Miracle-dz/UniFAR-FTUs huggingface dataset:
Taxonomy and Evaluation of Structured Compression of Convolutional Neural Networks

The success of deep neural networks in many real-world applications is leading to new challenges in building more efficient architectures. One effective way of making networks more efficient is neural network compression. We provide an overview of existing neural network compression methods that can be used to make neural networks more efficient by changing the architecture of the network. First, we introduce a new way to categorize all published compression methods, based on the amount of data and compute needed to make the methods work in practice. These are three 'levels of compression solutions'. Second, we provide a taxonomy of tensor factorization based and probabilistic compression methods. Finally, we perform an extensive evaluation of different compression techniques from the literature for models trained on ImageNet. We show that SVD and probabilistic compression or pruning methods are complementary and give the best results of all the considered methods. We also provide practical ways to combine them.
Deep Exemplar Networks for VQA and VQG

In this paper, we consider the problem of solving semantic tasks such as `Visual Question Answering' (VQA), where one aims to answers related to an image and `Visual Question Generation' (VQG), where one aims to generate a natural question pertaining to an image. Solutions for VQA and VQG tasks have been proposed using variants of encoder-decoder deep learning based frameworks that have shown impressive performance. Humans however often show generalization by relying on exemplar based approaches. For instance, the work by Tversky and Kahneman suggests that humans use exemplars when making categorizations and decisions. In this work, we propose the incorporation of exemplar based approaches towards solving these problems. Specifically, we incorporate exemplar based approaches and show that an exemplar based module can be incorporated in almost any of the deep learning architectures proposed in the literature and the addition of such a block results in improved performance for solving these tasks. Thus, just as the incorporation of attention is now considered de facto useful for solving these tasks, similarly, incorporating exemplars also can be considered to improve any proposed architecture for solving this task. We provide extensive empirical analysis for the same through various architectures, ablations, and state of the art comparisons.
Evidence that emission and absorption outflows in quasars are related

We analyse VLT/X-shooter data for seven quasars, where we study the relationships between their broad absorption line (BAL) and emission line outflows. We find (1) the luminosity of the [O iii] λ5007 emission profile decreases with increasing electron number density ($n_\mathrm{e}$) derived from the BAL outflow in the same quasar, (2) the measured velocity widths from the [O iii] emission features and C iv absorption troughs in the same object are similar, and (3) the mean radial velocity derived from the BAL outflow is moderately larger than the one from the [O iii] emission outflow. These findings are consistent with [O iii] and BAL outflows being different manifestations of the same wind, and the observed relationships are likely a reflection of the outflow density distribution. When we have outflows with smaller distances to the central source, their $n_\mathrm{e}$ is higher. Therefore, the [O iii] emission is collisionally de-excited and the [O iii] luminosity is then suppressed. Comparisons to previous studies show that the objects in our sample exhibit broad [O iii] emission features similar to the ones in extremely red quasars (ERQs). This might imply that BAL quasars and ERQs have the same geometry of outflows or are at a similar evolutionary stage. We find that the physical parameters derived from the BAL outflows can explain the amount of observed [O iii] luminosity, which strengthens our claim that both BAL and [O iii] outflows are from the same wind. These estimates can be tested with upcoming James Webb Space Telescope, which will be able to spatially resolve some of the ionized gas outflows.
Time constant of the transverse-field demagnetization of superconducting stacks of tapes

Stacks of REBCO tapes can trap large amounts of magnetic fields and can stay magnetized for long periods of times. This makes them an interesting option for major engineering applications such as motors, generators and magnetic bearings. When subjected to alternating magnetic fields transverse to the magnetization (or cross fields), superconducting tapes face a reduction in the trapped field, and thus it is the goal of this paper to understand the influence of all parameters in the cross field demagnetization of stacks of tapes. Major parameter dependencies considered for the scope of this paper are cross field amplitude and frequency, tape width, tape thickness (from 1 to 20 µm), and number of tapes (up to 20). This article also provides a systemic study of the relaxation time constant τ, which can be used to estimate the cross-field demagnetization decay for high number of cycles. Modeling is based on the Minimum Electro-Magnetic Entropy Production method, and it is shown that the 2D model gives very accurate results for long samples when compared with 3D model. Analytical formulas for large number of cycles have been devised. The results show that when the cross field amplitude is above the penetration field of one tape, the stack always fully demagnetizes, roughly in exponential decay. Increasing the number of tapes only increases the relaxation time. The formulas derived also hold when validated against numerical results, and can be used for quick approximation of decay constant. They also show that the cause of the decreases of cross field demagnetization with number of tapes is the increase in the self-inductance of the magnetization currents. The trends and insights obtained for cross field demagnetization for stacks are thus very beneficial for engineers and scientists working with superconducting magnet design and applications.
Impact of a minority relativistic electron tail interacting with a thermal plasma containing high-atomic-number impurities

