Miracle-dz/UniFAR-FTUs huggingface dataset:
Gene Expression Profiling of Transcription Factors of Helicobacter pylori under Different Environmental Conditions

Helicobacter pylori is a Gram-negative bacterium that colonizes the human gastric mucosa and causes peptic ulcers and gastric carcinoma. H. pylori strain 26695 has a small genome (1.67 Mb), which codes for few known transcriptional regulators that control bacterial metabolism and virulence. We analyzed by qRT-PCR the expression of 16 transcriptional regulators in H. pylori 26695, including the three sigma factors under different environmental conditions. When bacteria were exposed to acidic pH, urea, nickel, or iron, the sigma factors were differentially expressed with a particularly strong induction of fliA. The regulatory genes hrcA, hup, and crdR were highly induced in the presence of urea, nickel, and iron. In terms of biofilm formation fliA, flgR, hp1021, fur, nikR, and crdR were induced in sessile bacteria. Transcriptional expression levels of rpoD, flgR, hspR, hp1043, and cheY were increased in contact with AGS epithelial cells. Kanamycin, chloramphenicol, and tetracycline increased or decreased expression of regulatory genes, showing that these antibiotics affect the transcription of H. pylori. Our data indicate that environmental cues which may be present in the human stomach modulate H. pylori transcription.
Ultradense Word Embeddings by Orthogonal Transformation

Embeddings are generic representations that are useful for many NLP tasks. In this paper, we introduce DENSIFIER, a method that learns an orthogonal transformation of the embedding space that focuses the information relevant for a task in an ultradense subspace of a dimensionality that is smaller by a factor of 100 than the original space. We show that ultradense embeddings generated by DENSIFIER reach state of the art on a lexicon creation task in which words are annotated with three types of lexical information - sentiment, concreteness and frequency. On the SemEval2015 10B sentiment analysis task we show that no information is lost when the ultradense subspace is used, but training is an order of magnitude more efficient due to the compactness of the ultradense space.
Accelerated and Inexact Soft-Impute for Large-Scale Matrix and Tensor Completion

Matrix and tensor completion aim to recover a low-rank matrix / tensor from limited observations and have been commonly used in applications such as recommender systems and multi-relational data mining. A state-of-the-art matrix completion algorithm is Soft-Impute, which exploits the special “sparse plus low-rank” structure of the matrix iterates to allow efficient SVD in each iteration. Though Soft-Impute is a proximal algorithm, it is generally believed that acceleration destroys the special structure and is thus not useful. In this paper, we show that Soft-Impute can indeed be accelerated without comprising this structure. To further reduce the iteration time complexity, we propose an approximate singular value thresholding scheme based on the power method. Theoretical analysis shows that the proposed algorithm still enjoys the fast $O(1/T^2)$O(1/T2) convergence rate of accelerated proximal algorithms. We also extend the proposed algorithm to tensor completion with the scaled latent nuclear norm regularizer. We show that a similar “sparse plus low-rank” structure also exists, leading to low iteration complexity and fast $O(1/T^2)$O(1/T2) convergence rate. Besides, the proposed algorithm can be further extended to nonconvex low-rank regularizers, which have better empirical performance than the convex nuclear norm regularizer. Extensive experiments demonstrate that the proposed algorithm is much faster than Soft-Impute and other state-of-the-art matrix and tensor completion algorithms.
Realized Variance in the Presence of Non-Iid Microstructure Noise

It is a well known fact that at high sampling frequencies, the contamination of microstructure noise causes the Realized Variance to be a biased measure of the Integrated Variance. Recent developments in this field propose sampling on lower frequencies, sub-sampling techniques, or bias corrections using the autocorrelation patterns in the data. In this paper we propose a structural decomposition of the efficient price process and the microstructure noise. At the highest sampling frequency, we allow for potential correlation between the efficient price and the microstructure noise. For 20 actively traded stocks at Nasdaq, we find that the method provides a lower bound on Realized Variance. Applying a recently introduced bias correction reveals a very long persistence in transaction by transaction returns corrects the Realized Variance upwards to a level equal to low frequency Realized Variance. It remains questionable, however, whether this long persistence should be seen as microstructure noise, or is an inherent feature of the price process.
Disagreement About the Therapy, Not the Diagnosis: A Reply to the Rejoinders

