Miracle-dz/UniFAR-FTUs huggingface dataset:
An energy conservative wireless sensor network model for object tracking

This study aims to find the relationship between energy consumption level and object tracking success in an object tracking sensor network (OTSN). Convenient use of energy proposes a great challenge for wireless sensor network (WSN) design and the balance between successful object tracking and low energy consumption is a tight one. To address this issue, we propose a new network operation scheme for object tracking, implement this scheme in Network Simulator 2 (ns-2) and present the obtained results of the conducted simulation experiments. The simulation results show that the proposed method can be used to track objects in a WSN network in an energy conservative manner.
The Internet and the Ability to Innovate in Latin America

In this paper we provide an overview of the situation of the Internet in Latin America and argue that, although latecomers, Latin American countries could in principle catch up at a faster pace and a lower cost. But that will depend on the environment for innovation in the countries; in that respect, the adoption of the Internet may prove to be no different than other technological changes. The paper also discusses how the degree of innovativeness in a country helps explain the extent to which new technologies may be more effectively absorbed. What is surprising about this relationship is that it is valid even when isolating the fact that countries with better telephone infrastructure are also the ones with more Internet hosts. We found that the capacity to innovate and assimilate new technologies is not just a matter of income or infrastructure endowment.
Reducing invariants and total reflexivity

Motivated by a recent result of Yoshino, and the work of Bergh on reducible complexity, we introduce reducing versions of invariants of finitely generated modules over commutative Noetherian local rings. Our main result considers modules which have finite reducing Gorenstein dimension, and determines a criterion for such modules to be totally reflexive in terms of the vanishing of Ext. Along the way we give examples and applications, and in particular, prove that a Cohen-Macaulay local ring with canonical module is Gorenstein if and only if the canonical module has finite reducing Gorenstein dimension.
Dissecting Graph Neural Networks on Graph Classification

Graph Neural Nets (GNNs) have received increasing attentions, partially due to their superior performance in many node and graph classification tasks. However, there is a lack of understanding on what they are learning and how sophisticated the learned graph functions are. In this work, we first propose Graph Feature Network (GFN), a simple lightweight neural net defined on a set of graph augmented features. We then propose a dissection of GNNs on graph classification into two parts: 1) the graph filtering, where graph-based neighbor aggregations are performed, and 2) the set function, where a set of hidden node features are composed for prediction. To test the importance of these two parts separately, we prove and leverage the connection that GFN can be derived by linearizing graph filtering part of GNN. Empirically we perform evaluations on common graph classification benchmarks. To our surprise, we find that, despite the simplification, GFN could match or exceed the best accuracies produced by recently proposed GNNs, with a fraction of computation cost. Our results provide new perspectives on both the functions that GNNs learned and the current benchmarks for evaluating them.
Dynamic task mapping onto multi-core architectures through stream rewriting

Task graphs provide an efficient model of computation for specification, analysis, and implementation of concurrent applications. In this paper, we present a novel approach for mapping the class of series-parallel task graphs onto multi-core architectures based on pattern matching. Both the topology of the graph and the state of the tasks are encoded as a stream of tokens, which is iteratively rewritten at multiple positions in parallel. Hence, our technique is most useful for compute-intensive applications that must adapt to frequently varying and unpredictable workload at runtime. Several complex examples have been evaluated on a multi-core architecture and the experimental results show the effectiveness of our approach.
Brief report: family-based group intervention for young siblings of children with chronic illness and developmental disability.

