Miracle-dz/UniFAR-FTUs huggingface dataset:
A systematic association of subgraph counts over a network

We associate all small subgraph counting problems with a systematic graph encoding/representation system which makes a coherent use of graphlet structures. The system can serve as a unified foundation for studying and connecting many important graph problems in theory and practice. We describe topological relations among graphlets (graph elements) in rigorous mathematics language and from the perspective of graph encoding. We uncover, characterize and utilize algebraic and numerical relations in graphlet counts/frequencies. We present a novel algorithm for efficiently counting small subgraphs as a practical product of our theoretical findings. 1 ar X iv :2 10 3. 10 83 8v 1 [ cs . D M ] 1 9 M ar 2 02 1
GLOWin: A Flow-based Invertible Generative Framework for Learning Disentangled Feature Representations in Medical Images

Disentangled representations can be useful in many downstream tasks, help to make deep learning models more interpretable, and allow for control over features of synthetically generated images that can be useful in training other models that require a large number of labelled or unlabelled data. Recently, flow-based generative models have been proposed to generate realistic images by directly modeling the data distribution with invertible functions. In this work, we propose a new flow-based generative model framework, named GLOWin, that is end-to-end invertible and able to learn disentangled representations. Feature disentanglement is achieved by factorizing the latent space into components such that each component learns the representation for one generative factor. Comprehensive experiments have been conducted to evaluate the proposed method on a public brain tumor MR dataset. Quantitative and qualitative results suggest that the proposed method is effective in disentangling the features from complex medical images.
Multi-period portfolio optimization using model predictive control with mean-variance and risk parity frameworks

We employ model predictive control for a multi-period portfolio optimization problem. In addition to the mean-variance objective, we construct a portfolio whose allocation is given by model predictive control with a riskparity objective, and provide a successive convex program algorithm that provides 30 times faster and robust solutions in the experiments. Computational results on the multi-asset universe show that multi-period models perform better than their single period counterparts in out-of-sample period, 20062020. The out-of-sample risk-adjusted performance of both mean-variance and risk-parity formulations beat the fix-mix benchmark, and achieve Sharpe ratio of 0.64 and 0.97, respectively.
High Density Direct Bond Interconnect (DBI) Technology for Three Dimensional Integrated Circuit Applications

A novel direct wafer bonding technology capable of forming a very high density of electrical interconnections across the bond interface integral to the bond process is described. Results presented include an 8 um interconnection pitch, die-to-wafer and wafer-to-wafer bonding formats, temperature cycling reliability × 10 greater than the JEDEC requirement, connection yield ∼ 99.999, > 50% part yield on parts with ∼ 450,000 connections, and
New constraints on the 12CO(2–1)/(1–0) line ratio across nearby disc galaxies

Both the CO(2-1) and CO(1-0) lines are used to trace the mass of molecular gas in galaxies. Translating the molecular gas mass estimates between studies using different lines requires a good understanding of the behaviour of the CO(2-1)-to-CO(1-0) ratio, R21. We compare new, high quality CO(1-0) data from the IRAM 30-m EMPIRE survey to the latest available CO(2-1) maps from HERACLES, PHANGS-ALMA, and a new IRAM 30-m M51 Large Program. This allows us to measure R21 across the full star-forming disc of nine nearby, massive, star-forming spiral galaxies at 27′′(∼1−2 kpc) resolution. We find an average R21 = 0.64 ± 0.09 when we take the luminosity-weighted mean of all individual galaxies. This result is consistent with the mean ratio for disc galaxies that we derive from single-pointing measurements in the literature, R21,lit = 0.59+0.18 −0.09. The ratio shows weak radial variations compared to the point-to-point scatter in the data. In six out of nine targets the central enhancement in R21 with respect to the galaxy-wide mean is of order ∼ 10−20%. We estimate an azimuthal scatter of ∼20% in R21 at fixed galactocentric radius but this measurement is limited by our comparatively coarse resolution of 1.5 kpc. We find mild correlations between R21 and CO brightness temperature, IR intensity, 70-to-160 μm ratio, and IR-to-CO ratio. All correlations indicate that R21 increases with gas surface density, star formation rate surface density, and the interstellar radiation field.
MDMMT: Multidomain Multimodal Transformer for Video Retrieval

