Miracle-dz/UniFAR-FTUs huggingface dataset:
Formation and Expression of Pseudogenes on the B Chromosome of Rye[W][OPEN]

In contrast with the prevalent view that supernumerary B chromosomes do not contain genes, this work demonstrates that the B chromosome of rye contributes to the transcriptome and also affects in trans the activity of standard A chromosome–located genes. The authors propose a model for the evolution of B chromosome–located pseudogenes. B chromosomes (Bs) are dispensable components of the genomes of numerous species. In contrast with the prevalent view that Bs do not harbor genes, our recent sequence analysis revealed that Bs of rye (Secale cereale) are rich in gene-derived sequences. We compared these gene-like fragments of the rye B with their ancestral A-located counterparts and confirmed an A chromosomal origin and the pseudogenization of B-located gene-like fragments. About 15% of the pseudogene-like fragments on Bs are transcribed in a tissue-type and genotype-specific manner. In addition, B-located sequences can cause in trans down- or upregulation of A chromosome–encoded genic fragments. Phenotypes and effects associated with the presence of Bs might be explained by the activity of B-located pseudogenes. We propose a model for the evolution of B-located pseudogenes.
Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded Applications

In the age of Internet of Things (IoT), embedded devices ranging from ARM Cortex M0s with hundreds of KB of RAM to Arduinos with 2KB RAM are expected to perform increasingly sophisticated classification tasks, such as voice and gesture recognition, activity tracking, and biometric security. While convolutional neural networks (CNNs), together with spectrogram preprocessing, are a natural solution to many of these classification tasks, storage of the network’s activations often exceeds the hard memory constraints of embedded platforms. This paper presents memoryoptimal direct convolutions as a way to push classification accuracy as high as possible given strict hardware memory constraints at the expense of extra compute. We therefore explore the opposite end of the compute-memory trade-off curve from standard approaches that minimize latency. We validate the memory-optimal CNN technique with an Arduino implementation of the 10-class MNIST classification task, fitting the network specification, weights, and activations entirely within 2KB SRAM and achieving a state-of-theart classification accuracy for small-scale embedded systems of 99.15%.
Why Does Code Review Work for Open Source Software Communities?

Open source software communities have demonstrated that they can produce high quality results. The overall success of peer code review, commonly used in open source projects, has likely contributed strongly to this success. Code review is an emotionally loaded practice, with public exposure of reputation and ample opportunities for conflict. We set off to ask why code review works for open source communities, despite this inherent challenge. We interviewed 21 open source contributors from four communities and participated in meetings of ROS community devoted to implementation of the code review process. It appears that the hacker ethic is a key reason behind the success of code review in FOSS communities. It is built around the ethic of passion and the ethic of caring. Furthermore, we observed that tasks of code review are performed with strong intrinsic motivation, supported by many non-material extrinsic motivation mechanisms, such as desire to learn, to grow reputation, or to improve one's positioning on the job market. In the paper, we describe the study design, analyze the collected data and formulate 20 proposals for how what we know about hacker ethics and human and social aspects of code review, could be exploited to improve the effectiveness of the practice in software projects.
Detection of a Smartphone User's Distraction Based on Typing and Touch Gestures

Distraction influences how we experience user interfaces and perform tasks as it increases our workload. It can be caused by internal or external factors. However, it is difficult to measure it in everyday life situations. Smartphones might solve this problem. To examine correlations between smartphone features and distraction, we conducted a user study with 20 participants. They had to (1) type text to describe a picture and (2) swipe and tap to select images from a gallery, either with or without distraction. Ground truth data (NASA-TLX, task completion time, eye pupil diameter) confirmed a successful distraction. Distraction went along with significant changes in touch and typing behavior. That is, participants tended to type slower (higher inter-key interval), made more typing errors (higher keystroke per character), and swiped slower (lower swiping speed). We conclude that a detection of distraction is possible, but only relative to a basic or previous user behavior that needs to be known.
Demystifying Dropout