OBJECTIVE To evaluate the impact of a family-based group intervention for young siblings of children with chronic illness and developmental disability (CI/DD). METHODS Forty-three healthy siblings (ages 4-7 years) of children with CI/DD and their parents participated in an intervention designed to address sibling challenges that cut across types of diagnostic conditions. The intervention consisted of six sessions of collateral and integrated sibling-parent groups. Measures of sibling knowledge, sibling sense of connectedness with other children in similar family circumstances, and sibling global functioning were collected before and after intervention. A subsample of 17 families completed a 3-month follow-up. RESULTS Siblings' knowledge of the child's disorder and sibling connectedness increased significantly from pre- to posttreatment for both boys and girls, regardless of the nature of the brother or sister's condition. Sibling perceptions of self-competence increased from pre- to posttreatment, whereas parent reports of sibling behavioral functioning remained within the normal range. Improvements in sibling knowledge and connectedness maintained at follow-up. Parent satisfaction with the program was high. CONCLUSIONS Results support more controlled evaluations of family-based intervention to improve young sibling adaptation to CI/DD.
The Most Metal-poor Stars. V. The CEMP-no Stars in 3D and Non-LTE

We explore the nature of carbon-rich ([C/Fe]1D,LTE > +0.7), metal-poor ([Fe/H1D,LTE] < −2.0) stars in the light of post 1D,LTE literature analyses, which provide 3D–1D and NLTE–LTE corrections for iron, and 3D–1D corrections for carbon (from the CH G-band, the only indicator at lowest [Fe/H]). High-excitation C i lines are used to constrain 3D,NLTE corrections of G-band analyses. Corrections to the 1D,LTE compilations of Yoon et al. and Yong et al. yield 3D,LTE and 3D,NLTE Fe and C abundances. The number of CEMP-no stars in the Yoon et al. compilation (plus eight others) decreases from 130 (1D,LTE) to 68 (3D,LTE) and 35 (3D,NLTE). For stars with −4.5 < [Fe/H] < −3.0 in the compilation of Yong et al., the corresponding CEMP-no fractions change from 0.30 to 0.15 and 0.12, respectively. We present a toy model of the coalescence of pre-stellar clouds of the two populations that followed chemical enrichment by the first zero-heavy-element stars: the C-rich, hyper-metal-poor and the C-normal, very-metal-poor populations. The model provides a reasonable first-order explanation of the distribution of the 1D,LTE abundances of CEMP-no stars in the A(C) and [C/Fe] versus [Fe/H] planes, in the range −4.0 < [Fe/H] < −2.0. The Yoon et al. CEMP Group I contains a subset of 19 CEMP-no stars (14% of the group), four out of nine of which are binary, and which have large [Sr/Ba]1D,LTE values. The data support the conjectures of Hansen et al. and Arentsen et al. that these stars may have experienced enrichment from asymptotic giant branch stars and/or “spinstars. ”
FastDraw: Addressing the Long Tail of Lane Detection by Adapting a Sequential Prediction Network

The search for predictive models that generalize to the long tail of sensor inputs is the central difficulty when developing data-driven models for autonomous vehicles. In this paper, we use lane detection to study modeling and training techniques that yield better performance on real world test drives. On the modeling side, we introduce a novel fully convolutional model of lane detection that learns to decode lane structures instead of delegating structure inference to post-processing. In contrast to previous works, our convolutional decoder is able to represent an arbitrary number of lanes per image, preserves the polyline representation of lanes without reducing lanes to polynomials, and draws lanes iteratively without requiring the computational and temporal complexity of recurrent neural networks. Because our model includes an estimate of the joint distribution of neighboring pixels belonging to the same lane, our formulation includes a natural and computationally cheap definition of uncertainty. On the training side, we demonstrate a simple yet effective approach to adapt the model to new environments using unsupervised style transfer. By training FastDraw to make predictions of lane structure that are invariant to low-level stylistic differences between images, we achieve strong performance at test time in weather and lighting conditions that deviate substantially from those of the annotated datasets that are publicly available. We quantitatively evaluate our approach on the CVPR 2017 Tusimple lane marking challenge, difficult CULane datasets [29], and a small labeled dataset of our own and achieve competitive accuracy while running at 90 FPS.
One-Shot Image-to-Image Translation via Part-Global Learning With a Multi-Adversarial Framework

