Miracle-dz/UniFAR-FTUs huggingface dataset:
Nuclear entry and export of FIH are mediated by HIF1α and exportin1, respectively

ABSTRACT Hypoxia plays a crucial role at cellular and physiological levels in all animals. The responses to chronic hypoxia are, at least substantially, orchestrated by activation of the hypoxia inducible transcription factors (HIFs), whose stability and subsequent transcriptional activation are regulated by HIF hydroxylases. Factor inhibiting HIF (FIH), initially isolated as a HIFα interacting protein following a yeast two-hybrid screen, is an asparaginyl hydroxylase that negatively regulates transcriptional activation by HIF. This study aimed to define the mechanisms that govern transitions of FIH between the nucleus and cytoplasm. We report that FIH accumulates in the nucleus within a short time window during hypoxia treatment. We provide evidence, based on the application of genetic interventions and small molecule inhibition of the HIF hydroxylases, that the nuclear localization of FIH is governed by two opposing processes: nuclear entry by ‘coupling’ with HIF1α for importin β1-mediated nuclear import and active export via a Leptomycin B-sensitive exportin1-dependent pathway. This article has an associated First Person interview with the first author of the paper. Summary: Nuclear localization of FIH is governed by two opposing processes: nuclear entry by ‘coupling’ with HIF1α for importin β1-mediated nuclear import, and active export via a leptomycin B-sensitive exportin 1 (CRM1)-dependent pathway.
Quantization of Constrained Processor Data Paths Applied to Convolutional Neural Networks

Artificial Neural Networks (NNs) can effectively be used to solve many classification and regression problems, and deliver state-of-the-art performance in the application domains of natural language processing (NLP) and computer vision (CV). However, the tremendous amount of data movement and excessive convolutional workload of these networks hampers large-scale mobile and embedded productization. Therefore these models are generally mapped to energy-efficient accelerators without floating-point support. Weight and data quantization is an effective way to deploy high-precision models to efficient integer-based platforms. In this paper a quantization method for platforms without wide accumulation registers is being proposed. Two constraints to maximize the bit width of weights and input data for a given accumulator size are introduced. These constraints exploit knowledge about the weight and data distribution of individual layers. Using these constraints, we propose a layer-wise quantization heuristic to find a good fixed-point network approximation. To reduce the number of configurations to consider, only solutions that fully utilize the available accumulator bits are being tested. We demonstrate that 16-bit accumulators are able to obtain a Top-1 classification accuracy within 1% of the floating-point baselines on the CIFAR-10 and ILSVRC2012 image classification benchmarks.
SOMNet: Unsupervised Feature Learning Networks for Image Classification

We present here an unsupervised approach to learning suitable features for a deep learning framework applied to image classification. PCANet was introduced as a simple and efficient baseline for deep learning approaches which used cascaded principle component analysis (PCA) derived filter banks, as well as other simple image processing elements such as binary hashing and blockwise histograms. This was followed by DCTNet which used discrete cosine transform (DCT) filter banks as a learning-free alternative. In this paper we propose SOMNet which uses self-organizing map (SOM) based filters offering a non-orthogonal alternative to PCANet providing comparable performance. It is well established that SOM is a non-linear version of PCA but does not suffer from the same constraints. We also show that through the use of a simple trick in the binarization process results in a dramatic reduction in the dimension of the final feature vector, thus allowing the utilization of more filters which could lead to deeper and more complex structures in further work. We also demonstrate the results of a hybrid methodology that clusters generative Markov random fields (MRF) as filters which provides more diverse features in a data driven approach to deep learning.
Peek Search: Near-Optimal Online Markov Decoding

We resolve the fundamental problem of online decoding with ergodic Markov models. Specifically, we provide deterministic and randomized algorithms that are provably near-optimal under latency constraints with respect to the unconstrained offline optimal algorithm. Our algorithms admit efficient implementation via dynamic programs, and extend to (possibly adversarial) non-stationary or time-varying Markov settings as well. Moreover, we establish lower bounds in both deterministic and randomized settings subject to latency requirements, and prove that no online algorithm can perform significantly better than our algorithms.
Requirements Engineering in the Age of Societal-Scale Cyber-Physical Systems: The Case of Automated Driving

Societal-scale cyber-physical systems, such as smart grids, interconnected medical devices, and driverless transportation systems, are at the cusp of transforming how we live. Using automated driving as an example, this paper argues that requirements engineering for such systems will need to be data-driven, continuous, and values-based.
Responsiveness and meaningful improvement of mobility measures following MS rehabilitation