Dropout is a popular technique to train large-scale deep neural networks to alleviate the overﬁtting problem. To disclose the underlying reason for its gain, numerous works have tried to explain it from different perspectives. In this paper, unlike existing works, we explore it from a new perspective to provide new insight into this line of research. In detail, we disentangle the forward and backward pass of dropout. Then, we ﬁnd that these two passes need different levels of noise to improve the generalization performance of deep neural networks. Based on this observation, we propose the augmented dropout, which employs different dropping strategies in the forward and backward pass, to improve the standard dropout. Experimental results have veriﬁed the effectiveness of our proposed method.
Ladder Capsule Network

We propose a new architecture of the capsule network called the ladder capsule network, which has an alternative building block to the dynamic routing algorithm in the capsule network (Sabour et al., 2017). Motivated by the need for using only important capsules during training for robust performance, we first introduce a new layer called the pruning layer, which removes irrelevant capsules. Based on the selected capsules, we construct higher-level capsule outputs. Subsequently, to capture the part-whole spatial relationships, we introduce another new layer called the ladder layer, the outputs of which are regressed lower-level capsule outputs from higher-level capsules. Unlike the capsule network adopting the routing-by-agreement, the ladder capsule network uses backpropagation from a loss function to reconstruct the lower-level capsule outputs from higher-level capsules; thus, the ladder layer implements the reverse directional inference of the agreement/disagreement mechanism of the capsule network. The experiments on MNIST demonstrate that the ladder capsule network learns an equivariant representation and improves the capability to extrapolate or generalize to pose variations.
Learning Bilingual Sentiment-Specific Word Embeddings without Cross-lingual Supervision

Word embeddings learned in two languages can be mapped to a common space to produce Bilingual Word Embeddings (BWE). Unsupervised BWE methods learn such a mapping without any parallel data. However, these methods are mainly evaluated on tasks of word translation or word similarity. We show that these methods fail to capture the sentiment information and do not perform well enough on cross-lingual sentiment analysis. In this work, we propose UBiSE (Unsupervised Bilingual Sentiment Embeddings), which learns sentiment-specific word representations for two languages in a common space without any cross-lingual supervision. Our method only requires a sentiment corpus in the source language and pretrained monolingual word embeddings of both languages. We evaluate our method on three language pairs for cross-lingual sentiment analysis. Experimental results show that our method outperforms previous unsupervised BWE methods and even supervised BWE methods. Our method succeeds for a distant language pair English-Basque.
Object-Based Land Cover Classification of Cork Oak Woodlands using UAV Imagery and Orfeo ToolBox

This paper investigates the reliability of free and open-source algorithms used in the geographical object-based image classification (GEOBIA) of very high resolution (VHR) imagery surveyed by unmanned aerial vehicles (UAVs). UAV surveys were carried out in a cork oak woodland located in central Portugal at two different periods of the year (spring and summer). Segmentation and classification algorithms were implemented in the Orfeo ToolBox (OTB) configured in the QGIS environment for the GEOBIA process. Image segmentation was carried out using the Large-Scale Mean-Shift (LSMS) algorithm, while classification was performed by the means of two supervised classifiers, random forest (RF) and support vector machines (SVM), both of which are based on a machine learning approach. The original, informative content of the surveyed imagery, consisting of three radiometric bands (red, green, and NIR), was combined to obtain the normalized difference vegetation index (NDVI) and the digital surface model (DSM). The adopted methodology resulted in a classification with higher accuracy that is suitable for a structurally complex Mediterranean forest ecosystem such as cork oak woodlands, which are characterized by the presence of shrubs and herbs in the understory as well as tree shadows. To improve segmentation, which significantly affects the subsequent classification phase, several tests were performed using different values of the range radius and minimum region size parameters. Moreover, the consistent selection of training polygons proved to be critical to improving the results of both the RF and SVM classifiers. For both spring and summer imagery, the validation of the obtained results shows a very high accuracy level for both the SVM and RF classifiers, with kappa coefficient values ranging from 0.928 to 0.973 for RF and from 0.847 to 0.935 for SVM. Furthermore, the land cover class with the highest accuracy for both classifiers and for both flights was cork oak, which occupies the largest part of the study area. This study shows the reliability of fixed-wing UAV imagery for forest monitoring. The study also evidences the importance of planning UAV flights at solar noon to significantly reduce the shadows of trees in the obtained imagery, which is critical for classifying open forest ecosystems such as cork oak woodlands.
On the Convergence and Robustness of Adversarial Training