It is well known that humans can learn and recognize objects effectively from several limited image samples. However, learning from just a few images is still a tremendous challenge for existing main-stream deep neural networks. Inspired by analogical reasoning in the human mind, a feasible strategy is to “translate” the abundant images of a rich source domain to enrich the relevant yet different target domain with insufficient image data. To achieve this goal, we propose a novel, effective multi-adversarial framework (MA) based on part-global learning, which accomplishes the one-shot cross-domain image-to-image translation. In specific, we first devise a part-global adversarial training scheme to provide an efficient way for feature extraction and prevent discriminators from being overfitted. Then, a multi-adversarial mechanism is employed to enhance the image-to-image translation ability to unearth the high-level semantic representation. Moreover, a balanced adversarial loss function is presented, which aims to balance the training data and stabilize the training process. Extensive experiments demonstrate that the proposed approach can obtain impressive results on various datasets between two extremely imbalanced image domains and outperform state-of-the-art methods on one-shot image-to-image translation. Our code will be released with this paper at https://github.com/zhengziqiang/OST.
Unveiling the oldest and most massive galaxies at very high redshift

Context. The identification and characterisation of massive galaxies over a wide redshift range allow us to place stringent constraints on the cosmic history of galaxy mass assembly and on current models of galaxy formation and evolution. Aims. This work explores the existence of high-redshift massive galaxies unveiled with Spitzer +IRAC, but missed by conventional selection techniques based on optical and near-infrared observations. Methods. To this end, we used multi-wavelength imaging data available for the GOODS-South field (130 arcmin 2 ) and selected a flux-limited sample from the IRAC 3.6 μ m image to $S_{3.6}\geq 1.8 ~\mu$Jy ( m (AB) K -band image ($K>23.5$ AB). Our selection unveiled 20 galaxies on which we performed a detailed analysis. For each galaxy, we built a spectral energy distribution (SED) based on optical-to-8 μ m photometry. The SEDs were then used to estimate the photometric redshifts and to derive the main galaxies' physical properties. Further constraints were also obtained from the available X-ray and 24 μ m data. Results. The majority of the sample (14 out of 20) sources show degenerate/bimodal solutions for the photometric redshifts. These can either be heavily dust-enshrouded ($A_{\rm V}\sim2{-}4$) starbursts at $2 10^{12} L_{\odot}$, or massive post-starburst galaxies in the redshift interval $4 z dusty source, these latter objects favour a low-extinction solution, with four of them showing best-fit photo- z solutions at $z\sim4$. One galaxy, ID-6, the only source in our sample with both an X-ray and a 24 μ m detection, might be an extremely massive object at $z\sim 8$ detected during a post-starburst phase with concomitant QSO activity responsible for the 24 μ m and X-ray emissions (although a lower- z solution is not excluded). Conclusions. Our investigation of Spitzer -selected galaxies that have very red SEDs and completely undetected in the optical reveals a potential population of massive galaxies at $z\geq4$, which appear to include significant AGN emissions. These sources may be the oldest stellar systems at $z\sim4$, given that the estimated ages are close to the age of the Universe at that redshift. We found that these, previously unrecognised, optically obscured objects might provide an important contribution to the massive end ($M>10^{11}~M_{\odot}$) of the high- z stellar mass function, and they would almost double it. Our evidence in these mature high- z galaxies of the widespread presence of hidden AGNs may have important implications for galaxy formation, due to their feedback effects on the surrounding ISM.
Let's Push Things Forward: A Survey on Robot Pushing

As robots make their way out of factories into human environments, outer space, and beyond, they require the skill to manipulate their environment in multifarious, unforeseeable circumstances. With this regard, pushing is an essential motion primitive that dramatically extends a robot's manipulation repertoire. In this work, we review the robotic pushing literature. While focusing on work concerned with predicting the motion of pushed objects, we also cover relevant applications of pushing for planning and control. Beginning with analytical approaches, under which we also subsume physics engines, we then proceed to discuss work on learning models from data. In doing so, we dedicate a separate section to deep learning approaches which have seen a recent upsurge in the literature. Concluding remarks and further research perspectives are given at the end of the paper.
Load Balance Properties of Distributed Data Layouts for Clustered MOD Servers

