Miracle-dz/UniFAR-FTUs huggingface dataset:
An application of online exam in discrete mathematics course

In Discrete Mathematics courses, we use staged online exams to assess students' learning and make the exams part of the learning experiences. With the help of online exam, teachers can accurately evaluate students learning and give students personalized guidance, while students can receive immediate feedback of test to improve their learning. In this paper, we introduce the work in the use of online examination in Discrete Mathematics teaching, and discuss the related exam data analysis and mining methods and report on students' improvement in their final assessment. Through online exam and its data analysis, we can improve teaching and learning in an objective and scientific way1.
Immune Responses to Middle East Respiratory Syndrome Coronavirus During the Acute and Convalescent Phases of Human Infection

Abstract Background An understanding of immune responses against the Middle East respiratory syndrome (MERS) is important for the development of treatments and preventive measures. Here, we investigated the spectrum of immune responses occurring in patients with MERS during the early period of infection. Methods We obtained peripheral blood samples from 27 hospitalized patients recruited during the epidemic that occurred in 2015 in South Korea. Plasma cytokines/chemokines and antibodies were quantified. Virus-specific T cells were examined by intracellular cytokine staining after stimulation of peripheral blood mononuclear cells with overlapping peptides spanning whole virus structural proteins. Results At the acute phase of infection, elevated levels of plasma proinflammatory cytokines/chemokines were detected in proportion to the severity of the disease. Distinctively high frequencies of MERS coronavirus–reactive CD8+ T cells were also observed in patients with severe/moderate illness, whereas antibody and CD4+ T-cell responses were minimally detected at this stage. At the convalescent phase, disease severity–dependent antibody responses emerged and antigen-reactive cells were identified in both T-cell subsets. These T cells belonged to the T-helper 1 or type 1 cytotoxic T cell subtypes. While CD8+ T cells responded preferentially to the viral S protein compared with E/M/N proteins, especially at the acute stage, slightly more CD4+ T cells recognized E/M/N proteins compared with S protein at the convalescent phase. Conclusions Our findings show an association between the early CD8+ T-cell response and the severity of the infection, and also provide basic information that may help to prepare effective control strategies for MERS in humans.
TALEN: Tool for Annotation of Low-resource ENtities

We present a new web-based interface, TALEN, designed for named entity annotation in low-resource settings where the annotators do not speak the language. To address this non-traditional scenario, TALEN includes such features as in-place lexicon integration, TF-IDF token statistics, Internet search, and entity propagation, all implemented so as to make this difficult task efficient and frictionless. We conduct a small user study to compare against a popular annotation tool, showing that TALEN achieves higher precision and recall against ground-truth annotations, and that users strongly prefer it over the alternative. TALEN is available at: github.com/CogComp/talen.
Characterization of Bacteriophages Infecting Clinical Isolates of Clostridium difficile

Clostridium difficile is recognized as a problematic pathogen, causing severe enteric diseases including antibiotic-associated diarrhea and pseudomembranous colitis. The emergence of antibiotic resistant C. difficile has driven a search for alternative anti-infection modalities. A promising strategy for controlling bacterial infection includes the use of bacteriophages and their gene products. Currently, knowledge of phages active against C. difficile is still relatively limited by the fact that the isolation of phages for this organism is a technically demanding method since bacterial host themselves are difficult to culture. To isolate and characterize phages specific to C. difficile, a genotoxic agent, mitomycin C, was used to induce temperate phages from 12 clinical isolates of C. difficile. Five temperate phages consisting of ΦHR24, ΦHN10, ΦHN16-1, ΦHN16-2, and ΦHN50 were successfully induced and isolated. Spotting assays were performed against a panel of 92 C. difficile isolates to screen for susceptible bacterial hosts. The results revealed that all the C. difficile phages obtained in this work displayed a relatively narrow host range of 0–6.5% of the tested isolates. Electron microscopic characterization revealed that all isolated phages contained an icosahedral head connected to a long contractile tail, suggesting that they belonged to the Myoviridae family. Restriction enzyme analysis indicated that these phages possess unique double-stranded DNA genome. Further electron microscopic characterization revealed that the ΦHN10 absorbed to the bacterial surface via attachment to cell wall, potentially interacting with S-layer protein. Bacteriophages isolated from this study could lead to development of novel therapeutic agents and detection strategies for C. difficile.
Hidden and self-excited attractors in Chua circuit: synchronization and SPICE simulation

