Miracle-dz/UniFAR-FTUs huggingface dataset:
AlignSeg: Feature-Aligned Segmentation Networks

Aggregating features in terms of different convolutional blocks or contextual embeddings has been proven to be an effective way to strengthen feature representations for semantic segmentation. However, most of the current popular network architectures tend to ignore the misalignment issues during the feature aggregation process caused by step-by-step downsampling operations and indiscriminate contextual information fusion. In this paper, we explore the principles in addressing such feature misalignment issues and inventively propose Feature-Aligned Segmentation Networks (AlignSeg). AlignSeg consists of two primary modules, i.e., the Aligned Feature Aggregation (AlignFA) module and the Aligned Context Modeling (AlignCM) module. First, AlignFA adopts a simple learnable interpolation strategy to learn transformation offsets of pixels, which can effectively relieve the feature misalignment issue caused by multi-resolution feature aggregation. Second, with the contextual embeddings in hand, AlignCM enables each pixel to choose private custom contextual information adaptively, making the contextual embeddings be better aligned. We validate the effectiveness of our AlignSeg network with extensive experiments on Cityscapes and ADE20K, achieving new state-of-the-art mIoU scores of 82.6 and 45.95 percent, respectively. Our source code is available at https://github.com/speedinghzl/AlignSeg.
Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs

Batch normalization (BatchNorm) has become an indispensable tool for training deep neural networks, yet it is still poorly understood. Although previous work has typically focused on its normalization component, BatchNorm also adds two per-feature trainable parameters - a coefficient and a bias - whose role and expressive power remain unclear. To study this question, we investigate the performance achieved when training only these parameters and freezing all others at their random initializations. We find that doing so leads to surprisingly high performance. For example, sufficiently deep ResNets reach 82% (CIFAR-10) and 32% (ImageNet, top-5) accuracy in this configuration, far higher than when training an equivalent number of randomly chosen parameters from elsewhere in the network. BatchNorm achieves this performance in part by naturally learning to disable around a third of the random features. Not only do these results highlight the under-appreciated role of the affine parameters in BatchNorm, but - in a broader sense - they characterize the expressive power of neural networks constructed simply by shifting and rescaling random features.
Technological Interdependencies Predict Innovation Dynamics

We propose a simple model where the innovation rate of a technological domain depends on the innovation rate of the technological domains it relies on. Using data on US patents from 1836 to 2017, we make out-of-sample predictions and find that the predictability of innovation rates can be boosted substantially when network effects are taken into account. In the case where a technology$'$s neighborhood future innovation rates are known, the average predictability gain is 28$\%$ compared to simpler time series model which do not incorporate network effects. Even when nothing is known about the future, we find positive average predictability gains of 20$\%$. The results have important policy implications, suggesting that the effective support of a given technology must take into account the technological ecosystem surrounding the targeted technology.
Interacting galaxies in the IllustrisTNG simulations - I: Triggered star formation in a cosmological context

We use the IllustrisTNG cosmological hydrodynamical simulations to investigate how the specific star formation rates (sSFRs) of massive galaxies (M* > 1010 M⊙) depend on the distance to their closest companions. We estimate sSFR enhancements by comparing with control samples that are matched in redshift, stellar mass, local density, and isolation, and we restrict our analysis to pairs with stellar mass ratios of 0.1 to 10. At small separations (∼15 kpc), the mean sSFR is enhanced by a factor of 2.0 ± 0.1 in the flagship (110.7 Mpc)3 simulation (TNG100-1). Statistically significant enhancements extend out to 3D separations of 280 kpc in the (302.6 Mpc)3 simulation (TNG300-1). We find similar trends in the EAGLE and Illustris simulations, although their sSFR enhancements are lower than those in TNG100-1 by about a factor of two. Enhancements in IllustrisTNG galaxies are seen throughout the redshift range explored (0 ≤ $z$ < 1), with the strength of the enhancements decreasing with increasing redshift for galaxies with close companions. In order to more closely compare with observational results, we separately consider 2D projected distances between galaxies in IllustrisTNG. We detect significant sSFR enhancements out to projected separations of 260 kpc in TNG300-1, with projection effects diluting the size of the enhancements by about 20 per cent below 50 kpc. We find similar sSFR enhancements in TNG100-1 and Sloan Digital Sky Survey galaxies, with enhancements extending out to projected separations of about 150 kpc for star-forming galaxies at $z$ < 0.2. Finally, by summing over all separations, we estimate that the presence of closest companions boosts the average sSFR of massive galaxies in TNG100-1 by 14.5 per cent.
Library Adoption Dynamics in Software Teams

When a group of people strives to understand new information, struggle ensues as various ideas compete for attention. Steep learning curves are surmounted as teams learn together. To understand how these team dynamics play out in software development, we explore Git logs, which provide a complete change history of software repositories. In these repositories, we observe code additions, which represent successfully implemented ideas, and code deletions, which represent ideas that have failed or been superseded. By examining the patterns between these commit types, we can begin to understand how teams adopt new information. We specifically study what happens after a software library is adopted by a project, i.e. when a library is used for the first time in the project. We find that a variety of factors, including team size, library popularity, and prevalence on Stack Overflow are associated with how quickly teams learn and successfully adopt new software libraries.
Distributed Momentum for Byzantine-resilient Learning