Large scale storage servers that provide location transparent, interactive access to hundreds or thousands of concurrent, independent clients will be important components of hte furture information super-highway infrastructure. Two key requirements of such servers are as follows: support high parallelism and concurrency in data access to allow large number of access to the same or different data. Second, support independent interactive playout control operations such as fast-forward, rewind, slow-play, pause, resume, random access etc. with minimal latency. This paper assumes a distributed storage server architecture consisting of several high performance storage nodes interconnected by a high speed desk area. Read complete abstract on page 2.
Quantitative Analysis of Cloud Function Evolution in the AWS Serverless Application Repository

The serverless computing ecosystem is growing due to interest by software engineers. Beside Function-as-a-Service (FaaS) and Backend-as-a-Service (BaaS) systems, developer-oriented tools such as deployment and debugging frameworks as well as cloud function repositories enable the rapid creation of wholly or partially serverless applications. This study presents first insights into how cloud functions (Lambda functions) and composite serverless applications offered through the AWS Serverless Application Repository have evolved over the course of one year. Specifically, it outlines information on cloud function and function-based application offering models and descriptions, high-level implementation statistics, and evolution including change patterns over time. Several results are presented in live paper style, offering hyperlinks to continuously updated figures to follow the evolution after publication date.
Differentiable Game Mechanics

Deep learning is built on the foundational guarantee that gradient descent on an objective function converges to local minima. Unfortunately, this guarantee fails in settings, such as generative adversarial nets, that exhibit multiple interacting losses. The behavior of gradient-based methods in games is not well understood -- and is becoming increasingly important as adversarial and multi-objective architectures proliferate. In this paper, we develop new tools to understand and control the dynamics in n-player differentiable games. The key result is to decompose the game Jacobian into two components. The first, symmetric component, is related to potential games, which reduce to gradient descent on an implicit function. The second, antisymmetric component, relates to Hamiltonian games, a new class of games that obey a conservation law akin to conservation laws in classical mechanical systems. The decomposition motivates Symplectic Gradient Adjustment (SGA), a new algorithm for finding stable fixed points in differentiable games. Basic experiments show SGA is competitive with recently proposed algorithms for finding stable fixed points in GANs -- while at the same time being applicable to, and having guarantees in, much more general cases.
The Order of Things: Position-Aware Network-friendly Recommendations in Long Viewing Sessions

Caching has recently attracted a lot of attention in the wireless communications community, as a means to cope with the increasing number of users consuming web content from mobile devices. Caching offers an opportunity for a win-win scenario: nearby content can improve the video streaming experience for the user, and free up valuable network resources for the operator. At the same time, recent works have shown that recommendations of popular content apps are responsible for a significant percentage of users requests. As a result, some very recent works have considered how to nudge recommendations to facilitate the network (e.g., increase cache hit rates). In this paper, we follow up on this line of work, and consider the problem of designing cache friendly recommendations for long viewing sessions; specifically, we attempt to answer two open questions in this context: (i) given that recommendation position affects user click rates, what is the impact on the performance of such network-friendly recommender solutions? (ii) can the resulting optimization problems be solved efficiently, when considering both sequences of dependent accesses (e.g., YouTube) and position preference? To this end, we propose a stochastic model that incorporates position-aware recommendations into a Markovian traversal model of the content catalog, and derive the average cost of a user session using absorbing Markov chain theory. We then formulate the optimization problem, and after a careful sequence of equivalent transformations show that it has a linear program equivalent and thus can be solved efficiently. Finally, we use a range of real datasets we collected to investigate the impact of position preference in recommendations on the proposed optimal algorithm. Our results suggest more than 30% improvement with respect to state-of-the-art methods.
Tree-wise Distribution Sensitive hashing: Efficient Maximum likelihood Classification by joint dimensionality reduction in known probabilistic settings

