Miracle-dz/UniFAR-FTUs huggingface dataset:
Purification and Characterisation of Malate Dehydrogenase From Synechocystis sp. PCC 6803: Biochemical Barrier of the Oxidative Tricarboxylic Acid Cycle

Cyanobacteria possess an atypical tricarboxylic acid (TCA) cycle with various bypasses. Previous studies have suggested that a cyclic flow through the TCA cycle is not essential for cyanobacteria under normal growth conditions. The cyanobacterial TCA cycle is, thus, different from that in other bacteria, and the biochemical properties of enzymes in this TCA cycle are less understood. In this study, we reveal the biochemical characteristics of malate dehydrogenase (MDH) from Synechocystis sp. PCC 6803 MDH (SyMDH). The optimal temperature of SyMDH activity was 45–50°C and SyMDH was more thermostable than MDHs from other mesophilic microorganisms. The optimal pH of SyMDH varied with the direction of the reaction: pH 8.0 for the oxidative reaction and pH 6.5 for the reductive reaction. The reductive reaction catalysed by SyMDH was activated by magnesium ions and fumarate, indicating that SyMDH is regulated by a positive feedback mechanism. The Km-value of SyMDH for malate was approximately 210-fold higher than that for oxaloacetate and the Km-value for NAD+ was approximately 19-fold higher than that for NADH. The catalytic efficiency of SyMDH for the reductive reaction, deduced from kcat-values, was also higher than that for the oxidative reaction. These results indicate that SyMDH is more efficient in the reductive reaction in the TCA cycle, and it plays key roles in determining the direction of the TCA cycle in this cyanobacterium.
Uplink Massive MIMO for Channels with Spatial Correlation

A massive MIMO system entails a large number of base station antennas M serving a much smaller number of users. This leads to large gains in spectral and energy efficiency compared with other technologies. As the number of antennas M grows, the performance of such systems gets limited by pilot contamination interference. Large Scale Fading Precoding&#x002F;Postcoding (LSFP) was proposed for mitigation of pilot contamination, and it was shown that in channels without spatial correlation (uncorrelated base station antennas) LSFP leads to large spectral-efficiency gains. It was recently proven that if a channel has spatial correlation, then one can use this correlation to drastically reduce the pilot contamination interference in the asymptotic regime as M tends to infinity. In this work, we analyze the performance of Uplink (UL) transmission of massive MIMO systems with finitely many antennas M for channels with spatial correlation. We extend the idea of LSFP to correlated channel models and derive SINR expressions that depend only on slow fading channel components for such systems with and without LSFP. These simple expressions lead us to simple algorithms for transmit power optimization. As a result, we obtain a multi-fold increase in data transmission rates.
CANAL: A Cache Timing Analysis Framework via LLVM Transformation

A unified modeling framework for non-functional properties of a program is essential for research in software analysis and verification, since it reduces burdens on individual researchers to implement new approaches and compare existing approaches. We present CANAL, a framework that models the cache behaviors of a program by transforming its intermediate representation in the LLVM compiler. CANAL inserts auxiliary variables and instructions to allow standard verification tools to handle a new class of cache related properties, e.g., for computing the worst-case execution time and detecting side-channel leaks. We demonstrate the effectiveness of CANAL using three verification tools: KLEE, SMACK and Crab-llvm. We confirm the accuracy of our cache model by comparing with CPU cycle-accurate simulation results of GEM5.
Deep Learning on Low-Resource Datasets

In training a deep learning system to perform audio transcription, two practical problems may arise. Firstly, most datasets are weakly labelled, having only a list of events present in each recording without any temporal information for training. Secondly, deep neural networks need a very large amount of labelled training data to achieve good quality performance, yet in practice it is difficult to collect enough samples for most classes of interest. In this paper, we propose factorising the final task of audio transcription into multiple intermediate tasks in order to improve the training performance when dealing with this kind of low-resource datasets. We evaluate three data-efficient approaches of training a stacked convolutional and recurrent neural network for the intermediate tasks. Our results show that different methods of training have different advantages and disadvantages.
Different diversification histories in tropical and temperate lineages in the ascomycete subfamily Protoparmelioideae (Parmeliaceae)