Improving the robustness of deep neural networks (DNNs) to adversarial examples is an important yet challenging problem for secure deep learning. Across existing defense techniques, adversarial training with Projected Gradient Decent (PGD) is amongst the most effective. Adversarial training solves a min-max optimization problem, with the inner maximization generating adversarial examples by maximizing the classification loss, and the outer minimization finding model parameters by minimizing the loss on adversarial examples generated from the inner maximization. A criterion that measures how well the inner maximization is solved is therefore crucial for adversarial training. In this paper, we propose such a criterion, namely First-Order Stationary Condition for constrained optimization (FOSC), to quantitatively evaluate the convergence quality of adversarial examples found in the inner maximization. With FOSC, we find that to ensure better robustness, it is essential to use adversarial examples with better convergence quality at the later stages of training. Yet at the early stages, high convergence quality adversarial examples are not necessary and may even lead to poor robustness. Based on these observations, we propose a dynamic training strategy to gradually increase the convergence quality of the generated adversarial examples, which significantly improves the robustness of adversarial training. Our theoretical and empirical results show the effectiveness of the proposed method.
Isolation and characterization of uncoupler-resistant mutants of Bacillus subtilis

Three mutant strains of Bacillus subtilis were isolated on the basis of their ability to grow in the presence of 5 microM carbonyl cyanide m-chlorophenylhydrazone (CCCP). The mutants (AG2A, AG1A3, and AG3A) were also resistant to 2,4-dinitrophenol, and AG2A exhibited resistance to tributyltin and neomycin. The mutants all exhibited (i) elevated levels of membrane ATPase activity relative to the wild type; (ii) slightly elevated respiratory rates, with the cytochrome contents of the membranes being the same as or slightly lower than those of the wild type; (3) a passive membrane permeability to protons that was indistinguishable from that of the wild type in the absence of CCCP and that was increased by addition of CCCP to the same extent as observed with the wild type; and (4) an enhanced sensitivity to valinomycin with respect to the ability of the ionophore to reduce the transmembrane electrical potential. Finally and importantly, starved whole cells of all the mutants synthesized more ATP than the wild type did upon energization in the presence of any one of several agents that lowered the proton motive force. Studies of revertants indicated that the phenotype resulted from a single mutation. Since a mutation in the coupling membrane might produce such pleiotropic effects, an analysis of the membrane lipids was undertaken with preparations made from cells grown in the absence of CCCP. The membrane lipids of the uncoupler-resistant strains differed from those of the wild type in having reduced amounts of monounsaturated C16 fatty acids and increased ratios of iso/anteiso branches on the C15 fatty acids. Correlations between protonophore resistance and the membrane lipid compositions of the wild type, mutants, and revertants were most consistent with the hypothesis that a reduction in the content of monounsaturated C16 fatty acids in the membrane phospholipids is related, perhaps casually, to the ability to synthesize ATP at low bulk transmembrane electrochemical gradients of protons.
Demand-Driven Refinement of Points-to Analysis

