Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust and Scalable Differentiable Neural Computer for Question Answering

Deep learning models are often not easily adaptable to new tasks and require task-specific adjustments. The differentiable neural computer (DNC), a memory-augmented neural network, is designed as a general problem solver which can be used in a wide range of tasks. But in reality, it is hard to apply this model to new tasks. We analyze the DNC and identify possible improvements within the application of question answering. This motivates a more robust and scalable DNC (rsDNC). The objective precondition is to keep the general character of this model intact while making its application more reliable and speeding up its required training time. The rsDNC is distinguished by a more robust training, a slim memory unit and a bidirectional architecture. We not only achieve new state-of-the-art performance on the bAbI task, but also minimize the performance variance between different initializations. Furthermore, we demonstrate the simplified applicability of the rsDNC to new tasks with passable results on the CNN RC task without adaptions.
Auto Deep Compression by Reinforcement Learning Based Actor-Critic Structure

Model-based compression is an effective, facilitating, and expanded model of neural network models with limited computing and low power. However, conventional models of compression techniques utilize crafted features [2,3,12] and explore specialized areas for exploration and design of large spaces in terms of size, speed, and accuracy, which usually have returns Less and time is up. This paper will effectively analyze deep auto compression (ADC) and reinforcement learning strength in an effective sample and space design, and improve the compression quality of the model. The results of compression of the advanced model are obtained without any human effort and in a completely automated way. With a 4- fold reduction in FLOP, the accuracy of 2.8% is higher than the manual compression model for VGG-16 in ImageNet.
Fast Dynamic Convolutional Neural Networks for Visual Tracking

Most of the existing tracking methods based on CNN(convolutional neural networks) are too slow for real-time application despite the excellent tracking precision compared with the traditional ones. In this paper, a fast dynamic visual tracking algorithm combining CNN based MDNet(Multi-Domain Network) and RoIAlign was developed. The major problem of MDNet also lies in the time efficiency. Considering the computational complexity of MDNet is mainly caused by the large amount of convolution operations and fine-tuning of the network during tracking, a RoIPool layer which could conduct the convolution over the whole image instead of each RoI is added to accelerate the convolution and a new strategy of fine-tuning the fully-connected layers is used to accelerate the update. With RoIPool employed, the computation speed has been increased but the tracking precision has dropped simultaneously. RoIPool could lose some positioning precision because it can not handle locations represented by floating numbers. So RoIAlign, instead of RoIPool, which can process floating numbers of locations by bilinear interpolation has been added to the network. The results show the target localization precision has been improved and it hardly increases the computational cost. These strategies can accelerate the processing and make it 7x faster than MDNet with very low impact on precision and it can run at around 7 fps. The proposed algorithm has been evaluated on two benchmarks: OTB100 and VOT2016, on which high precision and speed have been obtained. The influence of the network structure and training data are also discussed with experiments.
Glow: Generative Flow with Invertible 1x1 Convolutions

Flow-based generative models (Dinh et al., 2014) are conceptually attractive due to tractability of the exact log-likelihood, tractability of exact latent-variable inference, and parallelizability of both training and synthesis. In this paper we propose Glow, a simple type of generative flow using an invertible 1x1 convolution. Using our method we demonstrate a significant improvement in log-likelihood on standard benchmarks. Perhaps most strikingly, we demonstrate that a generative model optimized towards the plain log-likelihood objective is capable of efficient realistic-looking synthesis and manipulation of large images. The code for our model is available at this https URL
Analysis of the Double Laser Emission Occurring in 1 . 55-m InAs – InP ( 113 ) B Quantum-Dot Lasers

In this paper, a theoretical model based on rate equations is used to investigate static and dynamic behaviors of InAs–InP (113)B quantum-dot (QD) lasers emitting at 1.55 m. More particularly, it is shown that two modelling approaches are required to explain the origin of the double laser emission occurring in QD lasers grown on both, GaAs and InP substrates. Numerical results are compared to experimental ones by using either a cascade or a direct relaxation channel model. The comparison demonstrates that when a direct relaxation channel is taken into account, the numerical results match very well the experimental ones and lead to a qualitative understanding of InAs–InP (113)B QD lasers. Numerical calculations for the turn-on delay are also presented. A relaxation oscillation frequency as high as 10 GHz is predicted which is very promising for the realization of directly modulated QD lasers for high-speed transmissions.
Advanced Compressor Tree Synthesis for FPGAs

