Reviewerly/exHarmony huggingface dataset:
Motion-aware Memory Network for Fast Video Salient Object Detection

Previous methods based on 3DCNN, convLSTM, or optical flow have achieved great success in video salient object detection (VSOD). However, they still suffer from high computational costs or poor quality of the generated saliency maps. To solve these problems, we design a space-time memory (STM)-based network, which extracts useful temporal information of the current frame from adjacent frames as the temporal branch of VSOD. Furthermore, previous methods only considered single-frame prediction without temporal association. As a result, the model may not focus on the temporal information sufficiently. Thus, we initially introduce object motion prediction between inter-frame into VSOD. Our model follows standard encoder--decoder architecture. In the encoding stage, we generate high-level temporal features by using high-level features from the current and its adjacent frames. This approach is more efficient than the optical flow-based methods. In the decoding stage, we propose an effective fusion strategy for spatial and temporal branches. The semantic information of the high-level features is used to fuse the object details in the low-level features, and then the spatiotemporal features are obtained step by step to reconstruct the saliency maps. Moreover, inspired by the boundary supervision commonly used in image salient object detection (ISOD), we design a motion-aware loss for predicting object boundary motion and simultaneously perform multitask learning for VSOD and object motion prediction, which can further facilitate the model to extract spatiotemporal features accurately and maintain the object integrity. Extensive experiments on several datasets demonstrated the effectiveness of our method and can achieve state-of-the-art metrics on some datasets. The proposed model does not require optical flow or other preprocessing, and can reach a speed of nearly 100 FPS during inference.
Locally Supervised Learning with Periodic Global Guidance

Locally supervised learning aims to train a neural network based on a local estimation of the global loss function at each decoupled module of the network. Auxiliary networks are typically appended to the modules to approximate the gradient updates based on the greedy local losses. Despite being advantageous in terms of parallelism and reduced memory consumption, this paradigm of training severely degrades the generalization performance of neural networks. In this paper, we propose Periodically Guided local Learning (PGL), which reinstates the global objective repetitively into the local-loss based training of neural networks primarily to enhance the model's generalization capability. We show that a simple periodic guidance scheme begets significant performance gains while having a low memory footprint. We conduct extensive experiments on various datasets and networks to demonstrate the effectiveness of PGL, especially in the configuration with numerous decoupled modules.
What Can Be Learnt With Wide Convolutional Neural Networks?

Understanding how convolutional neural networks (CNNs) can efficiently learn high-dimensional functions remains a fundamental challenge. A popular belief is that these models harness the local and hierarchical structure of natural data such as images. Yet, we lack a quantitative understanding of how such structure affects performance, e.g., the rate of decay of the generalisation error with the number of training samples. In this paper, we study infinitely-wide deep CNNs in the kernel regime. First, we show that the spectrum of the corresponding kernel inherits the hierarchical structure of the network, and we characterise its asymptotics. Then, we use this result together with generalisation bounds to prove that deep CNNs adapt to the spatial scale of the target function. In particular, we find that if the target function depends on low-dimensional subsets of adjacent input variables, then the decay of the error is controlled by the effective dimensionality of these subsets. Conversely, if the target function depends on the full set of input variables, then the error decay is controlled by the input dimension. We conclude by computing the generalisation error of a deep CNN trained on the output of another deep CNN with randomly-initialised parameters. Interestingly, we find that, despite their hierarchical structure, the functions generated by infinitely-wide deep CNNs are too rich to be efficiently learnable in high dimension.
Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities

Recently proposed methods in data subset selection, that is active learning and active sampling, use Fisher information, Hessians, similarity matrices based on gradients, and gradient lengths to estimate how informative data is for a model's training. Are these different approaches connected, and if so, how? We revisit the fundamentals of Bayesian optimal experiment design and show that these recently proposed methods can be understood as approximations to information-theoretic quantities: among them, the mutual information between predictions and model parameters, known as expected information gain or BALD in machine learning, and the mutual information between predictions of acquisition candidates and test samples, known as expected predictive information gain. We develop a comprehensive set of approximations using Fisher information and observed information and derive a unified framework that connects seemingly disparate literature. Although Bayesian methods are often seen as separate from non-Bayesian ones, the sometimes fuzzy notion of "informativeness" expressed in various non-Bayesian objectives leads to the same couple of information quantities, which were, in principle, already known by Lindley (1956) and MacKay (1992).
Search for or Navigate to? Dual Adaptive Thinking for Object Navigation

