Miracle-dz/UniFAR-FTUs huggingface dataset:
ON RANDOM TOMOGRAPHY WITH UNOBSERVABLE PROJECTION

We formulate and investigate a statistical inverse problem of a random tomographic nature, where a probability density function on R3 is to be recovered from observation of finitely many of its two-dimensional projections in random and unobservable directions. Such a problem is distinct from the classic problem of tomography where both the projections and the unit vectors normal to the projection plane are observable. The problem arises in single particle electron microscopy, a powerful method that biophysicists employ to learn the structure of biological macromolecules. Strictly speaking, the problem is unidentifiable and an appropriate reformulation is suggested hinging on ideas from Kendall’s theory of shape. Within this setup, we demonstrate that a consistent solution to the problem may be derived, without attempting to estimate the unknown angles, if the density is assumed to admit a mixture representation.
Comparative analysis of 1:1:2 and 1:2:2 FinFET SRAM bit-cell using assist circuit

Read and write yields of 6σ are achieved in 1:1:2 and 1:2:2 FinFET SRAM bit-cells using a negative bit-line write assist circuit and a suppressed word-line read assist circuit, respectively. These two bit-cells are compared in terms of read delay and leakage current. In spite of a smaller cell current, 1:1:2 bit-cell achieves 27.7% smaller read delay than 1:2:2 bit-cell due to a smaller bit-line capacitance. 1:1:2 bit-cell has a smaller leakage current due to the smaller fin number of pass gate transistors, but the difference is just 0.5% because of a larger Vth variation.
Why developers cannot embed privacy into software systems?: An empirical investigation

Pervasive use of software applications continue to challenge user privacy when users interact with software systems. Even though privacy practices such as Privacy by Design (PbD), have clear instructions for software developers to embed privacy into software designs, those practices are yet to become a common practice among software developers. The difficulty of developing privacy preserving software systems highlights the importance of investigating software developers and the problems they face when they are asked to embed privacy into application designs. Software developers are the community who can put practices such as PbD into action. Therefore identifying the problems they face when embedding privacy into software applications and providing solutions to those problems are important to enable the development of privacy preserving software systems. This study investigates 36 software developers in a software design task with instructions to embed privacy in order to identify the problems they face. We derive recommendation guidelines to address the problems to enable the development of privacy preserving software systems.
The Coming Era of AlphaHacking?: A Survey of Automatic Software Vulnerability Detection, Exploitation and Patching Techniques

With the success of the Cyber Grand Challenge (CGC) sponsored by DARPA, the topic of Autonomous Cyber Reasoning System (CRS) has recently attracted extensive attention from both industry and academia. Utilizing automated system to detect, exploit and patch software vulnerabilities seems so attractive because of its scalability and cost-efficiency compared with the human expert based solution. In this paper, we give an extensive survey of former representative works related to the underlying technologies of a CRS, including vulnerability detection, exploitation and patching. As an important supplement, we then review several pioneer studies that explore the potential of machine learning technologies in this field, and point out that the future development of Autonomous CRS is inseparable from machine learning.
Fast Neural Machine Translation Implementation

This paper describes the submissions to the efficiency track for GPUs at the Workshop for Neural Machine Translation and Generation by members of the University of Edinburgh, Adam Mickiewicz University, Tilde and University of Alicante. We focus on efficient implementation of the recurrent deep-learning model as implemented in Amun, the fast inference engine for neural machine translation. We improve the performance with an efficient mini-batching algorithm, and by fusing the softmax operation with the k-best extraction algorithm. Submissions using Amun were first, second and third fastest in the GPU efficiency track.
Colorectal cancer screening for average‐risk adults: 2018 guideline update from the American Cancer Society