This work presents novel methods for the optimization of compressor trees for FPGAs as required in many arithmetic computations. As demonstrated in recent work, important key elements for the design of efficient but fast compressor trees are target-optimized 4:2 compressors as well as generalized parallel counters (GPCs). However, the optimization of a compressor tree for minimal resources using both compressors and GPCs has not been addressed so far. As this combined optimization is a non-trivial task, three methods are proposed to find best solutions for a given problem size: 1) a heuristic that obtains compressor trees with typically less resources and fewer stages than state-of-the-art heuristics, 2) an integer linear programming (ILP)-based methodology that finds optimal compressor trees using the fewest stages possible, 3) a combined approach that partially solves the problem heuristically to reduce the search space for the ILP-based method. In all methods, the cost for pipeline registers can be included. Synthesis experiments show that the proposed methods provide pipelined compressor trees with about 40 percent less LUTs compared to trees of 2-input adders at the cost of being about 12 . 20 percent slower.
Temporal Difference Learning with Neural Networks - Study of the Leakage Propagation Problem

Temporal-Difference learning (TD) [Sutton, 1988] with function approximation can converge to solutions that are worse than those obtained by Monte-Carlo regression, even in the simple case of on-policy evaluation. To increase our understanding of the problem, we investigate the issue of approximation errors in areas of sharp discontinuities of the value function being further propagated by bootstrap updates. We show empirical evidence of this leakage propagation, and show analytically that it must occur, in a simple Markov chain, when function approximation errors are present. For reversible policies, the result can be interpreted as the tension between two terms of the loss function that TD minimises, as recently described by [Ollivier, 2018]. We show that the upper bounds from [Tsitsiklis and Van Roy, 1997] hold, but they do not imply that leakage propagation occurs and under what conditions. Finally, we test whether the problem could be mitigated with a better state representation, and whether it can be learned in an unsupervised manner, without rewards or privileged information.
Towards Radiologist-Level Accurate Deep Learning System for Pulmonary Screening

In this work, we propose advanced pneumonia and Tuberculosis grading system for X-ray images. The proposed system is a very deep fully convolutional classification network with online augmentation that outputs confidence values for diseases prevalence. Its a fully automated system capable of disease feature understanding without any offline preprocessing step or manual feature extraction. We have achieved state- of-the- art performance on the public databases such as ChestXray-14, Mendeley, Shenzhen Hospital X-ray and Belarus X-ray set.
Scalable Recommender Systems through Recursive Evidence Chains

Recommender systems can be formulated as a matrix completion problem, predicting ratings from user and item parameter vectors. Optimizing these parameters by subsampling data becomes difficult as the number of users and items grows. We develop a novel approach to generate all latent variables on demand from the ratings matrix itself and a fixed pool of parameters. We estimate missing ratings using chains of evidence that link them to a small set of prototypical users and items. Our model automatically addresses the cold-start and online learning problems by combining information across both users and items. We investigate the scaling behavior of this model, and demonstrate competitive results with respect to current matrix factorization techniques in terms of accuracy and convergence speed.
A longitudinal cohort study on the retainment of test-driven development

Background: Test-Driven Development (TDD) is an agile software development practice, which is claimed to boost both external quality of software products and developers' productivity. Aims: We want to study: (i) the TDD effects on the external quality of software products as well as the developers' productivity; and (ii) the retainment of TDD over a period of five months. Method: We conducted a (quantitative) longitudinal cohort study with 30 third-year undergraduate students in Computer Science at the University of Bari in Italy. Results: The use of TDD has a statistically significant effect neither on the external quality of software products nor on the developers' productivity. However, we observed that participants using TDD produced significantly more tests than those applying a non-TDD development process, and that the retainment of TDD is particularly noticeable in the amount of tests written. Conclusions: Our results should encourage software companies to adopt TDD because who practices TDD tends to write more tests---having more tests can come in handy when testing software systems or localizing faults---and it seems that novice developers retain TDD.
Jigsaw Puzzle Solving Using Local Feature Co-Occurrences in Deep Neural Networks

Archaeologists are in dire need of automated object reconstruction methods. Fragments reassembly is close to puzzle problems, which may be solved by computer vision algorithms. As they are often beaten on most image related tasks by deep learning algorithms, we study a classification method that can solve jigsaw puzzles. In this paper, we focus on classifying the relative position: given a couple of fragments, we compute their local relation (e.g. on top). We propose several enhancements over the state of the art in this domain, which is outperformed by our method by 25%. We propose an original dataset composed of pictures from the Metropolitan Museum of Art. We propose a greedy reconstruction method based on the predicted relative positions.
On the Equilibrium of Query Reformulation and Document Retrieval