We present a new state-of-the-art on the text-to-video re-trieval task on MSRVTT and LSMDC benchmarks where our model outperforms all previous solutions by a large margin. Moreover, state-of-the-art results are achieved using a single model and without finetuning. This multidomain generalisation is achieved by a proper combination of different video caption datasets. We show that our practical approach for training on different datasets can improve test results of each other. Additionally, we check intersection between many popular datasets and show that MSRVTT as well as ActivityNet contains a significant overlap between the test and the training parts. More details are available at https://github.com/papermsucode/mdmmt.
Inductive Inference in Supervised Classification

Inductive inference in supervised classification context constitutes to methods and approaches to assign some objects or items into different predefined classes using a formal rule that is derived from training data and possibly some additional auxiliary information. The optimality of such an assignment varies under different conditions due to intrinsic attributes of the objects being considered for such a task. One of these cases is when all the objects' features are discrete variables with a priori known categories. As another example, one can consider a modification of this case with a priori unknown categories. These two cases are the main focus of this thesis and based on Bayesian inductive theories, de Finetti type exchangeability is a suitable assumption that facilitates the derivation of classifiers in the former scenario. On the contrary, this type of exchangeability is not applicable in the latter case, instead, it is possible to utilise the partition exchangeability due to John Kingman. These two types of exchangeabilities are discussed and furthermore here I investigate inductive supervised classifiers based on both types of exchangeabilities. I further demonstrate that the classifiers based on de Finetti type exchangeability can optimally handle test items independently of each other in the presence of infinite amounts of training data while on the other hand, classifiers based on partition exchangeability still continue to benefit from joint labelling of all the test items. Additionally, it is shown that the inductive learning process for the simultaneous classifier saturates when the amount of test data tends to infinity.
Low-Complexity Dynamic Resource Scheduling for Downlink MC-NOMA Over Fading Channels

In this paper, we investigate dynamic resource scheduling (i.e., joint user, subchannel, and power scheduling) for downlink multi-channel non-orthogonal multiple access (MC-NOMA) systems over time-varying fading channels. Specifically, we address the weighted average sum rate maximization problem with quality-of-service (QoS) constraints. In particular, to facilitate fast resource scheduling, we focus on developing a very low-complexity algorithm. To this end, by leveraging Lagrangian duality and the stochastic optimization theory, we first develop an opportunistic MC-NOMA scheduling algorithm whereby the original problem is decomposed into a series of subproblems, one for each time slot. Accordingly, resource scheduling works in an online manner by solving one subproblem per time slot, making it more applicable to practical systems. Then, we further develop a heuristic joint subchannel assignment and power allocation (Joint-SAPA) algorithm with very low computational complexity, called Joint-SAPA-LCC, that solves each subproblem. Finally, through simulation, we show that our Joint-SAPA-LCC algorithm provides good performance comparable to the existing Joint-SAPA algorithms despite requiring much lower computational complexity. We also demonstrate that our opportunistic MC-NOMA scheduling algorithm in which the Joint-SAPA-LCC algorithm is embedded works well while satisfying given QoS requirements.
UniMoCo: Unsupervised, Semi-Supervised and Full-Supervised Visual Representation Learning