A minority relativistic electron component can arise in both laboratory and naturally-occurring plasmas. In the presence of high-atomic-number ion species, the ion charge state distribution at low bulk electron temperature can be dominated by relativistic electrons, even though their density is orders of magnitude lower. This is due to the relativistic enhancement of the collisional excitation and ionization cross sections. The resulting charge state effect can dramatically impact the radiative power loss rate and the related Bethe stopping power of relativistic electrons in a dilute plasma.
The 3d twisted index and wall-crossing

We study the twisted index of 3d \mathcal{N}=2𝒩=2 supersymmetric gauge theories on S^1 \times \SigmaS1×Σ in the presence of a real FI parameter deformation. This parameter induces a 1d FI parameter for the effective supersymmetric quantum mechanics on S^1S1. Using supersymmetric localisation, the twisted index can be expressed as a contour integral. We show that the contour prescription is modified in the presence of the 1d FI parameter, leading to wall-crossing phenomena for the twisted index. In particular, we derive a general wall-crossing formula for abelian gauge theories. We also examine the origin of wall-crossing as change of stability condition in the algebro-geometric interpretation of the twisted index. These ideas are illustrated for abelian theories with \mathcal{N}=4𝒩=4 supersymmetry and in a non-abelian example that reproduces wall-crossing phenomena associated to moduli spaces of stable pairs.
Hierarchical Character Embeddings: Learning Phonological and Semantic Representations in Languages of Logographic Origin Using Recursive Neural Networks

Logographs (Chinese characters) have recursive structures (i.e. hierarchies of sub-units in logographs) that contain phonological and semantic information, as developmental psychology literature suggests that native speakers leverage on the structures to learn how to read. Exploiting these structures could potentially lead to better embeddings that can benefit many downstream tasks. We propose building hierarchical logograph (character) embeddings from logograph recursive structures using treeLSTM, a recursive neural network. Using recursive neural network imposes a prior on the mapping from logographs to embeddings since the network must read in the sub-units in logographs according to the order specified by the recursive structures. Based on human behavior in language learning and reading, we hypothesize that modeling logographs’ structures using recursive neural network should be beneficial. To verify this claim, we consider two tasks (1) predicting logographs’ Cantonese pronunciation from logographic structures and (2) language modeling. Empirical results show that the proposed hierarchical embeddings outperform baseline approaches. Diagnostic analysis suggests that hierarchical embeddings constructed using treeLSTM is less sensitive to distractors, thus is more robust, especially on complex logographs.
AdaBits: Neural Network Quantization With Adaptive Bit-Widths

Deep neural networks with adaptive configurations have gained increasing attention due to the instant and flexible deployment of these models on platforms with different resource budgets. In this paper, we investigate a novel option to achieve this goal by enabling adaptive bit-widths of weights and activations in the model. We first examine the benefits and challenges of training quantized model with adaptive bit-widths, and then experiment with several approaches including direct adaptation, progressive training and joint training. We discover that joint training is able to produce comparable performance on the adaptive model as individual models. We also propose a new technique named Switchable Clipping Level (S-CL) to further improve quantized models at the lowest bit-width. With our proposed techniques applied on a bunch of models including MobileNet V1/V2 and ResNet50, we demonstrate that bit-width of weights and activations is a new option for adaptively executable deep neural networks, offering a distinct opportunity for improved accuracy-efficiency trade-off as well as instant adaptation according to the platform constraints in real-world applications.
J-MoDL: Joint Model-Based Deep Learning for Optimized Sampling and Reconstruction

Modern MRI schemes, which rely on compressed sensing or deep learning algorithms to recover MRI data from undersampled multichannel Fourier measurements, are widely used to reduce the scan time. The image quality of these approaches is heavily dependent on the sampling pattern. In this article, we introduce a continuous strategy to optimize the sampling pattern and the network parameters jointly. We use a multichannel forward model, consisting of a non-uniform Fourier transform with continuously defined sampling locations, to realize the data consistency block within a model-based deep learning image reconstruction scheme. This approach facilitates the joint and continuous optimization of the sampling pattern and the CNN parameters to improve image quality. We observe that the joint optimization of the sampling patterns and the reconstruction module significantly improves the performance of most deep learning reconstruction algorithms. The source code of the proposed joint learning framework is available at https://github.com/hkaggarwal/J-MoDL.
SalvageDNN: salvaging deep neural network accelerators with permanent faults through saliency-driven fault-aware mapping

