nils-herrmann/clean_scirepeval huggingface dataset:
Natural Language Generation with Neural Variational Models

Automatic generation of text is an important topic in natural language processing with applications in tasks such as machine translation and text summarization. In this thesis, we explore the use of deep neural networks for generation of natural language. Specifically, we implement two sequence-to-sequence neural variational models variational autoencoders (VAE) and variational encoder-decoders (VED). VAEs for text generation are difficult to train due to issues associated with the KullbackLeibler (KL) divergence term of the loss function vanishing to zero. We successfully train VAEs by implementing optimization heuristics such as KL weight annealing and word dropout. In addition, this work also proposes new and improved annealing schedules that facilitates the learning of a meaningful latent space. We also demonstrate the effectiveness of this continuous latent space through experiments such as random sampling, linear interpolation and sampling from the neighborhood of the input. We argue that if VAEs are not designed appropriately, it may lead to bypassing connections which results in the latent space being ignored during training. We show experimentally with the example of decoder hidden state initialization that such bypassing connections degrade the VAE into a deterministic model, thereby reducing the diversity of generated sentences. We discover that the traditional attention mechanism used in sequence-to-sequence VED models serves as a bypassing connection, thereby deteriorating the model’s latent space. In order to circumvent this issue, we propose the variational attention mechanism where the attention context vector is modeled as a random variable that can be sampled from a distribution. We show empirically using automatic evaluation metrics, namely entropy and distinct measures, that our variational attention model generates more diverse output sentences than the deterministic attention model. A qualitative analysis with human evaluation study proves that our model simultaneously produces sentences that are of high quality and equally fluent as the ones generated by the deterministic attention counterpart.
Optical PUFs Reloaded

We revisit optical physical unclonable functions (PUFs), which were proposed by Pappu et al. in their seminal first publication on PUFs [40, 41]. The first part of the paper treats non-integrated optical PUFs. Their security against modeling attacks is analyzed, and we discuss new image transformations that maximize the PUF’s output entropy while possessing similar error correction capacities as previous approaches [40, 41]. Furthermore, the influence of using more than one laser beam, varying laser diameters, and smaller scatterer sizes is systematically studied. Our findings enable the simple enhancement of an optical PUF’s security without additional hardware costs. Next, we discuss the novel application of non-integrated optical PUFs as so-called “Certifiable PUFs”. The latter are useful to achieve practical security in advanced PUF-protocols, as recently observed by Rührmair and van Dijk at Oakland 2013 . Our technique is the first mechanism for Certifiable PUFs in the literature, answering an open problem posed in . In the second part of the paper, we turn to integrated optical PUFs. We build the first prototype of an integrated optical PUF that functions without moving components and investigate its security. We show that these PUFs can surprisingly be attacked by machine learning techniques if the employed scattering structure is linear, and if the raw interference images of the PUF are available to the adversary. Our result enforces the use of non-linear scattering structures within integrated PUFs. The quest for suitable materials Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.  Our work makes intensive use of two prototypes of optical PUFs. The presented integratable optical PUF prototype is, to our knowledge, the first of its kind in the literature.
Binary Code Extraction and Interface Identification for Security Applications

Binary code reutilization is the process of automatically identifying the interface and extracting the instructions and data dependencies of a code fragment from an executable program, so that it is selfcontained and can be reused by external code. Binary code reutilization is useful for a number of security applications, including reusing the proprietary cryptographic or unpacking functions from a malware sample and for rewriting a network dialog. In this paper we conduct the first systematic study of automated binary code reutilization and its security applications. The main challenge in binary code reutilization is understanding the code fragment’s interface. We propose a novel technique to identify the prototype of an undocumented code fragment directly from the program’s binary, without access to source code or symbol information. Further, we must also extract the code itself from the binary so that it is self-contained and can be easily reused in another program. We design and implement a tool that uses a combination of dynamic and static analysis to automatically identify the prototype and extract the instructions of an assembly function into a form that can be reused by other C code. The extracted function can be run independently of the rest of the program’s functionality and shared with other users. We apply our approach to scenarios that include extracting the encryption and decryption routines from malware samples, and show that these routines can be reused by a network proxy to decrypt encrypted traffic on the network. This allows the network proxy to rewrite the malware’s encrypted traffic by combining the extracted encryption and decryption functions with the session keys and the protocol grammar. We also show that we can reuse a code fragment from an unpacking function for the unpacking routine for a different sample of the same family, even if the code fragment is not a complete function.
Fhl1 as a downstream target of Wnt signaling to promote myogenesis of C2C12 cells