We present DynaSens, a demand-driven approach to points-to analysis that uses slicing to automatically adjust the analysis' context-sensitivity. Within a points-to analysis, heap-carried data flows are composed of loads and stores, and these heap-carried dependences are difficult to resolve. Having observed the limitations of existing techniques, we propose a slicing analysis based on a demand-driven approach to resolve such dependences. Given a points-to query, a collection of relevant program elements is identified by the slicing analysis and handled context-sensitively by the points-to analysis. We compare the precision and cost of our points-to analysis against two state-of-the-art uniformly context-sensitive analyses that achieve the best trade between cost and precision to date. Evaluation results shows the points-to analysis refined by the slicing analysis achieves higher precision in most tests than the uniformly context-sensitive analyses, which are many times more costly.
Enhancing Key-Value Memory Neural Networks for Knowledge Based Question Answering

Traditional Key-value Memory Neural Networks (KV-MemNNs) are proved to be effective to support shallow reasoning over a collection of documents in domain specific Question Answering or Reading Comprehension tasks. However, extending KV-MemNNs to Knowledge Based Question Answering (KB-QA) is not trivia, which should properly decompose a complex question into a sequence of queries against the memory, and update the query representations to support multi-hop reasoning over the memory. In this paper, we propose a novel mechanism to enable conventional KV-MemNNs models to perform interpretable reasoning for complex questions. To achieve this, we design a new query updating strategy to mask previously-addressed memory information from the query representations, and introduce a novel STOP strategy to avoid invalid or repeated memory reading without strong annotation signals. This also enables KV-MemNNs to produce structured queries and work in a semantic parsing fashion. Experimental results on benchmark datasets show that our solution, trained with question-answer pairs only, can provide conventional KV-MemNNs models with better reasoning abilities on complex questions, and achieve state-of-art performances.
Supervised Tie Breaking in Test Case Prioritization

Test case prioritization reorders sequences of test cases with the aim of increasing the rate at which faults can be detected. Most existing prioritization techniques employ coverage information gathered on previous test case executions to rank test cases. Existing studies in the literature, however, show that there is a high chance that "ties" occur during the prioritization procedure when using coverage-based techniques; that is, there is a high chance that cases will occur in which two or more candidate test cases have identical code coverage behaviors. To break such ties, most techniques resort to random re-ordering of test cases, which can degrade the rate of fault detection. In this work, we use an ensemble of defect prediction models to guide prioritization techniques towards breaking such ties by re-ordering test cases in terms of the likelihood that they will cover fault-prone units of code.
The search for authenticity

In the popular imagination skinhead identity has come to be inextricably connected to a white-racist identity. This article explores this tenet through an ethnographic exploration of an online skinhead newsgroup, a milieu where racial markings are seemingly absent. The empirical findings expose that ‘racism’ is read ambivalently by the newsgroup's skinheads. ‘Racism’ is not viewed as a constituting component of skinhead identity; however, there is widespread commitment to a ‘white identity’. This article concentrates on the processes which give rise to a digitalized (white) skinhead identity, (re)established online in and through textual performances. Narratives of whiteness articulated through the node of skinness, reveal the salience of racial bodies in the virtual world. However, the imagined relationship between skinheads and racism is not straightforward. The skinheads of this research do not enact an explicit discriminatory racism, but rather they imagine whiteness as a performative condition of skinness, a notion that necessitates a figurative (and literal) aggressive relation to ‘otherness’.
Degradation of Surface Passivation on Crystalline Silicon and Its Impact on Light-Induced Degradation Experiments

A decrease of surface passivation quality is observed in FZ, Cz, and mc-Si lifetime samples during light-induced degradation (LID) treatments. It is shown that this degradation occurs not only in samples with single SiN<italic><sub> x</sub></italic>:H layers but also when using layer stacks consisting of SiO<italic><sub>x</sub></italic>/SiN <italic><sub>x</sub></italic>:H or AlO<italic><sub>x</sub></italic>:H/SiN<italic><sub>x</sub></italic>:H. Time-resolved calculation of the surface saturation current density <italic>J</italic><sub>0</sub><italic><sub>s</sub> </italic> is shown to be a reliable method to separate changes in the bulk and at the surface of samples during LID treatments. The impact of the observed changes in passivation quality on the outcome and interpretation of LID experiments aiming at changes in the bulk of Cz or mc-Si is investigated and discussed.
Air pollution and cardiovascular admissions association in Spain: results within the EMECAS project

