort to exchange data models and analysis results in ADML also increases.
Embedded queries and Mining Techniques

Data mining aims at extracting knowledge from data. Information rich datasets, such as EBSCOhost Newspaper Source , carry a significant amount of multi-typed current and archived news data. This extracted information can easily be constructed into a heterogeneous information network. Through use of many mining techniques, deeper comprehension can then be unearthed from the underlying relationships between article, authors, tags, etc. This paper focuses on building on two such techniques, classification and embedding. GNetMine  is a common classification algorithm that is able to label entities of different types through a small set of training data. Node2Vec  is an embedding approach, using the many nodes and edges in a heterogeneous network, and converting them into a lowdimensional vector space, that can quickly and easily allow for comparison between nodes of any type. The goal of this paper is to combine these methods and compare and contrast the quality of output of Node2Vec with unlabeled data, directly from the heterogeneous network of EBSCOhost sports news data, as well as adding learned classification labels. Can nodes labeled using classification as an input to network embedding, improve the outcome of the embedding results?
Compile-time function memoization

Memoization is the technique of saving the results of computations so that future executions can be omitted when the same inputs repeat. Recent work showed that memoization can be applied to dynamically linked pure functions using a load-time technique and results were encouraging for the demonstrated transcendental functions. A restriction of the proposed framework was that memoization was restricted only to dynamically linked functions and the functions must be determined beforehand. In this work, we propose function memoization using a compile-time technique thus extending the scope of memoization to user defined functions as well as making it transparently applicable to any dynamically linked functions. Our compile-time technique allows static linking of memoization code and this increases the benefit due to memoization by leveraging the inlining capability for the memoization wrapper. Our compile-time analysis can also handle functions with pointer parameters, and we handle constants more efficiently. Instruction set support can also be considered, and we propose associated hardware leading to additional performance gain.
NEW METHODS FOR AUTOMATIC READING OF VLP's (VEHICLE LICENSE PLATES)

In a former work, we built a VLP (Vehicle License Plates) reader using computer vision. To test other methods, we decided to implement a whole new system from scratch. In this communication, we summarize the methods that we have tested and the results we have obtained. Plate location is based on mathematical morphology and character recognition is implemented using Hausdorff distance. Results are comparable to those obtained with other methods.
iParaphrasing: Extracting Visually Grounded Paraphrases via an Image

A paraphrase is a restatement of the meaning of a text in other words. Paraphrases have been studied to enhance the performance of many natural language processing tasks. In this paper, we propose a novel task iParaphrasing to extract visually grounded paraphrases (VGPs), which are different phrasal expressions describing the same visual concept in an image. These extracted VGPs have the potential to improve language and image multimodal tasks such as visual question answering and image captioning. How to model the similarity between VGPs is the key of iParaphrasing. We apply various existing methods as well as propose a novel neural network-based method with image attention, and report the results of the first attempt toward iParaphrasing.
What Is Developmental Dyspraxia

The idea of developmental dyspraxia has been discussed in the research literature for almost 100 years. However, there continues to be a lack of consensus regarding both the definition and description of this disorder. This paper presents a neuropsychologically based operational definition of developmental dyspraxia that emphasizes that developmental dyspraxia is a disorder of gesture. Research that has investigated the development of praxis is discussed. Further, different types of gestural disorders displayed by children and different mechanisms that underlie developmental dyspraxia are compared to and contrasted with adult acquired apraxia. The impact of perceptual-motor, language, and cognitive impairments on children's gestural development and the possible associations between these developmental disorders and developmental dyspraxia are also examined. Also, the relationship among limb, orofacial, and verbal dyspraxia is discussed. Finally, problems that exist in the neuropsychological assessment of developmental dyspraxia are discussed and recommendations concerning what should be included in such an assessment are presented.