In the United States, colorectal cancer (CRC) is the fourth most common cancer diagnosed among adults and the second leading cause of death from cancer. For this guideline update, the American Cancer Society (ACS) used an existing systematic evidence review of the CRC screening literature and microsimulation modeling analyses, including a new evaluation of the age to begin screening by race and sex and additional modeling that incorporates changes in US CRC incidence. Screening with any one of multiple options is associated with a significant reduction in CRC incidence through the detection and removal of adenomatous polyps and other precancerous lesions and with a reduction in mortality through incidence reduction and early detection of CRC. Results from modeling analyses identified efficient and model‐recommendable strategies that started screening at age 45 years. The ACS Guideline Development Group applied the Grades of Recommendations, Assessment, Development, and Evaluation (GRADE) criteria in developing and rating the recommendations. The ACS recommends that adults aged 45 years and older with an average risk of CRC undergo regular screening with either a high‐sensitivity stool‐based test or a structural (visual) examination, depending on patient preference and test availability. As a part of the screening process, all positive results on noncolonoscopy screening tests should be followed up with timely colonoscopy. The recommendation to begin screening at age 45 years is a qualified recommendation. The recommendation for regular screening in adults aged 50 years and older is a strong recommendation. The ACS recommends (qualified recommendations) that: 1) average‐risk adults in good health with a life expectancy of more than 10 years continue CRC screening through the age of 75 years; 2) clinicians individualize CRC screening decisions for individuals aged 76 through 85 years based on patient preferences, life expectancy, health status, and prior screening history; and 3) clinicians discourage individuals older than 85 years from continuing CRC screening. The options for CRC screening are: fecal immunochemical test annually; high‐sensitivity, guaiac‐based fecal occult blood test annually; multitarget stool DNA test every 3 years; colonoscopy every 10 years; computed tomography colonography every 5 years; and flexible sigmoidoscopy every 5 years. CA Cancer J Clin 2018;68:250–281. © 2018 American Cancer Society.
Adaptive Network Sparsification with Dependent Variational Beta-Bernoulli Dropout

While variational dropout approaches have been shown to be effective for network sparsification, they are still suboptimal in the sense that they set the dropout rate for each neuron without consideration of the input data. With such input-independent dropout, each neuron is evolved to be generic across inputs, which makes it difficult to sparsify networks without accuracy loss. To overcome this limitation, we propose adaptive variational dropout whose probabilities are drawn from sparsity-inducing beta Bernoulli prior. It allows each neuron to be evolved either to be generic or specific for certain inputs, or dropped altogether. Such input-adaptive sparsity-inducing dropout allows the resulting network to tolerate larger degree of sparsity without losing its expressive power by removing redundancies among features. We validate our dependent variational beta-Bernoulli dropout on multiple public datasets, on which it obtains significantly more compact networks than baseline methods, with consistent accuracy improvements over the base networks.
Cancelable speaker verification system based on binary Gaussian mixtures

Biometrie systems suffer from non-revocabilty. In this paper, we propose a cancelable speaker verification system based on classical Gaussian Mixture Models (GMM) methodology enriched with the desired characteristics of revocability and privacy. The GMM model is transformed into a binary vector that is used by a shuffling scheme to generate a cancelable template and to guarantee the cancelabilty of the overall system. Leveraging the shuffling scheme, the speaker model can be revoked and another model can be reissued. Our proposed method enables the generation of multiple cancelable speaker templates from the same biometric modality that cannot be linked to the same user. The proposed system is evaluated on the RSR2015 databases. It outperforms the basic GMM system and experimentations show significant improvement in the speaker verification performance that achieves an Equal Error Rate (ERR) of 0.01%.
A Long Way to the Top: Significance, Structure, and Stability of Internet Top Lists

A broad range of research areas including Internet measurement, privacy, and network security rely on lists of target domains to be analysed; researchers make use of target lists for reasons of necessity or efficiency. The popular Alexa list of one million domains is a widely used example. Despite their prevalence in research papers, the soundness of top lists has seldom been questioned by the community: little is known about the lists' creation, representativity, potential biases, stability, or overlap between lists. In this study we survey the extent, nature, and evolution of top lists used by research communities. We assess the structure and stability of these lists, and show that rank manipulation is possible for some lists. We also reproduce the results of several scientific studies to assess the impact of using a top list at all, which list specifically, and the date of list creation. We find that (i) top lists generally overestimate results compared to the general population by a significant margin, often even an order of magnitude, and (ii) some top lists have surprising change characteristics, causing high day-to-day fluctuation and leading to result instability. We conclude our paper with specific recommendations on the use of top lists, and how to interpret results based on top lists with caution.
PPP1R21 homozygous null variants associated with developmental delay, muscle weakness, distinctive facial features, and brain abnormalities

We present 3 children with homozygous null variants in the PPP1R21 gene. A 3‐year‐old girl had profound developmental delay, hypotonia and weakness, poor feeding, recurrent chest infections and respiratory failure, rotatory nystagmus, absent reflexes, and a homozygous nonsense variant c.2089C>T (p.Arg697*). A 2‐year‐old boy had profound developmental delay, weakness and hypotonia, recurrent chest infections and respiratory distress, undescended testes, rotatory nystagmus, hyporeflexia, and a homozygous nonsense variant c.427C>T (p.Arg143*). An 11‐year‐old girl with profound developmental delay, weakness and hypotonia, stereotypic movements, growth failure, hyporeflexia, and a homozygous frameshift variant c. 87_88delAG (p.Gly30Cysfs*4). In addition, these children shared common facial features (thick eyebrows, hypertelorism, broad nasal bridge, short nose with upturned nasal tip and broad low‐hanging columella, thick lips, low‐set ears, and coarse facies with excessive facial hair), and brain abnormalities (cerebellar vermis hypoplasia, ventricular dilatation, and reduced white matter volume). Although PPP1R21 has not yet been linked to human disease, the consistency in the phenotype of individuals from unrelated families, the nature of the variants which result in truncated proteins, and the expected vital role for PPP1R21 in cellular function, all support that PPP1R21 is a novel disease‐associated gene responsible for the phenotype observed in these individuals.
Chlamydia psittaci and C. avium in feral pigeon (Columba livia domestica) droppings in two cities in the Netherlands