Objective: To evaluate the short term effect of air pollution on cardiovascular admissions in 14 Spanish cities Methods: The period under study was from 1995 to 1999. Daily emergency admissions for all cardiovascular diseases (CVD) and heart diseases (HD) were obtained from hospital records, and the corresponding daily levels of particulates, SO2, NO2, CO, and ozone were recorded. The magnitude of association was estimated using Poisson generalised additive models controlling for confounding and overdispersion. For each cause, lagged effects, up to three days, of each pollutant were examined and combined estimates were obtained. For ozone the analyses were restricted to the warm period. One and two pollutant models were performed. Results: Associations were more consistent in lag 0 (concurrent day) and 1 (lag 0–1), except in the case of ozone where there was a more delayed relation (lag 2–3). For combined estimates an increase of 10 μg/m3 in the PM10 levels in lag 0–1 was associated with an increase of 0.9% (95% CI: 0.4 to 1.5%) in the number of hospital admissions for CVD, and 1.6% (0.8 to 2.3%) for HD. For ozone the corresponding estimates for lag 2–3 were 0.7% (0.3 to 1.0) for CVD, and 0.7% (0.1 to 1.2) for HD. An increase of 1 mg/m3 in CO levels was associated with an increase of 2.1% (0.7 to 3.5%) in CVD admissions, and 4.2% (1.3 to 7.1%) in HD admissions. SO2 and NO2 estimates were more sensitive in two pollutant models Conclusions: A short term association between increases in daily levels of air pollutants and the number of daily admissions for cardiovascular diseases, with specificity for heart diseases, has been described in Spanish cities.
Agree-and-Prove: Generalized Proofs Of Knowledge and Applications

Proofs of knowledge (PoK) are one of the most fundamental notions in cryptography and have been used as a building block in numerous applications. The appeal of this notion is that it is parameterized by generic relations which an application can suitably instantiate. On the other hand, in many applications, a more generalized proof system would be desirable that captures aspects not considered by the low-level abstraction boundary of PoKs. First, the context in which the protocol is executed is encoded using a static auxiliary input, which is insuﬃcient to represent a world with more dynamic setup, or even the case where the relation to be proven does depend on a setup. Second, proofs of knowledge do by deﬁnition not take into account the statement derivation process. Yet, it often impacts either the complexity of the associated interactive proof or the eﬀective zero-knowledge guarantees that can still be provided by the proof system. Some of this critique has been observed and partially addressed by Bernhard et al. (PKC’15), who consider PoK in the presence of a random oracle, and Choudhuri et al. (Eurocrypt’19), who need PoK schemes in the presence of a ledger functionality. However, the theoretical foundation of a generalized notion of PoK with setup-dependent relations is still missing. As a ﬁrst contribution, we introduce this new notion and call it agree-and-proof . Agree-and-prove rigorously extends the basic PoK framework to include the missing aspects. The new notion provides clear semantics of correctness, soundness, and zero-knowledge in the presence of generic setup and under dynamic statement derivation. As a second contribution, we show that the agree-and-prove notion is the natural abstraction for applications that are in fact generalized PoKs, but for which the existing isolated notions do not reveal this intrinsic connection. First, we consider proofs-of-ownership of ﬁles for client-side ﬁle deduplication. We cast the problem and some of its prominent schemes in our agree-and-prove framework and formally analyze their security. Finally, leveraging our generalized zero-knowledge formalization, we devise a novel scheme that is provably the privacy-preserving analogon of the known Merkle-Tree based proof-of-ownership protocol. As a second application, we consider entity authentication and two-factor authentication. We thereby demonstrate that the agree-and-prove notion can not only phrase generalized PoKs, but also, along the same lines, proofs of possession or ability, such as proving the correct usage of a hardware token.