In this paper, we study jointly query reformulation and document relevance estimation, the two essential aspects of information retrieval (IR). Their interactions are modelled as a two-player strategic game: one player, a query formulator, taking actions to produce the optimal query, is expected to maximize its own utility with respect to the relevance estimation of documents produced by the other player, a retrieval modeler; simultaneously, the retrieval modeler, taking actions to produce the document relevance scores, needs to optimize its likelihood from the training data with respect to the refined query produced by the query formulator. Their equilibrium or equilibria will be reached when both are the best responses to each other. We derive our equilibrium theory of IR using normal-form representations: when a standard relevance feedback algorithm is coupled with a retrieval model, they would share the same objective function and thus form a partnership game; by contrast, pseudo relevance feedback pursues a rather different objective than that of retrieval models, therefore the interaction between them would lead to a general-sum game (though implicitly collaborative). Our game-theoretical analyses not only yield useful insights into the two major aspects of IR, but also offer new practical algorithms for achieving the equilibrium state of retrieval which have been shown to bring consistent performance improvements in both text retrieval and item recommendation.
Distributed Actuator Selection: Achieving Optimality via a Primal-Dual Algorithm

This letter addresses the actuator selection problem, i.e., given an interconnection of asymptotically stable linear dynamical systems on a network and <inline-formula> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula> possible actuators choose <inline-formula> <tex-math notation="LaTeX">${\nu }$ </tex-math></inline-formula> among them to achieve a certain objective. In general, this is a combinatorial optimization problem which is hard to solve; convex relaxations do not usually yield an optimal solution for the original problem. In this letter we focus on a particular instance of the actuator selection problem, namely the formulation with the trace of the controllability Gramian matrix as the optimization metric, and show that such a choice gives rise to an integer linear program (LP). Using properties of integral polyhedra, we show through a sequence of reformulations that the optimal solution of this problem can be determined by means of an LP without introducing any relaxation gap. This allows us to obtain the optimal solution using a primal-dual distributed algorithm, thus providing a scalable approach to the problem of actuator placement which has been up to now performed in a centralized manner enumerating all possible placement alternatives. We illustrate the main features of our approach by means of a case study involving a simplified model of the European power grid.
Cone Phosphodiesterase-6γ’ Subunit Augments Cone PDE6 Holoenzyme Assembly and Stability in a Mouse Model Lacking Both Rod and Cone PDE6 Catalytic Subunits

Rod and cone phosphodiesterase 6 (PDE6) are key effector enzymes of the vertebrate phototransduction pathway. Rod PDE6 consists of two catalytic subunits PDE6α and PDE6β and two identical inhibitory PDE6γ subunits, while cone PDE6 is composed of two identical PDE6α’ catalytic subunits and two identical cone-specific PDE6γ’ inhibitory subunits. Despite their prominent function in regulating cGMP levels and therefore rod and cone light response properties, it is not known how each subunit contributes to the functional differences between rods and cones. In this study, we generated an rd10/cpfl1 mouse model lacking rod PDE6β and cone PDE6α’ subunits. Both rod and cone photoreceptor cells are degenerated with age and all PDE6 subunits degrade in rd10/cpfl1 mice. We expressed cone PDE6α’ in both rods and cones of rd10/cpfl1 mice by adeno-associated virus (AAV)-mediated delivery driven by the ubiquitous, constitutive small chicken β-actin promoter. We show that expression of PDE6α’ rescues rod function in rd10/cpfl1 mice, and the restoration of rod light sensitivity is attained through restoration of endogenous rod PDE6γ and formation of a functional PDE6α’γ complex. However, improved photopic cone responses were achieved only after supplementation of both cone PDE6α’ and PDE6γ’ subunits but not by PDE6α’ treatment alone. We observed a two fold increase of PDE6α’ levels in the eyes injected with both PDE6α’ plus PDE6γ’ relative to eyes receiving PDE6α’ alone. Despite the presence of both PDE6γ’ and PDE6γ, the majority of PDE6α’ formed functional complexes with PDE6γ’, suggesting that PDE6α’ has a higher association affinity for PDE6γ’ than for PDE6γ. These results suggest that the presence of PDE6γ’ augments cone PDE6 assembly and enhances its stability. Our finding has important implication for gene therapy of PDE6α’-associated achromatopsia.
High levels of alexithymia in patients with multiple sclerosis