"Search for" or "Navigate to"? When finding an object, the two choices always come up in our subconscious mind. Before seeing the target, we search for the target based on experience. After seeing the target, we remember the target location and navigate to. However, recently methods in object navigation field almost only consider using object association to enhance "search for" phase while neglect the importance of "navigate to" phase. Therefore, this paper proposes the dual adaptive thinking (DAT) method to flexibly adjust the different thinking strategies at different navigation stages. Dual thinking includes search thinking with the object association ability and navigation thinking with the target location ability. To make the navigation thinking more effective, we design the target-oriented memory graph (TOMG) to store historical target information and the target-aware multi-scale aggregator (TAMSA) to encode the relative target position. We assess our methods on the AI2-Thor dataset. Compared with the state-of-the-art (SOTA) method, our method reports 10.8%, 21.5% and 15.7% increase in success rate (SR), success weighted by path length (SPL) and success weighted by navigation efficiency (SNE), respectively.
Multimodal Neural Machine Translation with Search Engine Based Image Retrieval

Recently, numbers of works shows that the performance of neural machine translation (NMT) can be improved to a certain extent with using visual information. However, most of these conclusions are drawn from the analysis of experimental results based on a limited set of bilingual sentence-image pairs, such as Multi30K. In these kinds of datasets, the content of one bilingual parallel sentence pair must be well represented by a manually annotated image, which is different with the actual translation situation. Some previous works are proposed to addressed the problem by retrieving images from exiting sentence-image pairs with topic model. However, because of the limited collection of sentence-image pairs they used, their image retrieval method is difficult to deal with the out-of-vocabulary words, and can hardly prove that visual information enhance NMT rather than the co-occurrence of images and sentences. In this paper, we propose an open-vocabulary image retrieval methods to collect descriptive images for bilingual parallel corpus using image search engine. Next, we propose text-aware attentive visual encoder to filter incorrectly collected noise images. Experiment results on Multi30K and other two translation datasets show that our proposed method achieves significant improvements over strong baselines.
Using Intervention Mapping protocol to design a nutrition education material

Introduction: Evidence-based and culturally acceptable educational materials have been shown to be an effective tool in promoting healthy eating. Objective: To design a printed nutritional education material using Intervention Mapping protocol for a Lifestyle Clinic. Methods: Four of the steps of the Intervention Mapping protocol were used: 1) needs assessment, 2 identification of behavioral outcomes, performance objectives, and change objectives, 3) selection of theory-based methods and strategies and 4) program development. Results: The Logical Model PRECEDE was built, the matrix of change objectives based on the determinants of knowledge, identification, availability, and information processing was used as a theoretical model. A 22-page magazine was obtained with content, images, text and writing based on the Intervention Mapping steps. Conclusion: The Intervention Mapping process provided evidence-based and participatory inputs for the design of the educational material of the Lifestyle Clinic.
The effectiveness of e-learning among students with special needs in light of the Corona pandemic from the point of view of their families in Jordan

The spread of the Corona pandemic has led to the emergence of many challenges and needs, including the need to provide educational services electronically. The students with special needs are considered a sensitive group to any changes in the educational process, this study aimed to evaluate the effectiveness of e-learning among students with special hearing needs during the Corona pandemic from the point of view of their parents in Aqaba Governorate, south of Jordan. The researcher analyzed the responses of (35) families of students with special hearing needs to the questionnaire prepared in advance to achieve the goal of this study. The results concluded that there is an average level of effectiveness of e-learning among students with special needs from the point of view of their parents, and that there is a strong statistical relationship for the level of effectiveness according to the age variable of students with special needs from the point of view of their parents.
Block avoiding point sequencings of partial Steiner systems

A partial $(n,k,t)_\lambda$-system is a pair $(X,\mathcal{B})$ where $X$ is an $n$-set of vertices and $\mathcal{B}$ is a collection of $k$-subsets of $X$ called blocks such that each $t$-set of vertices is a subset of at most $\lambda$ blocks. A sequencing of such a system is a labelling of its vertices with distinct elements of $\{0,\ldots,n-1\}$. A sequencing is $\ell$-block avoiding or, more briefly, $\ell$-good if no block is contained in a set of $\ell$ vertices with consecutive labels. Here we give a short proof that, for fixed $k$, $t$ and $\lambda$, any partial $(n,k,t)_\lambda$-system has an $\ell$-good sequencing for some $\ell=\Theta(n^{1/t})$ as $n$ becomes large. This improves on results of Blackburn and Etzion, and of Stinson and Veitch. Our result is perhaps of most interest in the case $k=t+1$ where results of Kostochka, Mubayi and Verstra\"{e}te show that the value of $\ell$ cannot be increased beyond $\Theta((n \log n)^{1/t})$. A special case of our result shows that every partial Steiner triple system (partial $(n,3,2)_1$-system) has an $\ell$-good sequencing for each positive integer $\ell \leq 0.0908\,n^{1/2}$.
Bayesian Active Learning for Sim-to-Real Robotic Perception