Previous studies have shown that Wnt signaling is involved in postnatal mammalian myogenesis; however, the downstream mechanism of Wnt signaling is not fully understood. This study reports that the murine four-and-a-half LIM domain 1 (Fhl1) could be stimulated by β-catenin or LiCl treatment to induce myogenesis. In contrast, knockdown of the Fhl1 gene expression in C2C12 cells led to reduced myotube formation. We also adopted reporter assays to demonstrate that either β-catenin or LiCl significantly activated the Fhl1 promoter, which contains four putative consensus TCF/LEF binding sites. Mutations of two of these sites caused a significant decrease in promoter activity by luciferase reporter assay. Thus, we suggest that Wnt signaling induces muscle cell differentiation, at least partly, through Fhl1 activation.
Sensory processing subtypes in autism: association with adaptive behavior.

Children with autism are frequently observed to experience difficulties in sensory processing. This study examined specific patterns of sensory processing in 54 children with autistic disorder and their association with adaptive behavior. Model-based cluster analysis revealed three distinct sensory processing subtypes in autism. These subtypes were differentiated by taste and smell sensitivity and movement-related sensory behavior. Further, sensory processing subtypes predicted communication competence and maladaptive behavior. The findings of this study lay the foundation for the generation of more specific hypotheses regarding the mechanisms of sensory processing dysfunction in autism, and support the continued use of sensory-based interventions in the remediation of communication and behavioral difficulties in autism.
Effects of nutritional conditions around weaning on carbonic anhydrase activity in the parotid gland and ruminal and abomasal epithelia of Holstein calves

Thirty-two male Holstein calves were used to investigate the effects of nutritional conditions around weaning and aging on carbonic anhydrase (CA) activity in the parotid gland and epithelium from the rumen and abomasum. We fed calf starter and lucerne hay as well as milk replacer (group N) or fed milk replacer either with (group S) or without (group M) administration of short-chain fatty acids (SCFA) through polypropylene tubing into the forestomach until 13 weeks of age. The diets were fed at 1000 hours and 1600 hours, and SCFA were administrated after milk replacer feeding at 1600 hours. Slaughter and tissue sampling were carried out between 1300 hours and 1430 hours at 1, 3, 7, 13, and 18 weeks of age. Tissue samples from five adult (1.5–2.0 years-old) Holstein steers were obtained from a local abattoir. In group N, CA activity in the parotid gland gradually and significantly increased toward the adult value, whilst in the epithelium from the rumen and abomasum, adult values were reached at 3 and 7 weeks of age, respectively. At 13 weeks, the activity for group N was significantly higher than that for the other two groups in the parotid gland, but there was no significant difference in the epithelium from the rumen and abomasum. The concentration of the carbonic isozyme VI in the parotid gland also changed with age but, in contrast to CA activity, had not reached adult levels by 13 weeks of age. In groups M and S, parotid saliva did not show any change toward an alkaline pH or toward a reciprocal change in the concentrations between Cl– and HCO3 –, even at 13 weeks of age. From these results we conclude that a concentrate-hay based diet around weaning has a crucial role in CA development in the parotid gland, but not in the epithelium of the rumen and abomasum.
The neurophysiology of dissociation and chronic disease.

Dissociation as a clinical psychiatric condition has been defined primarily in terms of the fragmentation and splitting of the mind, and perception of the self and the body. Its clinical manifestations include altered perceptions and behavior, including derealization, depersonalization, distortions of perception of time, space, and body, and conversion hysteria. Using examples of animal models, and the clinical features of the whiplash syndrome, we have developed a model of dissociation linked to the phenomenon of freeze/immobility. Also employing current concepts of the psychobiology of posttraumatic stress disorder (PTSD), we propose a model of PTSD linked to cyclical autonomic dysfunction, triggered and maintained by the laboratory model of kindling, and perpetuated by increasingly profound dorsal vagal tone and endorphinergic reward systems. These physiologic events in turn contribute to the clinical state of dissociation. The resulting autonomic dysregulation is presented as the substrate for a diverse group of chronic diseases of unknown origin.
An 8-bit QVGA AMOLED driver IC with a polynomial interpolation DAC