Abstract Background: Feral pigeons (Columba livia domestica) live and breed in many city centres and contact with their droppings can be a hazard for human health if the birds carry Chlamydia psittaci. Objective: The aim of this study was to establish whether pigeon droppings in two Dutch cities (Utrecht and Haarlem) contain C. psittaci and/or C. avium, which could be a potential hazard for transmission to humans. Methods: In May 2017 seven feral pigeon ‘hot spots’ with between 5 and 40+ pigeons present were identified in two cities by visual observations over two days. During the following ten days fresh droppings were collected at these hot spots and the samples were pooled per three droppings to achieve 40–41 samples per city. Samples were analysed for Chlamydia DNA with a broad range 23S Chlamydiaceae Real-Time PCR and positive samples were tested with a specific C. psittaci and C. avium Real-Time PCR. Positive C. psittaci samples were genotyped. Results: C. psittaci and C. avium were detected in both cities. For C. psittaci the prevalences in Utrecht and Haarlem were 2.4% and 7.5%, respectively; for C. avium 36.6% and 20.0%, respectively. One sample contained both species. All C. psittaci samples belonged to genotype B. Conclusion: C. psittaci and C. avium are present in feral pigeon droppings in Utrecht and Haarlem. Human contact with droppings from infected pigeons or inhalation of dust from dried droppings represent a potential hazard to public health.
Towards Virtualization of Software-Defined Networks: Analysis, Modeling, and Optimization

Combining the paradigms Network Virtualization (NV) and Software-Defined Networking (SDN) is expected to increase adaptability and flexibility of communication networks like the Internet. However, the combination of NV and SDN demands new measurement procedures to reveal unpredictable operation as well as algorithms for faster provisioning of virtual networks. This thesis develops measurement tools, mathematical models, and Machine Learning-based optimization methods for combining NV and SDN.
Analyzing the Effect of Currency Strength Changes on their Future Price Movement

We consider the problem of predicting direction of movement of popular currencies in the foreign exchange (Forex) market. Technical analysis is an analysis methodology for forecasting the direction of prices through the study of past market data. A currency strength indicator is a metric showing the relative strength of currencies over a period of time. In this study, we analyzed the performance of a strength indicator in a different way. Based on the change in strength, we opened a basket of virtual trades and measured their profitability. Several tests have been performed and they show consistent profits. The results are encouraging and a profitable forex trading system can be made by only considering strength changes in currencies.
Amortized Context Vector Inference for Sequence-to-Sequence Networks

Neural attention (NA) has become a key component of sequence-to-sequence models that yield state-of-the-art performance in as hard tasks as abstractive document summarization (ADS) and video captioning (VC). NA mechanisms perform inference of context vectors; these constitute weighted sums of deterministic input sequence encodings, adaptively sourced over long temporal horizons. Inspired from recent work in the field of amortized variational inference (AVI), in this work we consider treating the context vectors generated by soft-attention (SA) models as latent variables, with approximate finite mixture model posteriors inferred via AVI. We posit that this formulation may yield stronger generalization capacity, in line with the outcomes of existing applications of AVI to deep networks. To illustrate our method, we implement it and experimentally evaluate it considering challenging ADS, VC, and MT benchmarks. This way, we exhibit its improved effectiveness over state-of-the-art alternatives.
Accelerating the Parallelization of Lattice Boltzmann Method by Exploiting the Temporal Locality

The lattice Boltzmann method (LBM) is a widely used solution for the computational fluid dynamics. It has the natural advantages of parallelization that several parallelizable nested loops in space dimensions are enclosed by the time steps. Most previous work focus on the DOALL parallelism and data reuse for cache optimization. This paper proposes an effective approach to accelerate the LBM computing by fully exploiting temporal locality on shared memory multicore platform. The approach uses loop transformations to obtain the implementation of D2Q9 model of LBM that achieves data reuses for cache in successive time steps and wave-front parallelism. Besides, a synchronization strategy based on POST/WAIT operations is presented to optimize the communication among parallel threads. On an Intel multicore platform, the implementation of the proposed approach outperformed the original parallel codes and a cache-based approach for different grid sizes, improving the performance by 11% and 9% on average, respectively.
Application of ontology in medical heterogeneous data integration