Abstract Background: Environment and geographic processes affect species’ distributions as well as evolutionary processes, such as clade diversification. Estimating the time of origin and diversification of organisms helps us understand how climate fluctuations in the past might have influenced the diversification and present distribution of species. Complementing divergence dating with character evolution could indicate how key innovations have facilitated the diversification of species. Methods: We estimated the divergence times within the newly recognised subfamily Protoparmelioideae (Ascomycota) using a multilocus dataset to assess the temporal context of diversification events. We reconstructed ancestral habitats and substrate using a species tree generated in *Beast. Results: We found that the diversification in Protoparmelioideae occurred during the Miocene and that the diversification events in the tropical clade Maronina predate those of the extratropical Protoparmelia. Character reconstructions suggest that the ancestor of Protoparmelioideae was most probably a rock-dwelling lichen inhabiting temperate environments. Conclusions: Major diversification within the subtropical/tropical genus Maronina occurred between the Paleocene and Miocene whereas the diversifications within the montane, arctic/temperate genus Protoparmelia occurred much more recently, i.e. in the Miocene.
Multi-objective feature selection for EEG classification with multi-level parallelism on heterogeneous CPU-GPU clusters

The present trend in the development of computer architectures that offer improvements in both performance and energy efficiency has provided clusters with interconnected nodes including multiple multi-core microprocessors and accelerators. In these so-called heterogeneous computers, the applications can take advantage of different parallelism levels according to the characteristics of the architectures in the platform. Thus, the applications should be properly programmed to reach good efficiencies, not only with respect to the achieved speedups but also taking into account the issues related to energy consumption. In this paper we provide a multi-objective evolutionary algorithm for feature selection in electroencephalogram (EEG) classification, which can take advantage of parallelism at multiple levels: among the CPU-GPU nodes interconnected in the cluster (through message-passing), and inside these nodes (through shared-memory thread-level parallelism in the CPU cores, and data-level parallelism and thread-level parallelism in the GPU). The procedure has been experimentally evaluated in performance and energy consumption and shows statistically significant benefits for feature selection: speedups of up to 73 requiring only a 6% of the energy consumed by the sequential code.
A Feasibility Clinical Trial to Improve Social Attention in Autistic Spectrum Disorder (ASD) Using a Brain Computer Interface

Deficits in the interpretation of others' intentions from gaze-direction or other social attention cues are well-recognized in ASD. Here we investigated whether an EEG brain computer interface (BCI) can be used to train social cognition skills in ASD patients. We performed a single-arm feasibility clinical trial and enrolled 15 participants (mean age 22y 2m) with high-functioning ASD (mean full-scale IQ 103). Participants were submitted to a BCI training paradigm using a virtual reality interface over seven sessions spread over 4 months. The first four sessions occurred weekly, and the remainder monthly. In each session, the subject was asked to identify objects of interest based on the gaze direction of an avatar. Attentional responses were extracted from the EEG P300 component. A final follow-up assessment was performed 6-months after the last session. To analyze responses to joint attention cues participants were assessed pre and post intervention and in the follow-up, using an ecologic “Joint-attention task. ” We used eye-tracking to identify the number of social attention items that a patient could accurately identify from an avatar's action cues (e.g., looking, pointing at). As secondary outcome measures we used the Autism Treatment Evaluation Checklist (ATEC) and the Vineland Adaptive Behavior Scale (VABS). Neuropsychological measures related to mood and depression were also assessed. In sum, we observed a decrease in total ATEC and rated autism symptoms (Sociability; Sensory/Cognitive Awareness; Health/Physical/Behavior); an evident improvement in Adapted Behavior Composite and in the DLS subarea from VABS; a decrease in Depression (from POMS) and in mood disturbance/depression (BDI). BCI online performance and tolerance were stable along the intervention. Average P300 amplitude and alpha power were also preserved across sessions. We have demonstrated the feasibility of BCI in this kind of intervention in ASD. Participants engage successfully and consistently in the task. Although the primary outcome (rate of automatic responses to joint attention cues) did not show changes, most secondary neuropsychological outcome measures showed improvement, yielding promise for a future efficacy trial. (clinical-trial ID: NCT02445625—clinicaltrials.gov).
Isolation of a Reassortant H1N2 Swine Flu Strain of Type “Swine-Human-Avian” and Its Genetic Variability Analysis