ABSTRACT The article engages with the criticism of my suggestion to work towards synthesis and consilience (i.e., compatibility with natural science, especially cognitive science and evolutionary psychology) in strategic communication research. It notes that there seems to be agreement about the field’s unsatisfactory state, yet disagreement about the way forward. I attempt to demonstrate that none of the arguments developed by the critics breaks the case for synthesis and consilience, but I acknowledge that there is a pragmatic issue, namely a deeply rooted distrust of the natural sciences.
Clinical and Laboratory Features of the Nocardia spp. Based on Current Molecular Taxonomy

SUMMARY The recent explosion of newly described species of Nocardia results from the impact in the last decade of newer molecular technology, including PCR restriction enzyme analysis and 16S rRNA sequencing. These molecular techniques have revolutionized the identification of the nocardiae by providing rapid and accurate identification of recognized nocardiae and, at the same time, revealing new species and a number of yet-to-be-described species. There are currently more than 30 species of nocardiae of human clinical significance, with the majority of isolates being N. nova complex, N. abscessus, N. transvalensis complex, N. farcinica, N. asteroides type VI (N. cyriacigeorgica), and N. brasiliensis. These species cause a wide variety of diseases and have variable drug susceptibilities. Accurate identification often requires referral to a reference laboratory with molecular capabilities, as many newer species are genetically distinct from established species yet have few or no distinguishing phenotypic characteristics. Correct identification is important in deciding the clinical relevance of a species and in the clinical management and treatment of patients with nocardial disease. This review characterizes the currently known pathogenic species of Nocardia, including clinical disease, drug susceptibility, and methods of identification.
Effects of Environmental Factors on the Disinfection Performance of a Wastewater Stabilization Pond Operated in a Temperate Climate

Treatment in a wastewater stabilization pond (WSP) relies on natural purification processes, which can be sensitive to both location and climate. This study investigated the effects of three environmental factors, pH, dissolved oxygen (DO) and temperature, on disinfection efficiency in a WSP system consisting of three facultative cells, and operated in a temperate climate region, in Eastern Ontario, Canada. Indicator organism (Escherichia coli (E. coli)) removal in WSP systems is driven by a combination of different factors. Elevated pH and DO concentrations, which are attributed to the presence of algae, are important factors for effective disinfection. Therefore, the presence of algae in natural wastewater treatment systems can contribute appreciably to disinfection. Consequently, based on algal concentrations, removal efficiencies of pathogenic microorganisms during wastewater treatment over the course of a year can be highly variable, where higher removal efficiencies would be expected in summer and fall seasons.
Statistical Modelling Issues for The Adaptive Brain Interface

In this work methods for Electroencepephalogaphy (EEG) signal preprocessing and statistical modelling related to the brain computer interfaces were studied. Brain computer interface is a device for controlling various applications by user's conscious control of thoughts. No use peripheral nerves or muscles is required. All the main areas of statistical modelling related to the brain computer interfaces were studied: preprocessing, feature extraction and classification. The used preprocessing methods were spatial Laplacian filtering, bandpass filtering and wavelet denoising. In the feature extraction different ways to form a spectrum estimate were studied. Studied method were Fourier-and wavelet estimates and the estimate based on transfer function of an autoregressive (AR) model. For the classification two different statistical models were used. The first was multi-layer perceptron network and the another radial basis function network. The performance was measured by achieved channel capacity and false positive rate. From the preprocessing methods the spatial Laplacian filtering was found to be essential. The wavelet and AR-model based features were a bit more effective than the Fourier features. The multi-layer perceptron networks had better classification results but the radial basis function networks had fewer false positives.
ERMIA: Fast Memory-Optimized Database System for Heterogeneous Workloads

Large main memories and massively parallel processors have triggered not only a resurgence of high-performance transaction processing systems optimized for large main-memory and massively parallel processors, but also an increasing demand for processing heterogeneous workloads that include read-mostly transactions. Many modern transaction processing systems adopt a lightweight optimistic concurrency control (OCC) scheme to leverage its low overhead in low contention workloads. However, we observe that the lightweight OCC is not suitable for heterogeneous workloads, causing significant starvation of read-mostly transactions and overall performance degradation. In this paper, we present ERMIA, a memory-optimized database system built from scratch to cater the need of handling heterogeneous workloads. ERMIA adopts snapshot isolation concurrency control to coordinate heterogeneous transactions and provides serializability when desired. Its physical layer supports the concurrency control schemes in a scalable way. Experimental results show that ERMIA delivers comparable or superior performance and near-linear scalability in a variety of workloads, compared to a recent lightweight OCC-based system. At the same time, ERMIA maintains high throughput on read-mostly transactions when the performance of the OCC-based system drops by orders of magnitude.
Architectures of flexible symmetric key crypto engines—a survey: From hardware coprocessor to multi-crypto-processor system on chip