Momentum is a variant of gradient descent that has been proposed for its benefits on convergence. In a distributed setting, momentum can be implemented either at the server or the worker side. When the aggregation rule used by the server is linear, commutativity with addition makes both deployments equivalent. Robustness and privacy are however among motivations to abandon linear aggregation rules. In this work, we demonstrate the benefits on robustness of using momentum at the worker side. We first prove that computing momentum at the workers reduces the variance-norm ratio of the gradient estimation at the server, strengthening Byzantine resilient aggregation rules. We then provide an extensive experimental demonstration of the robustness effect of worker-side momentum on distributed SGD.
Revisiting Convolutional Neural Networks for Urban Flow Analytics

Convolutional Neural Networks (CNNs) have been widely adopted in raster-based urban flow analytics by virtue of their capability in capturing nearby spatial context. By revisiting CNN-based methods for different analytics tasks, we expose two common critical drawbacks in the existing uses: 1) inefficiency in learning global context, and 2) overlooking latent region functions. To tackle these challenges, in this paper we present a novel framework entitled DeepLGR that can be easily generalized to address various urban flow analytics problems. This framework consists of three major parts: 1) a local context module to learn local representations of each region; 2) a global context module to extract global contextual priors and upsample them to generate the global features; and 3) a region-specific predictor based on tensor decomposition to provide customized predictions for each region, which is very parameter-efficient compared to previous methods. Extensive experiments on two typical urban analytics tasks demonstrate the effectiveness, stability, and generality of our framework.
Counterfactual Mean Embeddings

Counterfactual inference has become a ubiquitous tool in online advertisement, recommendation systems, medical diagnosis, and finance. Accurate modeling of outcome distributions associated with different interventions---known as counterfactual distributions---is crucial for the success of these applications. In this work, we propose to model counterfactual distributions using a novel Hilbert space representation called counterfactual mean embedding (CME). The CME embeds the associated counterfactual distribution into a reproducing kernel Hilbert space (RKHS) endowed with a positive definite kernel, which allows us to perform causal inference over the entire landscape of the counterfactual distribution. Based on this representation, we propose a distributional treatment effect (DTE) which can quantify the causal effect over entire outcome distributions. Our approach is nonparametric as the CME can be estimated consistently from observational data without requiring any parametric assumption about the underlying distributions. We also establish a rate of convergence of the proposed estimator which depends on the smoothness of the conditional mean and the Radon-Nikodym derivative of the underlying marginal distributions. Furthermore, our framework also allows for more complex outcomes such as images, sequences, and graphs. Lastly, our experimental results on synthetic data and off-policy evaluation tasks demonstrate the advantages of the proposed estimator.
Synth . segmentation Real segmentation Synth . GT Synth . RGB Real RGB Fully Convolutional Discriminator synthetic path real path Region Growing

Deep learning techniques have been widely used in autonomous driving systems for the semantic understanding of urban scenes, however they need a huge amount of labeled data for training, which is difficult and expensive to acquire. A recently proposed workaround is to train deep networks using synthetic data, however the domain shift between real world and synthetic representations limits the performance. In this work a novel unsupervised domain adaptation strategy is introduced to solve this issue. The proposed learning strategy is driven by three components: a standard supervised learning loss on labeled synthetic data, an adversarial learning module that exploits both labeled synthetic data and unlabeled real data and finally a selfteaching strategy exploiting unlabeled data. The last component exploits a region growing framework guided by the segmentation confidence. Furthermore, we weighted this component on the basis of the class frequencies to enhance the performance on less common classes. Experimental results prove the effectiveness of the proposed strategy in adapting a segmentation network trained on synthetic datasets, like GTA5 and SYNTHIA, to real world datasets like Cityscapes and Mapillary.
To determine the pattern and severity of cardiac valves involvement in children with rheumatic heart disease