We isolated an influenza strain named A/Swine/Fujian/F1/2010 (H1N2) from a pig suspected to be infected with swine flu. The results of electron microscopy, hemagglutination (HA) assay, hemagglutination inhibition (HI) assay, and whole genome sequencing analysis suggest that it was a reassortant virus of swine (H1N1 subtype), human (H3N2 subtype), and avian influenza viruses. To further study the genetic evolution of A/Swine/Fujian/F1/2010 (H1N2), we cloned its whole genome fragments using RT-PCR and performed phylogenetic analysis on the eight genes. As a result, the nucleotide sequences of HA, NA, PB1, PA, PB2, NP, M, and NS gene are similar to those of A/Swine/Shanghai/1/2007(H1N2) with identity of 98.9%, 98.9%, 99.0%, 98.6%, 99.0%, 98.9%, 99.3%, and 99.3%, respectively. Similar to A/Swine/Shanghai/1/2007(H1N2), we inferred that the HA, NP, M, and NS gene fragments of A/Swine/Fujian/F1/2010 (H1N2) strain were derived from classical swine influenza H3N2 subtype, NA and PB1 were derived from human swine influenza H3N2 subtype, and PB2 and PA genes were derived from avian influenza virus. This further validates the role of swine as a “mixer” for influenza viruses.
Maze benchmark for testing evolutionary algorithms

In this paper, a new benchmark, for testing the capability of Evolutionary Algorithms to generate maze solving Exploration Strategies is introduced. This benchmark solves three problems commonly found in other benchmarks: small maze set, all mazes belonging to the same kind of maze, and biased methods for choosing starting locations. In this benchmark, the Connectivity Based Maze Generation Algorithm is proposed for building maze sets. Mazes generated using this algorithm exhibit a property called connectivity that indicates how much the walls are connected among them. Connectivity can be used to control the diversity of the sets. Additionally, a new method for choosing starting locations, that takes into account the maze goal positions, is presented. Using the Connectivity Based Maze Generation Algorithm and the new method for choosing starting locations, two problems that test the capability of an Evolutionary Algorithm for solving mazes with similar and different connectivity are built. Example datasets are generated and experiments are performed to validate the proposed benchmark.
Using the Explicit User Profile to Predict User Engagement in Active Video Watching

In this paper we leverage the explicit user profile (relating to experience, knowledge, and self-regulation) to predict user engagement in active video watching. Data from two user studies for informal learning of presentation skills in a Higher Education context is used to develop and validate the prediction models. Our results show that these user characteristics can reasonably predict the overall engagement (inactive, passive and constructive learners). Our approach can be used to inform adaptive interventions that prevent disengagement and enhance the learning experience.
Spatial Stream Backscatter Using Commodity WiFi

Backscatter WiFi offers a novel low-cost and low-energy solution for RFID tags to communicate with existing WiFi devices. State-of-the-art backscatter WiFi solutions have seldom explored advanced features in the latest WiFi standards, in particular, spatial multiplexing, which has been the cornerstone for 802.11n and beyond. In this paper, we present MOXcatter, a WiFi backscatter communication system that works with spatial streams using commodity radios, while keeping the ongoing data communication unaffected. In MOXcatter, a backscatter tag can embed its sensing data on ambient spatial-stream packets, and both the sensing data and the original packets can be decoded by commodity WiFi devices. We have built a MOXcatter prototype with FPGAs and commodity WiFi devices. The experiments show that MOXcatter achieves up to 50 Kbps throughput for a single stream and up to 1 Kbps for double streams with a communication range (tag-to-RX) up to 14 m. We discuss the tradeoffs therein and possible enhancements, and also showcase the applicability of our design through a sensor communication system.
A Computational Method for Evaluating UI Patterns