Throughput, flexibility, and security form the design trilogy of reconfigurable crypto engines; they must be carefully considered without reducing the major role of classical design constraints, such as surface, power consumption, dependability, and cost. Applications such as network security, Virtual Private Networks (VPN), Digital Rights Management (DRM), and pay per view have drawn attention to these three constraints. For more than ten years, many studies in the field of cryptographic engineering have focused on the design of optimized high-throughput hardware cryptographic cores (e.g., symmetric and asymmetric key block ciphers, stream ciphers, and hash functions). The flexibility of cryptographic systems plays a very important role in their practical application. Reconfigurable hardware systems can evolve with algorithms, face up to new types of attacks, and guarantee interoperability between countries and institutions. The flexibility of reconfigurable crypto processors and crypto coprocessors has reached new levels with the emergence of dynamically reconfigurable hardware architectures and tools. Last but not least, the security of systems that handle confidential information needs to be thoroughly evaluated at the design stage in order to meet security objectives that depend on the importance of the information to be protected and on the cost of protection. Usually, designers tackle security problems at the same time as other design constraints and in many cases target only one security objective, for example, a side-channel attack countermeasures, fault tolerance capability, or the monitoring of the device environment. Only a few authors have addressed all three design constraints at the same time. In particular, key management security (e.g., secure key generation and transmission, the use of a hierarchical key structure composed of session keys and master keys) has frequently been neglected to the benefit of performance and/or flexibility. Nevertheless, a few authors propose original processor architectures based on multi-crypto-processor structures and reconfigurable cryptographic arrays. In this article, we review published works on symmetric key crypto engines and present current trends and design challenges.
The Status of the Girl-Child in Soweto, South Africa

Abstract Several reports and Charters have all emphasized the overwhelming importance of the wellbeing of every child and their rights to a rewarding life. However, recent decades have brought growing recognition that throughout the world, women’s status is lower than men’s. More reports have also described the disadvantages or problems that the girl child faces vis-à-vis her brother. This discrimination is further entrenched by the system of institutionalised patriarchy which undermines basic human rights and also engenders a variety of harmful traditional practices. Inspite of this observation, none or very few studies have been conducted on the situation of the Girl-Child in South Africa. Even then such studies have only treated the Girl-Child within the larger spectrum of women studies. Against this backdrop, this study was conducted to investigate the special situation of the girl child in Soweto, South Africa. With a sample of six hundred respondents (boys and girls) not beyond age 19, selected through multi-stage sampling procedure, data was collected using the interview method. Findings reveal that contrary to what obtains in many other parts of Africa, the girl-child and the boy-child in Soweto enjoy comparable status, and no significant difference in care and treatment was reported.
Color perception in the intermediate periphery of the visual field

Color perception changes across the visual field. It is best in the fovea and declines in the periphery. Sensitivity to red–green color variations declines more steeply toward the periphery than sensitivity to luminance or blue–yellow colors. It is thought that this decline is due to the increasing size of receptive fields of parvocellular retinal ganglion cells and the unselective or random contribution of Land M-cones to the receptive field surround. In earlier psychophysical studies it has been found that L j M cone opponency becomes absent above 30 deg. However, physiological experiments in macaque monkeys have shown that midget ganglion cells exist in the intermediate zone of the peripheral retina (20–50 deg) that are strongly cone opponent. Here we explore this contradiction between physiological and psychophysical research, using stimuli of variable size at eccentricities of up to 50 deg. We found that chromatic detection gets worse with increasing eccentricity but is still possible even at large eccentricities. Our results show that chromatic detection at these eccentricities is mediated by cone-opponent mechanisms.
Surveillance & Modesty on Social Media: How Qataris Navigate Modernity and Maintain Tradition

Recent research on social media use in the Middle East and North Africa (MENA) region has focused on their role in the Arab Spring uprisings, but less work has examined the more mundane uses of these technologies. Yet exploring the way populations in the MENA region use social media in everyday life provides insight into how they are adapted to cultural contexts beyond those from which they originated. To better understand this process, we interviewed eleven Qatari nationals currently living in Doha, Qatar. Our analysis identifies ways users, particularly females, practice modesty, manage their own (and by extension) their family's reputation, and use social media to monitor and protect others. These findings are placed within a framework of social, or participatory surveillance, which challenges conventional notions of surveillance as a form of control and instead shows how surveillance has the potential to be empowering.
German newspaper coverage of allied prisoners of war during the Gulf crisis (1990/91)