Graphical Abstract Two symmetric hidden chaotic attractors (blue), trajectories (red) from unstable manifolds of two saddle points are either attracted to locally stable zero equilibrium, or tend to infinity; trajectories (black) from stable manifolds tend to equilibria. Abstract Nowadays various chaotic secure communication systems based on synchronization of chaotic circuits are widely studied. To achieve synchronization, the control signal proportional to the difference between the circuits signals, adjust the state of one circuit. In this paper the synchronization of two Chua circuits is simulated in Simulation Programs with Integrated Circuit Emphasis. It is shown that the choice of control signal is not straightforward, especially in the case of multistability and hidden attractors.
View Dependent Tone Mapping of HDR Panoramas for Head Mounted Displays

Head mounted display(HMD)s are characterized by relatively low resolution and low dynamic range. These limitations significantly reduce the visual quality of photorealistic captures on such displays. In this paper we present an interactive view dependent tone mapping technique for viewing up to 16K wide high dynamic range panoramas on HMDs via view-adjusted mapping function stored in separate texture file. We define this technique as ToneTexture. The use of a view adjusted tone mapping allows for expansion of the perceived color space available to the end user. This yields an improved visual appearance of both high dynamic range panoramas and low dynamic range panoramas on such displays. We present comparisons of the results produced by this technique against Reinhard tone mapping operators. Demonstration systems are available for WebGL and head mounted displays such as Oculus Rift and GearVR.
Data-Driven Polynomial Ridge Approximation Using Variable Projection

Inexpensive surrogates are useful for reducing the cost of science and engineering studies involving large-scale, complex computational models with many input parameters. A ridge approximation is one class of surrogate that models a quantity of interest as a nonlinear function of a few linear combinations of the input parameters. When used in parameter studies (e.g., optimization or uncertainty quantification), ridge approximations allow the low dimensional structure to be exploited, reducing the effective dimension. We introduce a new, fast algorithm for constructing a ridge approximation where the nonlinear function is a polynomial. This polynomial ridge approximation is chosen to minimize least squared mismatch between the surrogate and the quantity of interest on a given set of inputs. Naively, this would require optimizing both the polynomial coefficients and the linear combination of weights; the latter of which define a low-dimensional subspace of the input space. However, given a fixed subspace the optimal polynomial can be found by solving a linear least-squares problem, and hence by using variable projection the polynomial can be implicitly found leaving an optimization problem over the subspace alone. We provide an algorithm that finds this polynomial ridge approximation by minimizing over the Grassmann manifold of low-dimensional subspaces using a Gauss-Newton method. We provide details of this optimization algorithm and demonstrate its performance on several numerical examples. Our Gauss-Newton method has superior theoretical guarantees and faster convergence than the alternating approach for polynomial ridge approximation earlier proposed by Constantine, Eftekhari, Hokanson, and Ward [this https URL] that alternates between (i) optimizing the polynomial coefficients given the subspace and (ii) optimizing the subspace given the coefficients.
INDETERMINATE SPIKELET1 Recruits Histone Deacetylase and a Transcriptional Repression Complex to Regulate Rice Salt Tolerance1[OPEN]