Objective To determine responsiveness of functional mobility measures, and provide reference values for clinically meaningful improvements, according to disability level, in persons with multiple sclerosis (pwMS) in response to physical rehabilitation. Methods Thirteen mobility measures (clinician- and patient-reported) were assessed before and after rehabilitation in 191 pwMS from 17 international centers (European and United States). Combined anchor- and distribution-based methods were used. A global rating of change scale, from patients' and therapists' perspective, served as external criteria when determining the area under the receiver operating characteristic curve (AUC), the minimally important change (MIC), and the smallest real change (SRC). Patients were stratified into 2 subgroups based on disability level (Expanded Disability Status Scale score ≤4 [n = 72], >4 [n = 119]). Results The Multiple Sclerosis Walking Scale–12, physical subscale of the Multiple Sclerosis Impact Scale–29 (especially for the mildly disabled pwMS), Rivermead Mobility Index, and 5-repetition sit-to-stand test (especially for the moderately to severely disabled pwMS) were the most sensitive measures in detecting improvements in mobility. Findings were determined once the AUC (95% confidence interval) was above 0.5, MIC was greater than SRC, and results were comparable from the patient and therapist perspective. Conclusions Responsiveness, clinically meaningful improvement, and real changes of frequently used mobility measures were calculated, showing great heterogeneity, and were dependent on disability level in pwMS.
Unsupervised Pre-training on Improving the Performance of Neural Network in Regression

The paper aims to empirically analyse the perfor-mance of the prediction capability of Artificial Neural Network by applying a pre-training mechanism. The pre-training used here is same as the training of Deep Belief Network where the network is formed by stacking Restricted Boltzmann Machine one above the other successively. A different set of experiments are performed to understand in what scenario pre-trained ANN performed better than randomly initialised ANN. The results of experiments showed that pre-trained model performed better than randomly initialised ANN in terms of generalised error, computational units required and most importantly robust to change in hyperparameters such as learning rate and model architecture. The only cost is in additional time involved in the pre-training phase. Further, the learned knowledge in pretraining, which is stored as weights in ANN, are analysed using Hinton diagram. The analysis could provide clear picture of the pre-training that learned some of the hidden characteristics of the data.
Finding Answers from the Word of God: Domain Adaptation for Neural Networks in Biblical Question Answering

Question answering (QA) has significantly benefitted from deep learning techniques in recent years. However, domain-specific QA remains a challenge due to the significant amount of data required to train a neural network. This paper studies the answer sentence selection task in the Bible domain and answer questions by selecting relevant verses from the Bible. For this purpose, we create a new dataset BibleQA based on bible trivia questions and propose three neural network models for our task. We pre-train our models on a large-scale QA dataset, SQuAD, and investigate the effect of transferring weights on model accuracy. Furthermore, we also measured the model accuracies with different answer context lengths and different Bible translations. We affirm that transfer learning has a noticeable improvement in the model accuracy. We achieved relatively good results with shorter context lengths, whereas longer context lengths decreased model accuracy. We also find that using a more modern Bible translation in the dataset has a positive effect on the task.
Asynchronous Bundle Method for Large-Scale Regularized Risk Minimization

Bundle method for regularized risk minimization (BMRM) is a variant of Cutting Plane Method (CPM). It performs efficiently in solving a convex minimization problem, which is a core part in a plethora of machine learning applications. Nonetheless, while exposed to the challenge of large-scale learning, the synchronous parallel implementation of BMRM easily encounters the straggler problem due to the diversity among heterogeneous working nodes’ capability and unevenness in the inherent data distribution. In this paper, we propose a novel asynchronous distributed BMRM implementation, which employs an asynchronous computing window to fully explore the fast nodes’ computational capabilities while reserving the good convergence of the BMRM. Extensive experiments show that the asynchronous BMRM algorithm has significant improvement of performance over its synchronous counterpart, and owns the ability to solve large-scale problems efficiently.
Partial cloud-based evolving method for fault detection of HVAC system

In this paper we present an initial investigation of the evolving cloud-based method using partial density calculation for fault detection of HVAC system (Heating, Ventilation and Air Conditioning). Moreover, we investigate different approaches of choosing the most influential components when calculating the partial local density which is further used for evolving the model structure. The method is based on the simplified fuzzy rule- based-system AnYa where the antecedent part is presented by data clouds. The effectiveness of the proposed method is tested on a model of HVAC system and furthermore, different types of faults are investigated. The results were compared with the well established fault detection method DPCA (Dynamic Principal Component Analysis).
Sharing, Protection, and Compatibility for Reconfigurable Fabric with AmorphOS