UI design languages, such as Google's Material Design, make applications both easier to develop and easier to learn by providing a set of standard UI components. Nonetheless, it is hard to assess the impact of design languages in the wild. Moreover, designers often get stranded by strong-opinionated debates around the merit of certain UI components, such as the Floating Action Button and the Navigation Drawer. To address these challenges, this short paper introduces a method for measuring the impact of design languages and informing design debates through analyzing a dataset consisting of view hierarchies, screenshots, and app metadata for more than 9,000 mobile apps. Our data analysis shows that use of Material Design is positively correlated to app ratings, and to some extent, also the number of installs. Furthermore, we show that use of UI components vary by app category, suggesting a more nuanced view needed in design debates.
Phase Retrieval Under a Generative Prior

The phase retrieval problem asks to recover a natural signal $y_0 \in \mathbb{R}^n$ from $m$ quadratic observations, where $m$ is to be minimized. As is common in many imaging problems, natural signals are considered sparse with respect to a known basis, and the generic sparsity prior is enforced via $\ell_1$ regularization. While successful in the realm of linear inverse problems, such $\ell_1$ methods have encountered possibly fundamental limitations, as no computationally efficient algorithm for phase retrieval of a $k$-sparse signal has been proven to succeed with fewer than $O(k^2\log n)$ generic measurements, exceeding the theoretical optimum of $O(k \log n)$. In this paper, we propose a novel framework for phase retrieval by 1) modeling natural signals as being in the range of a deep generative neural network $G : \mathbb{R}^k \rightarrow \mathbb{R}^n$ and 2) enforcing this prior directly by optimizing an empirical risk objective over the domain of the generator. Our formulation has provably favorable global geometry for gradient methods, as soon as $m = O(kd^2\log n)$, where $d$ is the depth of the network. Specifically, when suitable deterministic conditions on the generator and measurement matrix are met, we construct a descent direction for any point outside of a small neighborhood around the unique global minimizer and its negative multiple, and show that such conditions hold with high probability under Gaussian ensembles of multilayer fully-connected generator networks and measurement matrices. This formulation for structured phase retrieval thus has two advantages over sparsity based methods: 1) deep generative priors can more tightly represent natural signals and 2) information theoretically optimal sample complexity. We corroborate these results with experiments showing that exploiting generative models in phase retrieval tasks outperforms sparse phase retrieval methods.
Handwritten Digits Recognition by Using CNN Alex-Net Pre-trained for Large-scale Object Image Dataset

Neural networks are powerful technology for classification of character patterns and object images. A new method for handwritten digits recognition is proposed by combining pre-trained Convolutional Neural Networks (CNN) and Support Vector Machines(SVM). Pre-trained CNN, Alex-Net can be used as pattern feature extractor. Alex-Net is pre-trained for large-scale object image dataset. An SVM is used as trainable classifier. The training 60k samples on MNIST database are trained by the SVM. The feature vectors of character patterns are passed to the SVM from Alex-Net. Without augmentation, experimental results of test error rate 1.03% on the test 10k MNIST database shows that proposed method is effective in handwritten digits recognition.
A smart IoT middleware for comfort management based on Multi-Agent-System

In this paper we propose a new approach of comfort management based on a smart IoT middleware composed of a Knowledge Base (KB) and a Multi-Agent System (MAS). Those elements allow users in a room to interact with the system to request in a simple and natural way the change of one or more comfort elements (including temperature, humidity, luminosity, noise, ventilation and air quality). Shared knowledge fed by the IoT network is used by smart layers (KB and MAS) to reason and propose an action on one or more IoT devices. The implementation is based on underlying ontologies of comfort and actions, real-world IoT devices and a set of agents representing each actuator. These latter participate to the global system's organization by playing a specific role and by having to reach local goals. Distribution of tasks and emergence of behaviour will result in the selection of actions to execute in order to meet this request for comfort change.
Accurate and Efficient Refactoring Detection in Commit History