An EAR-motif-containing AP2/ERF transcription factor IDS1 recruits a transcriptional repression complex to negatively regulate rice salt tolerance. Perception and transduction of salt stress signals are critical for plant survival, growth, and propagation. Thus, identification of components of the salt stress-signaling pathway is important for rice (Oryza sativa) molecular breeding of salt stress resistance. Here, we report the identification of an apetala2/ethylene response factor transcription factor INDETERMINATE SPIKELET1 (IDS1) and its roles in the regulation of rice salt tolerance. By genetic screening and phenotype analysis, we demonstrated that IDS1 conferred transcriptional repression activity and acted as a negative regulator of salt tolerance in rice. To identify potential downstream target genes regulated by IDS1, we conducted chromatin immunoprecipitation (ChIP) sequencing and ChIP-quantitative PCR assays and found that IDS1 may directly associate with the GCC-box-containing motifs in the promoter regions of abiotic stress-responsive genes, including LEA1 (LATE EMBRYOGENESIS ABUNDANT PROTEIN1) and SOS1 (SALT OVERLY SENSITIVE1), which are key genes regulating rice salt tolerance. IDS1 physically interacted with the transcriptional corepressor topless-related 1 and the histone deacetylase HDA1, contributing to the repression of LEA1 and SOS1 expression. Analyses of histone H3 acetylation status and RNA polymerase II occupation on the promoters of LEA1 and SOS1 further defined the molecular foundation of the transcriptional repression activity of IDS1. Our findings illustrate an epigenetic mechanism by which IDS1 modulates salt stress signaling as well as salt tolerance in rice.
Multi-RAT LPWAN in Smart Cities: Trial of LoRaWAN and NB-IoT Integration

The landscape of the contemporary IoT radio access technologies (RATs) is excessively diverse, especially when it comes to such a complex environment as Smart City. On the one hand, this diversity offers operators sufficient flexibility to select the most appropriate RAT for their target application. On the other hand, it becomes a severe limiting factor leading to high level of uncertainty for the IoT device vendors, who need to decide, which technology to support in their hardware. In this paper, we consider the provisioning of the low-power wide area network (LPWAN) devices supporting multiple RATs. First, we briefly discuss the parameters of several potential radio technologies as well as analyze the pros and cons of combining them in a single device. Next, we prototype a real-life device capable of communicating via two perspective LPWAN technologies, namely, LoRaWAN and NB-IoT, and report on the initial results of its performance evaluation. These confirm the feasibility of instrumenting dual-mode devices as well as reveal several important aspects related to the development of multi-radio IoT equipment and its performance. In our view, due to their higher flexibility, reliability, and dependability, the devices such as the one developed can be beneficial for various Smart City applications, with smart energy grids and road traffic control being only two of many examples.
The HIV-1 Subtype B Epidemic in French Guiana and Suriname Is Driven by Ongoing Transmissions of Pandemic and Non-pandemic Lineages

The HIV-1 subtype B epidemic in French Guiana and Suriname is characterized by the co-circulation of the globally disseminated “BPANDEMIC” lineage and of non-pandemic subtype B lineages of Caribbean origin (BCAR). To reconstruct the spatiotemporal pattern of spread of those viral lineages circulating in these two countries, a total of 361 HIV-1 subtype B pol sequences recovered from treatment-naive adult patients from French Guiana and Suriname between 2006 and 2012 were combined with BPANDEMIC and BCAR reference sequences. Major Guianese/Surinamese BPANDEMIC and BCAR lineages were identified by Maximum Likelihood phylogenetic analysis and the spatiotemporal and demographic parameters estimated using a Bayesian coalescent-based method. We detected four BCAR and three BPANDEMIC transmission chains of large size that together comprise most pandemic and non-pandemic subtype B sequences from French Guiana (≥52%) and Suriname (≥70%) here analyzed. These major lineages were probably introduced into French Guiana and Suriname from the Caribbean (BCAR) and North/South America (BPANDEMIC) between the middle 1970s and the late 1980s and spread among populations from both countries with roughly comparable demographic growth rates. We detected a significant trend for higher viral loads and higher proportion of homosexual/bisexual men among subjects infected with BPANDEMIC relative to BCAR strains in French Guiana. These results show that the HIV subtype B epidemic in French Guiana and Suriname has been driven by multiple active BCAR and BPANDEMIC transmission chains that arose since the middle 1970s onward and operate in both countries simultaneously. Although no significant differences in the epidemic potential of major BCAR and BPANDEMIC lineages were observed, relevant associations between the infecting subtype B lineage and epidemiological and clinical characteristics were detected in French Guiana.
Virtual Elements for the Navier-Stokes Problem on Polygonal Meshes