Cloud providers such as Amazon and Microsoft have begun to support on-demand FPGA acceleration in the cloud, and hardware vendors will support FPGAs in future processors. At the same time, technology advancements such as 3D stacking, through-silicon vias (TSVs), and FinFETs have greatly increased FPGA density. The massive parallelism of current FPGAs can support not only extremely large applications, but multiple applications simultaneously as well. System support for FPGAs, however, is in its infancy. Unlike software, where resource configurations are limited to simple dimensions of compute, memory, and I/O, FPGAs provide a multi-dimensional sea of resources known as the FPGA fabric: logic cells, floating point units, memories, and I/O can all be wired together, leading to spatial constraints on FPGA resources. Current stacks either support only a single application or statically partition the FPGA fabric into fixed-size slots. These designs cannot efficiently support diverse workloads: the size of the largest slot places an artificial limit on application size, and oversized slots result in wasted FPGA resources and reduced concurrency. This paper presents AMORPHOS, which encapsulates user FPGA logic in morphable tasks, or Morphlets. Morphlets provide isolation and protection across mutually distrustful protection domains, extending the guarantees of software processes. Morphlets can morph, dynamically altering their deployed form based on resource requirements and availability. To build Morphlets, developers provide a parameterized hardware design that interfaces with AMORPHOS, along with a mesh, which specifies external resource requirements. AMORPHOS explores the parameter space, generating deployable Morphlets of varying size and resource requirements. AMORPHOS multiplexes Morphlets on the FPGA in both space and 1 10 10
Using Low Cost Embedded Systems for Respiratory Sounds Auscultation

With technology advances, trying to replace expensive devices with cheaper, but efficient, systems is a promising approach to pursuit in the future. The main objective of this paper is to compare the Littmann 3200 (state-of-the-art electronic stethoscope) with a prototype built for this purpose (with an electric microphone as the sensor, and an Arduino Mega as a controller), in terms of sound quality, cough detection, and costs. Regarding sound quality, the Littmann is better, especially because of all the technology behind the stethoscope. The costs of both devices are significantly different: the prototype costs 90% less than the Littmann. In terms of cough detection, the Littmann has a sensitivity of 75.4±32.0% and a specificity of 99.6±2.5%, and the prototype has a sensitivity of 71.7±32.1% and a specificity of 98.0±11.0%, but the differences are not significant.
Network Intrusion Detection Using Kernel-based Fuzzy-rough Feature Selection

The purpose of the intrusion detection systems is to detect attacks on computer systems and networks. Many technologies can be used for intrusion detection, and one of the most effective technologies is data mining. The rapid development of network technology and internet of things makes network intrusion detection become one of the hot topics for research. Various classifiers have been applied in the field of network intrusion detection, but the performance of such approaches highly depends on the features used. Therefore, feature selection approaches have been usually used along with classifiers for network intrusion detection, including the fuzzy-rough feature selection. The fuzzy-rough sets is an extension of the classical rough sets, which can deal with the imprecision and uncertainty of discrete, real value or noise data. It can be seen from the practical applications that there are some shortcomings. Therefore, researchers combine fuzzy-rough sets with kernel methods in order to solve these problems. In this paper, the kernel-based fuzzy-rough feature selection method is used to select the feature subset for the intrusion detection. The proposed approach is validated and evaluated using the KDD 99 dataset with the support of different common classifiers. The experimental outcomes obtained by applying the kernel-based fuzzy-rough feature selection method on KDD data set demonstrate that it performs well in terms of reduction effect and accuracy.
Differential Energy Profiling: Energy Optimization via Diffing Similar Apps

Mobile app energy profilers provide a foundational energy diagnostic tool by identifying energy hotspots in the app source code. However, they only tackle the first challenge faced by developers, as, after presented with the energy hotspots, developers typically do not have any guidance on how to proceed with the remaining optimization process: (1) Is there a more energy-efficient implementation for the same app task? (2) How to come up with the more efficient implementation? To help developers tackle these challenges, we developed a new energy profiling methodology called differential energy profiling that automatically uncovers more efficient implementations of common app tasks by leveraging existing implementations of similar apps which are bountiful in the app marketplace. To demonstrate its effectiveness, we implemented such a differential energy profiler, DIFFPROF, for Android apps and used it to profile 8 groups (from 6 popular app categories) of 5 similar apps each. Our extensive case studies show that DIFFPROF provides developers with actionable diagnosis beyond a traditional energy profiler: it identifies non-essential (unmatched or extra) and known-to-be inefficient (matched) tasks, and the call trees of tasks it extracts further allow developers to quickly understand the reasons and develop fixes for the energy difference with minor manual debugging efforts.
Career Skills Assessment in a Doctor of Pharmacy Curriculum