While learning from synthetic training data has recently gained an increased attention, in real-world robotic applications, there are still performance deficiencies due to the so-called Sim-to-Real gap. In practice, this gap is hard to resolve with only synthetic data. Therefore, we focus on an efficient acquisition of real data within a Sim-to-Real learning pipeline. Concretely, we employ deep Bayesian active learning to minimize manual annotation efforts and devise an autonomous learning paradigm to select the data that is considered useful for the human expert to annotate. To achieve this, a Bayesian Neural Network (BNN) object detector providing reliable uncertainty estimates is adapted to infer the informativeness of the unlabeled data. Furthermore, to cope with mis-alignments of the label distribution in uncertainty-based sampling, we develop an effective randomized sampling strategy that performs favorably compared to other complex alternatives. In our experiments on object classification and detection, we show benefits of our approach and provide evidence that labeling efforts can be reduced significantly. Finally, we demonstrate the practical effectiveness of this idea in a grasping task on an assistive robot.
Assessing the Early Bird Heuristic (for Predicting Project Quality)

Before researchers rush to reason across all available data or try complex methods, perhaps it is prudent to first check for simpler alternatives. Specifically, if the historical data has the most information in some small region, perhaps a model learned from that region would suffice for the rest of the project. To support this claim, we offer a case study with 240 projects, where we find that the information in those projects "clump" towards the earliest parts of the project. A quality prediction model learned from just the first 150 commits works as well, or better than state-of-the-art alternatives. Using just this "early bird" data, we can build models very quickly and very early in the project life cycle. Moreover, using this early bird method, we have shown that a simple model (with just a few features) generalizes to hundreds of projects. Based on this experience, we doubt that prior work on generalizing quality models may have needlessly complicated an inherently simple process. Further, prior work that focused on later-life cycle data needs to be revisited since their conclusions were drawn from relatively uninformative regions. Replication note: all our data and scripts are available here: https://github.com/snaraya7/early-bird
The cognitive profile in Slovak children with autism spectrum disorders

This study aimed to identify strengths and weaknesses in a cognitive profile of children with Autism Spectrum Disorders (ASD) and to study relationship between the triad of impairments in ASD, delayed achievement of speech milestones and cognitive abilities.Our sample consisted of 30 children, male-to-female ratio 5:1, diagnosed with ASD using the Autism Diagnostic Interview -Revised and the Autism Diagnostic Observation Schedule.Results showed significant variations in their cognitive abilities as measured the Woodcock-Johnson International Editions.Cognitive efficiency, consisting of Processing speed and working memory, has been significantly reduced, yet individuals with ASD performed well on fluid intelligence, suggesting possible underestimation of their performance assessed with tests standardized for neurotypical population.We also identified three negative predictors of their overall IQ: inflexible adherence to specific non-functional routines or rituals, persistent preoccupation with parts of objects or non-functional elements of play materials and a delayed use of sentences.
On the Classification of Binary Completely Transitive Codes with Almost-Simple Top-Group

A code $C$ in the Hamming metric, that is, is a subset of the vertex set $V\varGamma$ of the Hamming graph $\varGamma=H(m,q)$, gives rise to a natural distance partition $\{C,C_1,\ldots,C_ρ\}$, where $ρ$ is the covering radius of $C$. Such a code $C$ is called completely transitive if the automorphism group $\rm{Aut}(C)$ acts transitively on each of the sets $C$, $C_1$, \ldots, $C_ρ$. A code $C$ is called $2$-neighbour-transitive if $ρ\geq 2$ and $\rm{Aut}(C)$ acts transitively on each of $C$, $C_1$ and $C_2$. Let $C$ be a completely transitive code in a binary ($q=2$) Hamming graph having full automorphism group $\rm{Aut}(C)$ and minimum distance $δ\geq 5$. Then it is known that $\rm{Aut}(C)$ induces a $2$-homogeneous action on the coordinates of the vertices of the Hamming graph. The main result of this paper classifies those $C$ for which this induced $2$-homogeneous action is not an affine, linear or symplectic group. We find that there are $13$ such codes, $4$ of which are non-linear codes. Though most of the codes are well-known, we obtain several new results. First, a new non-linear completely transitive code is constructed, as well as a related non-linear code that is $2$-neighbour-transitive but not completely transitive. Moreover, new proofs of the complete transitivity of several codes are given. Additionally, we answer the question of the existence of distance-regular graphs related to the completely transitive codes appearing in our main result.
Cryptanalysis of the quantum public-key cryptosystem OTU under heuristics from combinatorial statements