Refactoring detection algorithms have been crucial to a variety of applications: (i) empirical studies about the evolution of code, tests, and faults, (ii) tools for library API migration, (iii) improving the comprehension of changes and code reviews, etc. However, recent research has questioned the accuracy of the state-of-the-art refactoring detection tools, which poses threats to the reliability of their application. Moreover, previous refactoring detection tools are very sensitive to user-provided similarity thresholds, which further reduces their practical accuracy. In addition, their requirement to build the project versions/revisions under analysis makes them inapplicable in many real-world scenarios. To reinvigorate a previously fruitful line of research that has stifled, we designed, implemented, and evaluated RMiner, a technique that overcomes the above limitations. At the heart of RMiner is an AST-based statement matching algorithm that determines refactoring candidates without requiring user-defined thresholds. To empirically evaluate RMiner, we created the most comprehensive oracle to date that uses triangulation to create a dataset with considerably reduced bias, representing 3,188 refactorings from 185 open-source projects. Using this oracle, we found that RMiner has a precision of 98% and recall of 87%, which is a significant improvement over the previous state-of-the-art.
A Novel Finger-Assisted Touch-free Text Input System Without Training

Recently, tiny smart devices have become increasingly popular in our lives because of their neat features and stylish appearance. However, their small form factors, especially screens, make it inconvenient for users to enter texts with conventional methods such as soft keyboards, which need a fairly large screen. To address this problem, we propose a novel texts-input system, called EchoType, with which users can enter texts with a finger writing in the air. EchoType makes use of acoustic sensors (i.e., microphone and speaker) to sense finger gestures and infer texts based on mapping relation between gestures and basic letters. We take a step to enable users to input texts with acoustic signals. Compared with existing approaches, EchoType enjoys merits of low hardware requirements and high scalability to different mobile devices.
Stem cell heterogeneity drives the parasitic life cycle of Schistosoma mansoni

Schistosomes are parasitic flatworms infecting hundreds of millions of people. These parasites alternate between asexual reproduction in molluscan hosts and sexual reproduction in mammalian hosts; short-lived, water-borne stages infect each host. Thriving in such disparate environments requires remarkable developmental plasticity, manifested by five body plans deployed throughout the parasite’s life cycle. Stem cells in Schistosoma mansoni provide a potential source for such plasticity; however, the relationship between stem cells from different life-cycle stages remains unclear, as does the origin of the germline, required for sexual reproduction. Here, we show that subsets of larvally derived stem cells are likely sources of adult stem cells and the germline. We also identify a novel gene that serves as the earliest marker for the schistosome germline, which emerges inside the mammalian host and is ultimately responsible for disease pathology. This work reveals the stem cell heterogeneity driving the propagation of the schistosome life cycle.
Enlightened Debugging

Numerous automated techniques have been proposed to reduce the cost of software debugging, a notoriously time-consuming and human-intensive activity. Among these techniques, Statistical Fault Localization (SFL) is particularly popular. One issue with SFL is that it is based on strong, often unrealistic assumptions on how developers behave when debugging. To address this problem, we propose Enlighten, an interactive, feedback-driven fault localization technique. Given a failing test, Enlighten (1) leverages SFL and dynamic dependence analysis to identify suspicious method invocations and corresponding data values, (2) presents the developer with a query about the most suspicious invocation expressed in terms of inputs and outputs, (3) encodes the developer feedback on the correctness of individual data values as extra program specifications, and (4) repeats these steps until the fault is found. We evaluated Enlighten in two ways. First, we applied Enlighten to 1,807 real and seeded faults in 3 open source programs using an automated oracle as a simulated user; for over 96% of these faults, Enlighten required less than 10 interactions with the simulated user to localize the fault, and a sensitivity analysis showed that the results were robust to erroneous responses. Second, we performed an actual user study on 4 faults with 24 participants and found that participants who used Enlighten performed significantly better than those not using our tool, in terms of both number of faults localized and time needed to localize the faults.