The paper proposes an 8-bit AMOLED driver IC with a polynomial interpolation DAC. This architecture maintains high-accuracy AMOLED panels with 8-bit compensated gamma correction and supporting low-complex configuration which results in additional occupied die area. The proposed driver consists of a 6-bit gamma correction resistor-string DAC and a 2-bit polynomial interpolation current-modulation sub-DAC. The two-stage DAC leads to a compact die size compared with conventional 8-bit resister-string DAC, and the polynomial interpolation method provides high accurate grey level voltages than linear one. The AMOLED driver was realized in 0.35-μm CMOS process with DNL and INL of 0.43 LSB and 0.43 LSB.
An Introduction to Programming for Bioscientists: A Python-Based Primer

Computing has revolutionized the biological sciences over the past several decades, such that virtually all contemporary research in molecular biology, biochemistry, and other biosciences utilizes computer programs. The computational advances have come on many fronts, spurred by fundamental developments in hardware, software, and algorithms. These advances have influenced, and even engendered, a phenomenal array of bioscience fields, including molecular evolution and bioinformatics; genome-, proteome-, transcriptome- and metabolome-wide experimental studies; structural genomics; and atomistic simulations of cellular-scale molecular assemblies as large as ribosomes and intact viruses. In short, much of post-genomic biology is increasingly becoming a form of computational biology. The ability to design and write computer programs is among the most indispensable skills that a modern researcher can cultivate. Python has become a popular programming language in the biosciences, largely because (i) its straightforward semantics and clean syntax make it a readily accessible first language; (ii) it is expressive and well-suited to object-oriented programming, as well as other modern paradigms; and (iii) the many available libraries and third-party toolkits extend the functionality of the core language into virtually every biological domain (sequence and structure analyses, phylogenomics, workflow management systems, etc.). This primer offers a basic introduction to coding, via Python, and it includes concrete examples and exercises to illustrate the language's usage and capabilities; the main text culminates with a final project in structural bioinformatics. A suite of Supplemental Chapters is also provided. Starting with basic concepts, such as that of a "variable," the Chapters methodically advance the reader to the point of writing a graphical user interface to compute the Hamming distance between two DNA sequences.
Assembly HVDC Breaker for HVDC Grids With Modular Multilevel Converters

The modular multilevel converter (MMC) with half-bridge submodules (SMs) is the most promising technology for high-voltage direct current (HVDC) grids, but it lacks dc fault clearance capability. There are two main methods to handle the dc-side short-circuit fault. One is to employ the SMs that have dc fault clearance capability, but the power losses are high and the converter has to be blocked during the clearance. The other is to employ the hybrid HVDC breakers. The breaker is capable of interrupting fault current within 5 ms, but this technology is not cost effective, especially in meshed HVDC grids. In this paper, an assembly HVDC breaker and the corresponding control strategy are proposed to overcome these drawbacks. The assembly HVDC breaker consists of an active short-circuit breaker (ASCB), a main mechanical disconnector, a main breaker, and an accessory discharging switch (ADS). When a dc-side short-circuit fault occurs, the ASCB and the ADS close immediately to shunt the fault current. The main breaker opens after a short delay to isolate the faulted line from the system and then the mechanical disconnector opens. With the disconnector in open position, the ASCB opens and breaks the current. The proposed breaker can handle the dc-side fault with competitively low cost, and the operating speed is fast enough. A model of a four-terminal monopolar HVDC grid is developed in Power Systems Computer Aided Design / Electromagnetic Transients including DC, and the simulation result proves the validity and the feasibility of the proposed solution.
The Discrete Shearlet Transform: A New Directional Transform and Compactly Supported Shearlet Frames

It is now widely acknowledged that analyzing the intrinsic geometrical features of the underlying image is essential in many applications including image processing. In order to achieve this, several directional image representation schemes have been proposed. In this paper, we develop the discrete shearlet transform (DST) which provides efficient multiscale directional representation and show that the implementation of the transform is built in the discrete framework based on a multiresolution analysis (MRA). We assess the performance of the DST in image denoising and approximation applications. In image approximations, our approximation scheme using the DST outperforms the discrete wavelet transform (DWT) while the computational cost of our scheme is comparable to the DWT. Also, in image denoising, the DST compares favorably with other existing transforms in the literature.
Visualization of Neural Network Predictions for Weather Forecasting