Abstract The knapsack cryptography is the public-key cryptography whose security depends mainly on the hardness of the subset sum problem. Many of knapsack schemes were broken by low-density attacks, which are attack methods to use the situation that a shortest vector or a closest vector in a lattice corresponds to a solution of the subset sum problem. For the case when the Hamming weight of a solution for a random instance of the subset sum problem is arbitrary, if the density is less than 0.9408, then the instance can be solvable almost surely by a single call of lattice oracle. This fact was theoretically shown by Coster et al. In Crypto 2000, Okamoto, Tanaka and Uchiyama introduced the concept of quantum public key cryptosystems and proposed a knapsack cryptosystem, so-called OTU scheme. However, no known algorithm breaks the OTU scheme. In this paper, we introduce some combinatorial statements to describe necessary condition for the failure of low density attacks. More precisely, we give better heuristics than Gaussian heuristics for minimum norms of orthogonal lattices. Consequently, we show that the OTU scheme can be broken under these heuristics.
Design optimization of a LightWeight category combat robot

The DotBotz Robotics Team develops combat and autonomous robots, and among combat robots, there is a LightWeight category robot (27.2 Kg), developed in 2019 for the Winter Challenge XV.During the competition, the project presented problems in your structure, visa what O material was fragile at streaming in movement, that it was not efficient; and in the traction of its wheels, which made locomotion difficult.Based on the obstacles presented the need to optimize the project arose, aiming at greater competitiveness and also learning about concepts such as materials, mass transmission and distribution.To solve the problems to which the robot was submitted, the solutions found and applied at the project were changes at the method in streaming in movement in between at wheels, in the metal alloy of the frame material and in the shape and material of the wheels.The development of projects like this allows greater integration with the external community, spreading knowledge about robotics through initiatives social of the team and also with the exhibition from results and competitions per means in communications virtual.
An Empirical Analysis of the Effects of Population Growth on Economic Growth in Ethiopia Using an Autoregressive Distributive Lag (ARDL) Model Approach

Between 1980 and 2020, the study looks at “An Empirical Analysis of the Effects of Population Growth on Economic Growth in Ethiopia Using an Autoregressive Distributive Lag (ARDL) Model Approach.” The appraisal coefficient of population growth (POP) and the implication is positive and significant, according to the findings of this study. However, in response to the long-term association between population expansion and economic growth in Ethiopia, the macroeconomic variables were subjected to a limit test and a broader causality test. From the finding of bound test, since the value of F-statics is greater than the upper boundary line, there are long run equilibrium relationships among RGDP, population size, foreign direct investment, personal remittance, population growth rate, rate of inflation, and gross capital formation. According to the findings of the Granger causality test, real gross domestic product can cause Ethiopian population size (POP), but population number (POP) cannot cause real gross domestic product at the same time. The motivation of doing this research was to know the sign and effects of population growth on economic growth. Finally, in order to boost Ethiopia’s economic growth, the government should implement policies that would attract foreign investors. The government should also establish a benchmark to ensure that the economy increases faster than the population.
Bayesian deep learning for error estimation in the analysis of anomalous diffusion

Abstract Modern single-particle-tracking techniques produce extensive time-series of diffusive motion in a wide variety of systems, from single-molecule motion in living-cells to movement ecology. The quest is to decipher the physical mechanisms encoded in the data and thus to better understand the probed systems. We here augment recently proposed machine-learning techniques for decoding anomalous-diffusion data to include an uncertainty estimate in addition to the predicted output. To avoid the Black-Box-Problem a Bayesian-Deep-Learning technique named Stochastic-Weight-Averaging-Gaussian is used to train models for both the classification of the diffusion model and the regression of the anomalous diffusion exponent of single-particle-trajectories. Evaluating their performance, we find that these models can achieve a well-calibrated error estimate while maintaining high prediction accuracies. In the analysis of the output uncertainty predictions we relate these to properties of the underlying diffusion models, thus providing insights into the learning process of the machine and the relevance of the output.
Measuring quality of DNA sequence data via degradation

We formulate and apply a novel paradigm for characterization of genome data quality, which quantifies the effects of intentional degradation of quality. The rationale is that the higher the initial quality, the more fragile the genome and the greater the effects of degradation. We demonstrate that this phenomenon is ubiquitous, and that quantified measures of degradation can be used for multiple purposes, illustrated by outlier detection. We focus on identifying outliers that may be problematic with respect to data quality, but might also be true anomalies or even attempts to subvert the database.