We consider the problem of maximum likelihood classification of a high dimensional data point y to billions of classes $x_1,. ,x_N$, where the conditional probability p(y|x) is known. In the most general case, the complexity of the brute-force method for this classification grows linearly, O(N), with the number of classes N. Efficient multiclass classification methods have been introduced to solve this problem with logarithmic complexity. However, these methods suffer from the curse of dimensionality, i.e., in large dimensions their complexity approaches $O(N)$ per query data point. In the special case where the conditional probability distribution $p(y|x)$ is a Gaussian centered at x, i.e., $p(y|x) \propto N (x,\sigma)$, the maximum likelihood classification reduces to the nearest neighbor search with the Euclidean norm. Sublinear methods based on locality sensitive hashing (LSH) have been introduced to solve an approximate version of the nearest neighbor search for high dimensional data. Inspired by these advances, here we introduce distribution sensitive hashing (DSH) to solve an approximate version of the maximum likelihood classification problem through joint dimensionality reduction. In the case of discrete probability distributions, we design TreeDSH, a universal family of distribution sensitive hashes based on the decision trees, and show that their complexity grow sub-linearly. Theory and simulation presented in this paper demonstrate that TreeDSH is more efficient than LSH-hamming and Min-Hashing schemes. Finally, we apply TreeDSH to the problem of peptide identification from mass spectrometry data.
Generative Adversarial Networks and Conditional Random Fields for Hyperspectral Image Classification

In this paper, we address the hyperspectral image (HSI) classification task with a generative adversarial network and conditional random field (GAN-CRF)-based framework, which integrates a semisupervised deep learning and a probabilistic graphical model, and make three contributions. First, we design four types of convolutional and transposed convolutional layers that consider the characteristics of HSIs to help with extracting discriminative features from limited numbers of labeled HSI samples. Second, we construct semisupervised generative adversarial networks (GANs) to alleviate the shortage of training samples by adding labels to them and implicitly reconstructing real HSI data distribution through adversarial training. Third, we build dense conditional random fields (CRFs) on top of the random variables that are initialized to the softmax predictions of the trained GANs and are conditioned on HSIs to refine classification maps. This semisupervised framework leverages the merits of discriminative and generative models through a game-theoretical approach. Moreover, even though we used very small numbers of labeled training HSI samples from the two most challenging and extensively studied datasets, the experimental results demonstrated that spectral–spatial GAN-CRF (SS-GAN-CRF) models achieved top-ranking accuracy for semisupervised HSI classification.
The enclosure method for inverse obstacle scattering over a finite time interval: VI. Using shell-type initial data

Abstract A simple idea of finding a domain that encloses an unknown discontinuity embedded in a body is introduced by considering an inverse boundary value problem for the heat equation. The idea gives a design of a special heat flux on the surface of the body such that from the corresponding temperature field on the surface one can extract the smallest radius of the sphere centered at an arbitrary given point in the whole space and enclosing unknown inclusions. Unlike before, the designed flux is free from a large parameter. An application of the idea to a coupled system of the elastic wave and heat equations are also given.
The Strange Metal State of the Electron-Doped Cuprates

An understanding of the high-temperature copper oxide (cuprate) superconductors has eluded the physics community for over thirty years, and represents one of the greatest unsolved problems in condensed matter physics. Particularly enigmatic is the normal state from which superconductivity emerges, so much so that this phase has been dubbed a "strange metal. " In this article, we will review recent research into this strange metallic state as realized in the electron-doped cuprates with a focus on their transport properties. The electron-doped compounds differ in several ways from their more thoroughly studied hole-doped counterparts, and understanding these asymmetries of the phase diagram may prove crucial to developing a final theory of the cuprates. Most of the experimental results discussed in this review have yet to be explained and remain an outstanding challenge for theory.