Momentum Contrast (MoCo) achieves great success for unsupervised visual representation. However, there are a lot of supervised and semi-supervised datasets, which are already labeled. To fully utilize the label annotations, we propose Unified Momentum Contrast (UniMoCo), which extends MoCo to support arbitrary ratios of labeled data and unlabeled data training. Compared with MoCo, UniMoCo has two modifications as follows: (1) Different from a single positive pair in MoCo, we maintain multiple positive pairs on-the-fly by comparing the query label to a label queue. (2) We propose a Unified Contrastive(UniCon) loss to support an arbitrary number of positives and negatives in a unified pair-wise optimization perspective. Our UniCon is more reasonable and powerful than the supervised contrastive loss in theory and practice. In our experiments, we pre-train multiple UniMoCo models with different ratios of ImageNet labels and evaluate the performance on various downstream tasks. Experiment results show that UniMoCo generalizes well for unsupervised, semi-supervised and supervised visual representation learning.
Domains of attraction of invariant distributions of the infinite Atlas model

The infinite Atlas model describes a countable system of competing Brownian particles where the lowest particle gets a unit upward drift and the rest evolve as standard Brownian motions. The stochastic process of gaps between the particles in the infinite Atlas model does not have a unique stationary distribution and in fact for every a≥ 0, πa := ⊗∞ i=1Exp(2 + ia) is a stationary measure for the gap process. We say that an initial distribution of gaps is in the weak domain of attraction of the stationary measure πa if the time averaged laws of the stochastic process of the gaps, when initialized using that distribution, converge to πa weakly in the large time limit. We provide general sufficient conditions on the initial gap distribution of the Atlas particles for it to lie in the weak domain of attraction of πa for each a≥ 0. The cases a= 0 and a > 0 are qualitatively different as is seen from the analysis and the sufficient conditions that we provide. Proofs are based on the analysis of synchronous couplings, namely, couplings of the ranked particle systems started from different initial configurations, but driven using the same set of Brownian motions.
IA Planner: Motion Planning Using Instantaneous Analysis for Autonomous Vehicle in the Dense Dynamic Scenarios on Highways

In dense and dynamic scenarios, planning a safe and comfortable trajectory is full of challenges when traffic participants are driving at high speed. The classic graph search and sampling methods first perform path planning and then configure the corresponding speed, which lacks a strategy to deal with the high-speed obstacles. Decoupling optimization methods perform motion planning in the S-L and S-T domains respectively. These methods require a large free configuration space to plan the lane change trajectory. In dense dynamic scenes, it is easy to cause the failure of trajectory planning and be cut in by others, causing slow driving speed and bring safety hazards. We analyze the collision relationship in the spatio-temporal domain, and propose an instantaneous analysis model which only analyzes the collision relationship at the same time. In the model, the collision-free constraints in 3D spatio-temporal domain is projected to the 2D space domain to remove redundant constraints and reduce computational complexity. Experimental results show that our method can plan a safe and comfortable lane-changing trajectory in dense dynamic scenarios. At the same time, it improves traffic efficiency and increases ride comfort.
LSDAT: Low-Rank and Sparse Decomposition for Decision-based Adversarial Attack

We propose LSDAT, an image-agnostic decision-based black-box attack that exploits low-rank and sparse decomposition (LSD) to dramatically reduce the number of queries and achieve superior fooling rates compared to the state-of-the-art decision-based methods under given imperceptibility constraints. LSDAT crafts perturbations in the low-dimensional subspace formed by the sparse component of the input sample and that of an adversarial sample to obtain query-efficiency. The specific perturbation of interest is obtained by traversing the path between the input and adversarial sparse components. It is set forth that the proposed sparse perturbation is the most aligned sparse perturbation with the shortest path from the input sample to the decision boundary for some initial adversarial sample (the best sparse approximation of shortest path, likely to fool the model). Theoretical analyses are provided to justify the functionality of LSDAT. Unlike other dimensionality reduction based techniques aimed at improving query efficiency (e.g, ones based on FFT), LSD works directly in the image pixel domain to guarantee that non-$\ell_2$ constraints, such as sparsity, are satisfied. LSD offers better control over the number of queries and provides computational efficiency as it performs sparse decomposition of the input and adversarial images only once to generate all queries. We demonstrate $\ell_0$, $\ell_2$ and $\ell_\infty$ bounded attacks with LSDAT to evince its efficiency compared to baseline decision-based attacks in diverse low-query budget scenarios as outlined in the experiments.
The Shape of Learning Curves: A Review