Recurrent neural networks are prime candidates for learning evolutions in multi-dimensional time series data. The performance of such a network is judged by the loss function, which is aggregated into a scalar value that decreases during training. Observing only this number hides the variation that occurs within the typically large training and testing data sets. Understanding these variations is of highest importance to adjust network hyperparameters, such as the number of neurons, number of layers or to adjust the training set to include more representative examples. In this paper, we design a comprehensive and interactive system that allows users to study the output of recurrent neural networks on both the complete training data and testing data. We follow a coarse-to-fine strategy, providing overviews of annual, monthly and daily patterns in the time series and directly support a comparison of different hyperparameter settings. We applied our method to a recurrent convolutional neural network that was trained and tested on 25 years of climate data to forecast meteorological attributes, such as temperature, pressure and wind velocity. We further visualize the quality of the forecasting models, when applied to various locations on Earth and we examine the combination of several forecasting models. This is the authors preprint. The definitive version is available at http://diglib.eg.org/ and http://onlinelibrary.wiley.com/.
Congestion control for high bandwidth-delay product networks

Theory and experiments show that as the per-flow product of bandwidth and latency increases, TCP becomes inefficient and prone to instability, regardless of the queuing scheme. This failing becomes increasingly important as the Internet evolves to incorporate very high-bandwidth optical links and more large-delay satellite links.To address this problem, we develop a novel approach to Internet congestion control that outperforms TCP in conventional environments, and remains efficient, fair, scalable, and stable as the bandwidth-delay product increases. This new eXplicit Control Protocol, XCP, generalizes the Explicit Congestion Notification proposal (ECN). In addition, XCP introduces the new concept of decoupling utilization control from fairness control. This allows a more flexible and analytically tractable protocol design and opens new avenues for service differentiation.Using a control theory framework, we model XCP and demonstrate it is stable and efficient regardless of the link capacity, the round trip delay, and the number of sources. Extensive packet-level simulations show that XCP outperforms TCP in both conventional and high bandwidth-delay environments. Further, XCP achieves fair bandwidth allocation, high utilization, small standing queue size, and near-zero packet drops, with both steady and highly varying traffic. Additionally, the new protocol does not maintain any per-flow state in routers and requires few CPU cycles per packet, which makes it implementable in high-speed routers.
Deep Face Recognition with Center Invariant Loss

Convolutional Neural Networks (CNNs) have been widely used for face recognition and got extraordinary performance with large number of available face images of different people. However, it is hard to get uniform distributed data for all people. In most face datasets, a large proportion of people have few face images. Only a small number of people appear frequently with more face images. These people with more face images have higher impact on the feature learning than others. The imbalanced distribution leads to the difficulty to train a CNN model for feature representation that is general for each person, instead of mainly for the people with large number of face images. To address this challenge, we proposed a center invariant loss which aligns the center of each person to enforce the learned features to have a general representation for all people. The center invariant loss penalizes the difference between each center of classes. With center invariant loss, we can train a robust CNN that treats each class equally regardless the number of class samples. Extensive experiments demonstrate the effectiveness of the proposed approach. We achieve state-of-the-art results on LFW and YTF datasets.
Testing advanced driver assistance systems using multi-objective search and neural networks

Recent years have seen a proliferation of complex Advanced Driver Assistance Systems (ADAS), in particular, for use in autonomous cars. These systems consist of sensors and cameras as well as image processing and decision support software components. They are meant to help drivers by providing proper warnings or by preventing dangerous situations. In this paper, we focus on the problem of design time testing of ADAS in a simulated environment. We provide a testing approach for ADAS by combining multi-objective search with surrogate models developed based on neural networks. We use multi-objective search to guide testing towards the most critical behaviors of ADAS. Surrogate modeling enables our testing approach to explore a larger part of the input search space within limited computational resources. We characterize the condition under which the multi-objective search algorithm behaves the same with and without surrogate modeling, thus showing the accuracy of our approach. We evaluate our approach by applying it to an industrial ADAS system. Our experiment shows that our approach automatically identifies test cases indicating critical ADAS behaviors. Further, we show that combining our search algorithm with surrogate modeling improves the quality of the generated test cases, especially under tight and realistic computational resources.
Clinical implications of genome-wide DNA methylation studies in acute myeloid leukemia

Acute myeloid leukemia (AML) is the most common type of acute leukemia in adults. AML is a heterogeneous malignancy characterized by distinct genetic and epigenetic abnormalities. Recent genome-wide DNA methylation studies have highlighted an important role of dysregulated methylation signature in AML from biological and clinical standpoint. In this review, we will outline the recent advances in the methylome study of AML and overview the impacts of DNA methylation on AML diagnosis, treatment, and prognosis.