In the process of data integration, medical data is a huge obstacle to the development of medical information because of its complicated data type, large amount of data and semantic heterogeneity among different data sources. Integration of massive medical heterogeneous data is the urgent need to promote the process of medical information to be solved. According to the characteristics of medical data sources, an ontology-based medical heterogeneous data integration scheme is proposed. By introducing the medical similarity algorithm into the hybrid medical ontology, not only the multi-semantic problem is solved, but also the current heterogeneity of mass medical data is eliminated. Finally, experiments show that this method performs well in eliminating the semantic uncertainty of data in the medical field and can improve the integration efficiency of heterogeneous data in the medical data warehouse. It is practically feasible to solve the problem of medical heterogeneous data integration.
Multimodal Speaker Segmentation and Diarization using Lexical and Acoustic Cues via Sequence to Sequence Neural Networks

While there has been substantial amount of work in speaker diarization recently, there are few efforts in jointly employing lexical and acoustic information for speaker segmentation. Towards that, we investigate a speaker diarization system using a sequence-to-sequence neural network trained on both lexical and acoustic features. We also propose a loss function that allows for selecting not only the speaker change points but also the best speaker at any time by allowing for different speaker groupings. We incorporate Mel Frequency Cepstral Coefficients (MFCC) as an acoustic feature alongside lexical information that are obtained from conversations from the Fisher dataset. Thus, we show that acoustics provide complementary information to the lexical modality. The experimental results show that sequence-to-sequence system trained on both word sequences and MFCC can improve on speaker diarization result compared to the system that only relies on lexical modality or the baseline MFCC-based system. In addition, we test the performance of our proposed method with Automatic Speech Recognition (ASR) transcripts. While the performance on ASR transcripts drops, the Diarization Error Rate (DER) of our proposed method still outperforms the traditional method based on Bayesian Information Criterion (BIC).
Whole-transcriptome analysis reveals genetic factors underlying flowering time regulation in rapeseed (Brassica napus L.).

Rapeseed (Brassica napus L.), one of the most important sources of vegetable oil and protein-rich meals worldwide, is adapted to different geographical regions by modification of flowering time. Rapeseed cultivars have different day length and vernalization requirements, which categorize them into winter, spring, and semiwinter ecotypes. To gain a deeper insight into genetic factors controlling floral transition in B. napus, we performed RNA sequencing (RNA-seq) in the semiwinter doubled haploid line, Ningyou7, at different developmental stages and temperature regimes. The expression profiles of more than 54,000 gene models were compared between different treatments and developmental stages, and the differentially expressed genes were considered as targets for association analysis and genetic mapping to confirm their role in floral transition. Consequently, 36 genes with association to flowering time, seed yield, or both were identified. We found novel indications for neofunctionalization in homologs of known flowering time regulators like VIN3 and FUL. Our study proved the potential of RNA-seq along with association analysis and genetic mapping to identify candidate genes for floral transition in rapeseed. The candidate genes identified in this study could be subjected to genetic modification or targeted mutagenesis and genotype building to breed rapeseed adapted to certain environments.
An experimental comparison of label selection methods for hierarchical document clusters

The focus of this paper is on the evaluation of sixteen labeling methods for hierarchical document clusters over five datasets. All of the methods are independent from clustering algorithms, applied subsequently to the dendrogram construction and based on probabilistic dependence relations among labels and clusters. To reach a fair comparison as well as a standard benchmark, we rewrote and presented the labeling methods in a similar notation. The experimental results were analyzed through a proposed evaluation methodology based on: (i) data standardization before applying the cluster labeling methods and over the labeling results; (ii) a particular information retrieval process, using the obtained labels and their hierarchical relations to construct the search queries; (iii) evaluation of the retrieval process through precision, recall and F measure; (iv) variance analysis of the retrieval results to better understanding the differences among the labeling methods; and, (v) the emulation of a human judgment through the analysis of a topic observed coherence measure - normalized Pointwise Mutual Information (PMI). Applying the methodology, we are able to highlight the advantages of certain methods: to capture specific information; for a better document hierarchy comprehension at different levels of granularity; and, to capture the most coherent labels through the label selections. Finally, the experimental results demonstrated that the label selection methods which hardly consider hierarchical relations had the best results.
Data-Driven Formal Reasoning and Their Applications in Safety Analysis of Vehicle Autonomy Features

Analyzing the safety of automotive systems at the application level is -especially challenging as often very complex or even incomplete models are available. The authors combine formal reasoning with simulation data to effectively -validate safety or estimate worst case situations of automotive control systems. —Hans-Joachim Wunderlich, Universität Stuttgart