Learning curves provide insight into the dependence of a learner's generalization performance on the training set size. This important tool can be used for model selection, to predict the effect of more training data, and to reduce the computational complexity of model training and hyperparameter tuning. This review recounts the origins of the term, provides a formal definition of the learning curve, and briefly covers basics such as its estimation. Our main contribution is a comprehensive overview of the literature regarding the shape of learning curves. We discuss empirical and theoretical evidence that supports well-behaved curves that often have the shape of a power law or an exponential. We consider the learning curves of Gaussian processes, the complex shapes they can display, and the factors influencing them. We draw specific attention to examples of learning curves that are ill-behaved, showing worse learning performance with more training data. To wrap up, we point out various open problems that warrant deeper empirical and theoretical investigation. All in all, our review underscores that learning curves are surprisingly diverse and no universal model can be identified.
XProtoNet: Diagnosis in Chest Radiography with Global and Local Explanations

Automated diagnosis using deep neural networks in chest radiography can help radiologists detect life-threatening diseases. However, existing methods only provide predictions without accurate explanations, undermining the trustworthiness of the diagnostic methods. Here, we present XProtoNet, a globally and locally interpretable diagnosis framework for chest radiography. XProtoNet learns representative patterns of each disease from X-ray images, which are prototypes, and makes a diagnosis on a given X-ray image based on the patterns. It predicts the area where a sign of the disease is likely to appear and compares the features in the predicted area with the prototypes. It can provide a global explanation, the prototype, and a local explanation, how the prototype contributes to the prediction of a single image. Despite the constraint for interpretability, XProtoNet achieves state-of-the-art classification performance on the public NIH chest X-ray dataset.
CoordiNet: uncertainty-aware pose regressor for reliable vehicle localization

In this paper, we investigate visual-based camera re-localization with neural networks for robotics and autonomous vehicles applications. Our solution is a CNN-based algorithm which predicts camera pose (3D translation and 3D rotation) directly from a single image. It also provides an uncertainty estimate of the pose. Pose and uncertainty are learned together with a single loss function and are fused at test time with an EKF. Furthermore, we propose a new fully convolutional architecture, named CoordiNet, designed to embed some of the scene geometry.Our framework outperforms comparable methods on the largest available benchmark, the Oxford RobotCar dataset, with an average error of 8 meters where previous best was 19 meters. We have also investigated the performance of our method on large scenes for real time (18 fps) vehicle localization. In this setup, structure-based methods require a large database, and we show that our proposal is a reliable alternative, achieving 29cm median error in a 1.9km loop in a busy urban area.
Air-stable, high-conduction solid electrolytes of arsenic-substituted Li4SnS4

Lithium-ion-conducting solid electrolytes show promise for enabling high-energy secondary battery chemistries and solving safety issues associated with conventional lithium batteries. Achieving the combination of high ionic conductivity and outstanding chemical stability in solid electrolytes is a grand challenge for the synthesis of solid electrolytes. Herein we report the design of aliovalent substitution of Li4SnS4 to achieve high conduction and excellent air stability based on the hard and soft acids and bases theory. The solid electrolyte of composition Li3.833Sn0. 833As0.166S4 has a high ionic conductivity of 1.39 mS cm−1 at 25 °C. Considering the high Li+ transference number, this phase conducts Li+ as well as carbonate-based liquid electrolytes. This research also addresses the compatibility of the sulfide-based solid electrolytes through chemical passivation.
Methanol extract of Peltophorum Pterocarpum stem bark modulates Plasmodium berghei ANKA 65-Induced hypoglycemia and lipid dysfunction in Mice