Deep neural networks (DNNs) have proliferated in most of the application domains that involve data processing, predictive analysis and knowledge inference. Alongside the need for developing highly performance-efficient DNN accelerators, there is an utmost need to improve the yield of the manufacturing process in order to reduce the per unit cost of the DNN accelerators. To this end, we present ‘SalvageDNN’, a methodology to enable reliable execution of DNNs on the hardware accelerators with permanent faults (typically due to imperfect manufacturing processes). It employs a fault-aware mapping of different parts of a given DNN on the hardware accelerator (subjected to faults) by leveraging the saliency of the DNN parameters and the fault map of the underlying processing hardware. We also present novel modifications in a systolic array design to further improve the yield of the accelerators while ensuring reliable DNN execution using ‘SalvageDNN’ and negligible overheads in terms of area, power/energy and performance. This article is part of the theme issue ‘Harmonizing energy-autonomous computing and intelligence’.
The Costs of Implementing Vaccination With the RTS,S Malaria Vaccine in Five Sub-Saharan African Countries

Background. The World Health Organization has recommended pilot implementation of a candidate vaccine against malaria (RTS,S/AS01) in selected sub-Saharan African countries. This exploratory study aimed to estimate the costs of implementing RTS,S in Burkina Faso, Ghana, Kenya, Mozambique, and Tanzania. Methods. Key informants of the expanded program on immunization at all levels in each country were interviewed on the resources required for implementing RTS,S for routine vaccination. Unit prices were derived from the same sources or from international price lists. Incremental costs in 2015 US dollars were aggregated per fully vaccinated child (FVC). It was assumed the four vaccine doses were either all delivered at health facilities or the fourth dose was delivered in an outreach setting. Results. The costs per FVC ranged from US$25 (Burkina Faso) to US$37 (Kenya) assuming a vaccine price of US$5 per dose. Across countries, recurrent costs represented the largest share dominated by vaccines (including wastage) and supply costs. Non-recurrent costs varied substantially across countries, mainly because of differences in needs for hiring personnel, in wages, in cold-room space, and equipment. Recent vaccine introductions in the countries may have had an impact on resource availability for a new vaccine implementation. Delivering the fourth dose in outreach settings raised the costs, mostly fuel, per FVC by less than US$1 regardless of the country. Conclusions. This study provides relevant information for donors and decision makers about the cost of implementing RTS,S. Variations within and across countries are important and the unknown future price per dose and wastage rate for this candidate vaccine adds substantially to the uncertainty about the actual costs of implementation.
Long Non-coding RNA Maternally Expressed 3 Increases the Expression of Neuron-Specific Genes by Targeting miR-128-3p in All-Trans Retinoic Acid-Induced Neurogenic Differentiation From Amniotic Epithelial Cells

MicroRNA (miR)-128-3p is a brain-enriched miRNA that participates in the regulation of neural cell differentiation and the protection of neurons, but the mechanisms by which miR-128-3p regulates its target and downstream genes to influence cell fate from adult stem cells are poorly understood. In this study, we show down-regulation of miR-128-3p during all-trans retinoic acid (ATRA)-induced neurogenic differentiation from amniotic epithelial cells (AECs). We investigated miR-128-3p in both the Notch pathway and in the expression of neuron-specific genes predicted to be involved in miR-128-3p signaling to elucidate its role in the genetic regulation of downstream neurogenic differentiation. Our results demonstrate that miR-128-3p is a negative regulator for the transcription of the neuron-specific genes β III-tubulin, neuron-specific enolase (NSE), and polysialic acid-neural cell adhesion molecule (PSA-NCAM) via targeting Jagged 1 to inhibit activation of the Notch signaling pathway. We also used bioinformatics algorithms to screen for miR-128-3p interactions with long non-coding (lnc) RNA and circular RNA as competing endogenous RNAs to further elucidate underlying down-regulated molecular mechanisms. The lncRNA maternally expressed 3 is up-regulated by the ATRA/cAMP/CREB pathway, and it, in turn, is directly down-regulated by miR-128-3p to increase the amount of neuron differentiation. Endogenous miRNAs are, therefore, involved in neurogenic differentiation from AECs and should be considered during the development of effective cell transplant therapies for the treatment of neurodegenerative disease.
Pliocene Avian Footprints from the Lake Eyre Basin, South Australia

ABSTRACT The tetrapod trace fossil record of Australia has, until recently, been poorly studied relative to records on other continents. In the last few decades, a series of studies have revealed that these traces are more common than was previously thought. They have provided fossil evidence of taxa for which no skeletal fossils are known and have been found in areas of the country where no skeletal fossils occur. We describe a series of footprints from the Main Body Tirari Formation in the Lake Eyre Basin representing the first avian footprints published from the Australian Pliocene. Skeletal fossils from this member of the Tirari Formation are rare and are restricted to a few mammalian taxa. Three avian ichnotaxa are identified here, including Anatipeda alfi, Phoenicopterichnum isp. , and Koreanaornis sirin (hamanensis), with several prints potentially belonging to a second species of Koreanaornis. We also clarify several contradictions relating to the relevant avian ichnotaxonomy and propose an unambiguous method for measuring digit divarication in avian footprints with curved digital traces. The footprints described here indicate that flamingos, a variety of waders, and swans or pelicans were present when the lacustrine sediments of the Main Body Tirari Formation were laid down. These prints highlight the potential for more trace fossils to be found in the region, from a time period poorly represented by body fossils.
Lesion Sterilization and Tissue Repair in Nonvital Primary Teeth: An In vivo Study