Abstract Alexithymia is a personality trait characterized by difficulties identifying and describing feelings. Some researchers describe high levels of alexithymia among patients with multiple sclerosis (MS) but literature data on this subject are scarce. Objective: The objective of the present study was to characterize findings of alexithymia in patients with MS. Methods: This cross-sectional case-control study included 180 patients with MS and a matched control group. Data for patients with MS included disease duration, number of demyelinating relapses and degree of neurological disability, as assessed by the Expanded Disability Scale Score (EDSS). In addition, the Hospital Anxiety and Depression (HAD) scale and the Toronto Alexithymia Scale (TAS) were used. Results: There were 126 women and 54 men in each group, with median age of 37 years and median education of 16 years. Patients with MS had higher degrees of depression (p<0.01), anxiety (p=0.01) and alexithymia (p<0.01) than did control subjects. For individuals with MS, depressive traits (p<0.01), anxious traits (p=0.03), higher age (p=0.02), lower education level (p=0.02), higher degree of disability (p<0.01) and not being actively employed (p=0.03) were associated with higher rates of alexithymia. Conclusion: Alexithymia was a relevant finding in patients with MS.
Using user emotions to trigger UI adaptation

User experience is an important user interface quality factor that needs to be maintained during interaction. With this goal, we propose to adapt user interfaces to users' emotions. This requires being able to detect at runtime negative emotions to trigger adaptations. However, emotions depend on users' gender and age. This makes it difficult to identify negative emotions and their cause. This paper studies the categorization of emotions depending on users' age and gender and two user experience dimensions (aesthetics and usability). Thanks to an experiment, it proposes some emotions thresholds that can be used for detecting usability and aesthetics problems from users' age and gender. These results are used in a user interface adaptation system to infer negative emotions and user interface problems.
Soft grasping with wet adhesion: Preliminary evaluation

Locking two surfaces with minimum normal force may result in safe grasping of objects in soft robotic hands. This paper presents a preliminary approach on design and analysis of a bio-inspired soft pad that enhances the adhesion with the environment by morphological design of its surface at micro-scale. The design principle is originated from the biological wet attachment of a tree-frog toes with the surrounding environment, caused by capillary force and surface tension of a secretion film between the toe and the surface. Especially, the tree-frog's toe has a network of polygonal cells (or blocks) with grooves among them, which act as liquid reservoirs and capillary tubes. We conducted some analysis on this wet adhesion principle, showing that total normal force increases with the grooved pattern compared to the that of the flat one in wet condition. We then fabricated a micro-patterned mold, using e-beam technology, for casting grooved surface onto a silicon substrate. We also conducted preliminary investigation of the adhesion strength of the fabricated soft pad with measurement of normal force under wet and dry condition. This is the first time wet adhesion was considered in soft robotic grasping, and this research is expected to be applied in wet and high-moisture environment.
Enhancing Ebony? Common Associations With a cis-Regulatory Haplotype for Drosophila melanogaster Thoracic Pigmentation in a Japanese Population and Australian Populations

The molecular underpinnings of pigmentation diversity in Drosophila have recently emerged as a model for understanding how the evolution of different cis-regulatory variants results in common adaptive phenotypes within species. We compared sequence variation in a 5′ regulatory region harboring a modular enhancer containing a ∼0.7-kb core element contributing to abdominal melanisation in African, and a ∼0.5-kb core element contributing to thoracic pigmentation in D. melanogaster from Japan, to tropical and temperate populations from eastern Australia previously shown to be divergent in thoracic pigmentation and ebony expression. The Australian populations exhibited strong association with the core enhancer polymorphism cluster in complete association with Dark and Light phenotypes from Iriomote, Japan. Moreover, the Iriomote Light and Dark core enhancer haplotypes are common to the Australian populations in the direction predicted by pigmentation phenotype. We also confirmed the Japanese patterns of linkage disequilibrium and association of the tropical inversion In(3R)Payne with the Light enhancer haplotype in the Australian tropical light population. A worldwide survey of the ∼0.5-kb ebony control region SNPs and haplotypes in a subset of the Drosophila Genome Nexus (DGN) populations suggest origins in the sub-Saharan ancestral region surrounding Zambia and subsequent invasion following colonization out of Africa. A previous study demonstrated complex within and between population genetic architecture for abdominal pigmentation which is also correlated with thoracic pigmentation in melanized DGN sub-Saharan populations; however, the ∼0.5-kb ebony control region was not associated and both haplotypes are common even in the most intensely pigmented D. melanogaster from high altitude Ethiopia. In the Australian populations, the strong phenotypic association with the enhancer SNPs and haplotypes that at least partly regulates ebony expression in the Iriomote population, our previous work demonstrating opposing clines for thoracic pigmentation and ebony expression, where the expression cline parallels the In(3R)Payne cline, and the concerted evolution of pigmentation intensity and ebony expression under rapid experimental evolution, all point to a common adaptive evolutionary pathway in distinct populations.