Objective. To assess students’ knowledge of, perceived importance of, and confidence in six career skills areas (curriculum vitae/resume writing, interviewing skills/business attire, phone interviews, thank you notes, business/dining etiquette, and networking) before, immediately after, and six months after participating in a career skills workshop. Methods. All students in a senior-level seminar course participated in the same simulation/performance-based workshop that was coupled with verbal or rubric-based feedback for each of the areas. Results. Ninety-one students participated in the study and all students’ knowledge significantly increased over the study as determined by study baseline, conclusion, and six-month follow-up assessments. At study follow-up, knowledge increased an average of +7.1 percentage points from baseline. Multivariate analysis indicated significant increases in confidence from baseline to follow-up ranging from +0.15 to +0.29 across the six workshop areas, with resume/CV preparation having the highest increase. From study onset to follow-up, students perceived that the six career skills areas were above the average importance midpoint (3.0). Conclusion. The workshop was effective in increasing students’ knowledge and confidence of essential career skills vital to pursuing post-graduate employment. These career skills are important for helping students distinguish themselves in a competitive job market.
Static and Dynamic Ensembles of Neural Networks for Solar Power Forecasting

Accurate forecasting of the power generated by PhotoVoltaic (PV) systems is needed for the successful integration of solar power into the electricity grid. In this paper, we consider the task of simultaneously forecasting the PV power output for the next day at half-hourly intervals using only previous PV power data. We propose a number of methods for constructing static and dynamic ensembles of neural networks. The static ensembles are based on random sampling and random feature selection, and the dynamic ones adaptively weight the contribution of the ensemble members based on their recent performance. We conduct an evaluation using Australian solar PV data for two years, and compare the results with state-of-the-art single prediction models and classical ensemble models. The results show that the proposed static ensembles are beneficial, achieving higher accuracy than the single neural networks and the other single and ensemble models used for comparison. The dynamic ensemble versions further improve the accuracy, demonstrating the potential of adaptively combining the predictions of ensemble members for accurate solar power forecasting.
Robust Fitting of Subdivision Surfaces for Smooth Shape Analysis

Most shape analysis methods use meshes to discretize the shape and functions on it by piecewise linear functions. Fine meshes are then necessary to represent smooth shapes and compute accurate curvatures or Laplace-Beltrami eigenfunctions at large computational costs. We avoid this bottleneck by representing smooth shapes as subdivision surfaces and using the subdivision scheme to parametrize smooth surface functions with few control parameters. We propose a model to fit a subdivision surface to input samples that, unlike previous methods, can be applied to noisy and partial scans from depth sensors. The task is formulated as an optimization problem with robust data terms and solved with a sequential quadratic program that outperforms the solvers previously used to fit subdivision surfaces to noisy data. Our experiments show that the compression of a subdivision representation does not affect the accuracy of the Laplace-Beltrami operator and allows to compute shape descriptors, geodesics, and shape matchings at a fraction of the computational cost of mesh representations.
Automatic detection of drowsiness using in-ear EEG

Sleep monitoring with wearable electroencephalography (EEG) has recently been validated and reported in the research community. One such device is our ultra-wearable, unobtrusive, and inconspicuous in-ear EEG system, which has already been demonstrated to be next-generation solution for out-of-clinic sleep monitoring. We here provide a further proof of concept of the utility of ear-EEG in day time drowsiness monitoring in the real-world. For rigour, hypnograms are obtained from manually scored daytime nap recordings from twentythree subjects, while a complexity science feature-structural complexity extracted from scalp- and ear-EEG recordings - is used in the classification stage, in conjunction with a binary-class support vector machine (SVM). The achieved drowsiness classification accuracies range from 80.0% to 82.9% for ear-EEG, with the corresponding accuracies for scalp-EEG ranging from 86.8 % to 88.8 %. Given the notoriously difficult to classify drowsiness related changes in EEG (similar to the issues with the NREM Stage 1), this conclusively confirms the feasibility of in-ear EEG for automatic light sleep classification. This also promises a key stepping stone towards continuous, discreet, and user-friendly wearable out-of-clinic drowsiness monitoring in the real-world, with numerous applications in the monitoring the state of body and mind of pilots, train drivers, and tele-operators.
Active learning through two-stage clustering

Clustering-based active learning approaches take advantage of the structure of the data to select representative instances. However, some algorithms are either inefficient or only applicable to some data. In this paper, we propose an effective and adaptive algorithm that will be called active learning through two-stage clustering (ALTA). The first stage is data preprocessing using the two-round-clustering algorithm to obtain $\sqrt n $ small blocks, where n is the number of instances. For each block, the closest instance of the center is selected as the representative. The second stage is the active learning of representative instances through density clustering. This stage consists of a number of iterations of density clustering, labeling and classification. In general, data preprocessing reduces the size of the data and the complexity of the algorithm. The combination of distance vector clustering and density clustering makes the algorithm more adaptive. Experiments are performed in comparison against the state-of-the-art active learning algorithms on nine datasets. Results demonstrate that the new algorithm has higher classification accuracy with the same number of labeled data.