ABSTRACT This study evaluated the effects of methanol extract of Peltophorum pterocarpum stem bark (MEPTS) on blood glucose level and lipid profile of Plasmodium berghei ANKA 65-parasitized mice. The levels of total cholesterol (TC), low- and high-density lipoproteins (LDL and HDL, respectively), triacylglycerols (TAG) and blood glucose in parasitized mice treated with MEPTS (100, 200 or 400 mg kg−1 b.w. d−1, p.o) and artemether/lumenfantrin (28 mg kg−1 b.w. d−1, p.o) were determined and compared with malaria control. Alkaloids, saponins, flavonoids, tannins, glycosides, steroids, carotenoids, anthocyanins, anthraquinones, terpenoids, and phenols were detected in the extract. On days 1, 2, and 3, there were progressive increase in malaria parasitemia in malaria control compared with parasitized-MEPTS-administered mice that had dose-dependent decrease in malaria parasitemia. Malaria control group showed hyperlipidemia and hypoglycemia compared to normal mice, which was ameliorated by the administration of MEPTS, and artemether/lumenfantrin. These findings suggest that MEPTS may have potent antimalarial activity and modulated lipid dysfunction and hypoglycemia generated by malaria.
The Impact of Working Capital Management on Financial Performance of Cost Leadership and Differentiation Strategy Firms in Different Business Cycles: Evidence from New Zealand

ABSTRACT We examine the impact of managing working capital on financial performance in New Zealand over the period of 2005 to 2017. Our empirical analysis provides evidence of a nonlinear relationship between working-capital management and financial performance. In addition, our finding seems to suggest that a cost leadership strategy firm is more efficient at managing working capital than a differentiation strategy firm. Specifically, our findings imply that managers of the firms with a cost-leadership strategy created more value by reducing net-trade cycle by 40 days. Our research has implications for managing trade receivables and inventories given their importance for firms’ survival.
Housing, Relationships, and Substance Use among Formerly Incarcerated Females

ABSTRACT The need for housing is an important concern for most women about to be released from prison, especially by those that need substance use treatment. This study explored the association between housing settings, relationships, and substance use. Two hundred adult females that had endorsed previous incarceration within the past 2 years participated in the initial baseline study. This study used multi-level modeling to analyze if housing settings and/or relationships within a living condition are associated with substance use. Results indicated that participants in homeless, and mutual settings used substances significantly more than participants in their own house or apartment. Participants that were in controlled, residential, and transitional settings used significantly less than those living in their own house or apartment. Living with parents, family, or sexual partner significantly increased substance use. Utilizing these results to plan housing arrangements before women are released from correctional facilities may be beneficial for substance use recovery. Currently, there is no literature that directly measures the relationship between distinct housing settings, as well as the relationships that exist within them and substance use.
Dividend Behavior of Indian Firms: New Evidence from Large Data Set

ABSTRACT This paper investigates the dividend behavior of 5,785 non-financial non-utility non-government listed Indian firms during 1990–2016 using trend and econometric analysis. It is found that the dividend distribution tax is suppressing dividends whereas improved corporate governance norms are increasing dividends. The evidence suggests that cash dividends are preferred more than share-repurchases and firms either increase or maintain stable dividends. Further, a concentration of dividends by the Indian firms is observed. The firms that are profitable, large and have lesser investment opportunities exhibit a higher propensity to pay dividends. Logit regression estimates suggest an appearing, disappearing, and reappearing trend of dividends.
Panasonic in China: A Search to Overcome the Liability of Foreignness

ABSTRACT This paper analyzes the evolutionary trajectory of the Panasonic Corporation in China during the past 40 years from a liability of foreignness (LOF) perspective. Conceptually, the LOF is interpreted as dynamic processes that occur on entry and subsequently as a result of interactions in corporate strategy, host country policies and local rivalries. For Panasonic in China these interactions are highlighted through three distinct phases, involving LOF on entry and in two subsequent post-entry periods. This evolution has seen Panasonic becoming increasingly embedded in China in terms of decision-making, innovation, marketing and supply networks.