Herman & Chomsky (1988) have developed a propaganda model to explain why some subjects are preferably dealt with by the news media whereas no attention is paid to others. An essential criterion for this is the usefulness of a topic. Herman & Chomsky, therefore, refer to "worthy" und "unworthy victims": victims of national or international violence, whose torments, though comparable from an objective point of view, are useful as a weapon against an actual enemy in one case, while they are useless, and hence devoid of interest, in another. If responsibility for violence lies in one's own sphere of influence, any reporting on its victims would be unfavorable to one's national interest.
Teaching Abstract Thinking in Introduction to Computer Science for 7th Graders

Abstraction is one of the most fundamental ideas in computer science (CS), and as such it is highly important to start teaching it as early as possible. However, teaching this soft concept to novices is a very complicated task, as has been emphasized by many CS and mathematics education experts. In this paper, we describe the first year of a study that aims to improve students' abstraction skills in algorithmic problem solving. We implement a new teaching method introduced in [2] that was designed to improve CS abstraction skills and to teach them more explicitly. We studied the effects of this teaching method in the context of an introductory CS course for 7th graders. In this course, Scratch is used as the programming language in which the solutions are implemented. We describe part of the first year's results.
A Supply and Demand Model of the College Admissions Problem

We consider a decentralized college admissions problem with uncertainty. We assume that a continuum of heterogeneous students apply to two colleges. College application choices are nontrivial because they are costly and their evaluations are noisy. Colleges set admissions standards for signals of student caliber. We develop the supply and demand framework where admissions standards act like prices that allocate scarce slots to students. We explore comparative statics in the game between colleges, and find that the better college is affected by the lesser college’s admissions standard in this decentralized world. Our analysis is complicated by the students’ application portfolio shifts. Noisy applications not only distorts outcomes, but even strategies: We find that the best students apply to the best college, and the best college sets higher standards only when the colleges differ sufficiently in quality, and the lesser school is not too small. Applying the model, we find that racial affirmative action at the better college is optimally met by a discriminatory admissions policy at the weaker school; and that binding early admissions programs are more effective than non-binding programs when a college competes with a rival that does not use early admissions. An earlier version was called “The College Admissions Problem with Uncertainty. ” Lones is grateful for the financial support of the National Science Foundation. We have benefited from seminars at BU, UCLA, Georgetown, HBS, the 2006 Two-Sided Matching Conference (Bonn), 2006 SED (Vancouver), 2006 Latin American Econometric Society Meetings (Mexico City), and 2007 American Econometric Society Meetings (New Orleans). Arizona State University, Department of Economics, Tempe, AZ 85287-3806. Harvard University, Department of Economics, Cambridge, MA 02138. University of Michigan, Economics Department, Ann Arbor, MI 48109-1220.
Vehicle Detection from 3D Lidar Using Fully Convolutional Network

Convolutional network techniques have recently achieved great success in vision based detection tasks. This paper introduces the recent development of our research on transplanting the fully convolutional network technique to the detection tasks on 3D range scan data. Specifically, the scenario is set as the vehicle detection task from the range data of Velodyne 64E lidar. We proposes to present the data in a 2D point map and use a single 2D end-to-end fully convolutional network to predict the objectness confidence and the bounding boxes simultaneously. By carefully design the bounding box encoding, it is able to predict full 3D bounding boxes even using a 2D convolutional network. Experiments on the KITTI dataset shows the state-of-the-art performance of the proposed method.
Comparing Clustering with Pairwise and Relative Constraints

Clustering can be improved with the help of side information about the similarity relationships among instances. Such information has been commonly represented by two types of constraints: pairwise constraints and relative constraints, regarding similarities about instance pairs and triplets, respectively. Prior work has mostly considered these two types of constraints separately and developed individual algorithms to learn from each type. In practice, however, it is critical to understand/compare the usefulness of the two types of constraints as well as the cost of acquiring them, which has not been studied before. This paper provides an extensive comparison of clustering with these two types of constraints. Specifically, we compare their impacts both on human users that provide such constraints and on the learning system that incorporates such constraints into clustering. In addition, to ensure that the comparison of clustering is performed on equal ground (without the potential bias introduced by different learning algorithms), we propose a probabilistic semi-supervised clustering framework that can learn from either type of constraints. Our experiments demonstrate that the proposed semi-supervised clustering framework is highly effective at utilizing both types of constraints to aid clustering. Our user study provides valuable insights regarding the impact of the constraints on human users, and our experiments on clustering with the human-labeled constraints reveal that relative constraint is often more efficient at improving clustering.