A family of Virtual Element Methods for the 2D Navier-Stokes equations is proposed and analysed. The schemes provide a discrete velocity field which is point-wise divergence-free. A rigorous error analysis is developed, showing that the methods are stable and optimally convergent. Several numerical tests are presented, confirming the theoretical predictions. A comparison with some mixed finite elements is also performed.
Morphological Band Selection for Hyperspectral Imagery

In this letter, a novel morphological band selection method is proposed to obtain the most representative bands from hyperspectral image (HSI) in an unsupervised manner. In order to sufficiently process the HSI, we propose to use only a small set of data instead of using the original full data. For the obtained clusters, the differences of spectral response curves are applied to measure the local discrimination capability of bands, of which the local maximum value point is yielded based on the morphological processing. To verify the performance of the proposed method, the robustness of the parameters has been evaluated, while the effectiveness and superiority have been tested on three popular hyperspectral data sets. The experiment results have shown that the proposed method outperforms other methods.
A constrained extremum-seeking control for CPU thermal management

The increasing complexity of computing architectures is pushing for novel Dynamic Thermal Management (DTM) techniques. Accordingly, more accurate power and thermal models are required. In this work, we propose a thermal controller based on a constrained extremum-seeking algorithm, enabling resource allocation optimization under specific thermal constraints. This approach comes with many advantages. First, the controller does not require any model of the system, dropping the need for a complex and potentially imprecise estimation phase. Second, it allows the control of derived measurements. We show how this may positively impact on the CPU reliability.
Change of hyoid bone position in patients treated for and resolved of myofascial pain

ABSTRACT Objective: The purpose of this study was to investigate change in hyoid bone position for patients suffering from myofascial pain and to evaluate the modified hyoid triangle as a three-dimensional cephalometric modality for measuring hyoid position. Methods: Pre- and post-treatment CBCT scans for 30 female patients resolved of myofascial pain were reviewed by a blinded investigator using the modified hyoid triangle. Changes in dimensions of the hyoid triangle were analyzed with the t-test for paired comparisons. Results: Statistical analysis showed a drawing nearer of the chin to the third cervical vertebra (–2.0 mm, p = 0.026) and a release of the hyoid bone away from the floor of the mouth (1.5 mm, p = 0.011). Discussion: These findings suggest resolution of myofascial pain may correlate with decreased forward head posture and relaxation of suprahyoid musculature. The potential for change in oropharyngeal dimension and airway is evident.
Mine, yours, ours: examples reuse and the self-expression of programming students

Examples are commonly used as support to teach and learn programming. However, programming students (even the most experienced ones) neglect the study of code and the understanding of the message conveyed by it, preferring to reuse e adapt the code to achieve their goals. This work investigates how students use examples in their activities and, in cases where it is reused, how the appropriation of the code message happens and how it impacts in programmer's self-expression and his/her final message communicated with the program.
Recurrent Deep Multiagent Q-Learning for Autonomous Agents in Future Smart Grid

The broker mechanism is widely applied to serve for interested parties to derive long-term policies to reduce costs or gain profits in smart grid. However, brokers are faced with a number of challenging problems such as balancing demand and supply from customers and competing with other coexisting brokers to maximize profits. In this paper, we develop an effective pricing strategy for brokers in local electricity retail market based on recurrent deep multiagent reinforcement learning and sequential clustering. We use real household electricity consumption data to simulate the retail market for evaluating our strategy. The experiments demonstrate the superior performance of the proposed pricing strategy and highlight the effectiveness of our reward shaping mechanism.
An Experimental Study on Coverage Enhancement of LTE Cat-M1 for Machine-Type Communication