Aim and Objective: The aim of this study is to evaluate the clinical and radiographic success of endodontic treatment of nonvital primary teeth using lesion sterilization and tissue repair (LSTR) therapy. Materials and Methods: A sample size of 40 primary molars from 34 children of 3–10 years of age were selected for the study. Nonvital primary molars were treated using LSTR technique with a combination of antibacterial drugs comprising ciprofloxacin, metronidazole, and doxycycline with propylene glycol as vehicle. The procedure was completed in a single appointment. Only analgesics were prescribed postoperatively. Postoperative evaluation was done after 1, 3, 6, and 12 months. Results: Clinical signs and symptoms got resolved in the 1st-month follow-up itself and continued in the subsequent visits. Radiographic evaluation showed a gradual improvement in success rates through the 12-month follow-up period. Conclusion: LSTR technique can be considered as an alternative treatment option for nonvital primary teeth avoiding a course of systemic antibiotics to the children. Doxycycline can be used in the drug combination instead of minocycline which also gives higher clinical and radiographic success.
Effect of rehabilitation education on pain, knee stiffness and performance difficulty in patients undergoing knee replacement surgery: A randomized clinical trial

Objective: To determine the effect of rehabilitation education on pain, knee stiffness and performance difficulty in patients undergoing knee replacement surgery. Methods: This randomized clinical trial study was performed on 96 patients undergoing knee replacement surgery, who were randomly divided into two groups: the control group and the intervention group, with 48 patients in each group. In the intervention group, the patients received educational intervention in four stages (one day before surgery, 24 h and 48 h later, upon discharge from the hospital. In the control group, only the routine of the hospital was performed. Questionnaires were completed before and 6 weeks after the intervention. Results: The mean scores of pain, knee stiffness and performance difficulty were significantly decreased in the intervention group (P=0.01). Compared to the control group, the intervention group had a better outcome of the illness, including pain, knee stiffness and performance difficulty (P=0.001). Conclusion: Rehabilitation education could be a suitable way to improve the surgical outcomes of patients undergoing total knee replacement.
Effect of psychological nursing combined with rehabilitation training on adl in patients with craniocerebral injury

Objectives: The objective of this study is to explore the effect of rehabilitation training combined with psychological nursing on the daily life ability of patients with craniocerebral injury. Methods: Forty-eight patients with craniocerebral injury from August 2015 to April 2017 were selected as the study participants, and they were randomly divided into control group and observation group. Twenty-four cases in the control group were treated with routine care, and 24 cases in the observation group were treated with psychological nursing and rehabilitation training. The exercise function, daily activity ability, and satisfaction degree of the two groups were analyzed. Results: Compared with the observation group, the recovery of exercise ability was lower in the control group than that in the observation group, and there was a statistically significant difference between the two groups (P < 0.05). Compared with the control group, the recovery of daily living activity of patients in the observation group was more remarkable, and there was a statistically significant difference between the two groups (P < 0.05). The patients' satisfaction in the control group was significantly lower than that in the observation group, and the difference between the two groups was statistically significant (P < 0.05). Conclusions: The application of rehabilitation training and psychological nursing in clinical nursing of patients with craniocerebral injury can speed up the recovery function of patients.
Cheiloscopy: Association of lip prints in different skeletal malocclusions

Aim: The aim of this study was to assess the association between lip print pattern and different types of skeletal malocclusion. Materials and Methods: A sample of 60 individuals (18–30 years old) with skeletal Class I, Class II Division 1, Class II Division 2, and Class III malocclusion as confirmed by Angle between point A and point B. angle were taken for the study and were named as Groups I, II, III, and IV, respectively. Lip print was recorded by lipstick-paper method and was classified according to Tsuchihashi classification as Type I, Type I', Type II, Type III, Type IV, Type V. Results: It was seen that partial vertical groove (PVG) lip pattern was most prevalent for all the groups. In skeletal Class I, PVG lip pattern was most prevalent, whereas in skeletal Class II Division 1, intersecting groove, in skeletal Class II Division 2, PVG, and in skeletal Class III, complete vertical groove were prevalent. The difference between the four malocclusion groups of each type of lip print was significant except for Type II. Conclusion: Lip print will help in sorting out participants in more reliable manner in cases of mass disaster or criminal investigations. Furthermore, they help in predicting type of malocclusion beforehand for the successful execution of preventive and interceptive orthodontic procedures.