This paper studies coverage enhancement of LTE Cat-M1 devices from an experimental perspective. We first conduct link-level lab experiments on Cat-M1 modules using an RF test system and show the coverage gain as well as link throughput versus different repetition numbers. From these measurement results, we observe significant coverage gain resulting from the repetition mechanism, at the expense of a lower throughput. To mitigate the reduction in throughput, we further propose adaptive repetition algorithms to achieve higher throughput while maintaining the same coverage. The simulation results show that the proposed algorithms achieve significantly higher throughput than the fixed-repetition approach.
Time-Course of Metabolic and Proteomic Responses to Different Nitrate/Ammonium Availabilities in Roots and Leaves of Maize

The availability of nitrate and ammonium significantly affects plant growth. Co-provision of both nutrients is generally the best nutritional condition, due to metabolic interactions not yet fully elucidated. In this study, maize grown in hydroponics was exposed to different nitrogen (N) availabilities, consisting of nitrate, ammonium and co-provision. Roots and leaves were analyzed after 6, 30, and 54 h by biochemical evaluations and proteomics. The ammonium-fed plants showed the lowest biomass accumulation and the lowest ratio of inorganic to organic N content, suggesting a metabolic need to assimilate ammonium that was not evident in plants grown in co-provision. The N sources differently affected the root proteome, inducing changes in abundance of proteins involved in N and carbon (C) metabolisms, cell water homeostasis, and cell wall metabolism. Notable among these changes was that some root enzymes, such as asparagine synthetase, phosphoenolpyruvate (PEP) carboxylase, and formate dehydrogenase showed a relevant upsurge only under the sole ammonium nutrition. However, the leaf proteome appeared mainly influenced by total N availability, showing changes in the abundance of several proteins involved in photosynthesis and in energy metabolism. Overall, the study provides novel information about the biochemical determinants involved in plant adaptation to different N mineral forms.
Dependent Relational Gamma Process Models for Longitudinal Networks

A probabilistic framework based on the covariate-dependent relational gamma process is developed to analyze relational data arising from longitudinal networks. The proposed framework characterizes networked nodes by nonnegative node-group memberships, which allow each node to belong to multiple latent groups simultaneously, and encodes edge probabilities between each pair of nodes using a Bernoulli Poisson link to the embedded latent space. Within the latent space, our framework models the birth and death dynamics of individual groups via a thinning function. Our framework also captures the evolution of individual node-group memberships over time using gamma Markov processes. Exploiting the recent advances in data augmentation and marginalization techniques, a simple and efficient Gibbs sampler is proposed for posterior computation. Experimental results on a simulation study and three real-world temporal network data sets demonstrate the model’s capability, competitive performance and scalability compared to state-of-the-art methods.
Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems

Learning to coordinate between multiple agents is an important problem in many reinforcement learning problems. Key to learning to coordinate is exploiting loose couplings, i.e., conditional independences between agents. In this paper we study learning in repeated fully cooperative games, multi-agent multi-armed bandits (MAMABs), in which the expected rewards can be expressed as a coordination graph. We propose multi-agent upper confidence exploration (MAUCE), a new algorithm for MAMABs that exploits loose couplings, which enables us to prove a regret bound that is logarithmic in the number of arm pulls and only linear in the number of agents. We empirically compare MAUCE to sparse cooperative Q-learning, and a state-of-the-art combinatorial bandit approach, and show that it performs much better on a variety of settings, including learning control policies for wind farms.
A Diffusional Schedule for Traffic Reducing on Network-on-Chip

Tasks on NoC (Network-on-Chip) are less efficient because of long-distance data synchronization. An inefficient task schedule strategy can lead to a large number of remote data accessing that ruins the speedup of parallel execution of multiple tasks. Thus, we propose an energy efficient task schedule to reduce task traffic with a diffusional pattern. The task mapping algorithm can optimize traffic distribution by limit tasks into a small area to reduce NoC activities. Comparing to application-layer optimization, our task mapping can obtain 20% energy saving and 15% latency reduction on average.