Background: Rheumatic heart disease (RHD) is the most common cardiovascular disease in children and young adults and is a major public health problem in developing world. Objectives: To determine the relative frequency and severity of the cardiac valve lesions in children, diagnosed as RHD. M aterials and Methods: This prospective observational study was conducted from January 2013 to December 2014 in the Department of Pediatrics and Cardiology of teaching institution. Children less than 15 years of age fulfilling the selection criteria of RHD were evaluated by transthoracic echocardiography for pattern and degree of valvular involvement, according to the American Heart Association/American College of Cardiology guidelines. Results: A total of 45 patients (23 males and 22 females) were studied. The most common age group was 10-12 years, 91.11% cases belonged to lower socio-economic class. The most common lesion was mitral regurgitation (MR) (91.11%) with female predominance (51.22%) followed by atrial regurgitation (AR) (51.11%) with female (56.53%) predominance, mitral stenosis (MS) (26.66%) with male (58.33%) predominance, and tricuspid regurgitation (24.44%) with female (63.64%) predominance. Out of 45 cases of RHD, 29 cases (64.44%) had combined valvular lesions, out of which 14 cases (48.28%) had both MR and AR. Conclusion: RHD is still a common problem in our country. In our study, the most of the patients had combined valvular lesions. The predominant lesion was MR followed by aortic regurgitation and MS. Regurgitant lesions were seen predominantly in females while stenotic lesions were seen in males. Echocardiography should be done routinely for patients with RHD, focusing on younger population, to facilitate diagnosis and definitive treatment before complications set in. Key words: Aortic regurgitation, Aortic stenosis, Echocardiography, Mitral regurgitation, Mitral stenosis, Rheumatic heart disease
Do Local and International Venture Capitalists Play Well Together? The Complementarity of Local and International Venture Capitalists

We find that entrepreneurial firms in emerging nations backed by syndicates composed of international and local venture capitalists have more successful exits and higher post-IPO operating performance than those backed by syndicates of purely international or purely local venture capitalists. We control for the potential endogenous participation and syndication by international VCs using instrumental variables analyses and a natural experiment and find a causal effect of international VC participation on successful outcomes. International VCs face disadvantages in their investments due to the lack of proximity to the entrepreneurial firm. Using air service agreements between countries as an exogenous change in effective proximity, we find that entrepreneurial firms backed by international VCs are more successful when travel becomes easier between the two countries. Overall, our results indicate that the greater venture capital expertise of international venture capitalists and the superior local knowledge and lower monitoring costs of local venture capitalists are both important in obtaining successful investment outcomes.
Algorithm For The Control Of Receivables In The Declaration Of Foreign Trade Goods In Real Time

The task of modeling the process of customs clearance of goods of foreign trade is considered. One of the priority tasks of the customs authorities is the automation of the process of controlling receivables of foreign trade participants in conditions of remote declaration of goods. In the course of making decisions on the customs clearance of goods, there is a problem associated with determining the monitoring criteria for the receivables of foreign trade participants. The proposed method for calculating receivables allows you to develop an algorithm for controlling it when declaring foreign trade goods in real time.
Interference-Aware Trajectory Design for Ground-Aerial Uplink NOMA Cellular Networks

This paper investigates ground-aerial uplink non- orthogonal multiple access (NOMA) cellular networks. A unmanned aerial vehicle (UAV) user and ground users (GUEs) are served by ground base stations (GBSs) by utilizing uplink NOMA protocol. The goal is to minimize the UAV mission completion time by jointly designing the UAV trajectory and UAV-GBS association vectors while considering the interference of UAV to other non-associated GBSs. The formulated problem is a mixed integer non-convex problem and involves infinite number of variables, which is difficult to be directly solved. To tackle this challenge, we first prove the optimal UAV trajectory satisfies \emph{fly-hover-fly} communication policy. With this insight, we propose an efficient algorithm to solve the original problem based on a properly constructed graph by invoking graph theory and convex optimization techniques. Numerical results show that the UAV mission completion time is significantly minimized with proposed NOMA scheme compared with conventional orthogonal multiple access (OMA) communication and reveal a tradeoff between the UAV mission completion time and GUEs' quality-of-service (QoS) requirements.
Analytical Performance Analysis of CdZnO/ZnO-Based Multiple Quantum Well Solar Cell

This article presents analytical and simulation evaluation of multiple quantum well solar cells (MQWSC) with CdZnO/ZnO as the intrinsic layer, Sb-doped ZnO (SZO) as a p-type layer, and Ga-doped ZnO (GZO) as an n-type layer of the p-i-n solar cell (SC). The material parameters used in this article are obtained from the experimental reports on the properties of ZnO and CdZnO thin films grown by dual-ion-beam sputtering (DIBS). The American Society for Testing and Materials (ASTM) standards data sheets have been utilized for attaining photon flux density instead of the blackbody radiation formula. The analytically obtained results show good agreement with the simulated results obtained by the ATLAS simulation tool. The variation of device performance parameters is examined for thermal stability. The results show that, for the proposed ZnO-based MQWSC, the open-circuit voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{{\text {oc}}}$ </tex-math></inline-formula>) has a negative temperature coefficient (−2.63 mV/°C), and short-circuit current density (<inline-formula> <tex-math notation="LaTeX">${J}_{{\text {sc}}}$ </tex-math></inline-formula>) and conversion efficiency (<inline-formula> <tex-math notation="LaTeX">${\eta }$ </tex-math></inline-formula>) have positive temperature coefficients of <inline-formula> <tex-math notation="LaTeX">$2.43\times 10^{{-{3}}} \,\,{\text {mA}/\text {cm}^{{{2}}}\cdot ^{\circ }}\text{C}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$2.91\times 10^{{-{3}}}$ </tex-math></inline-formula> %/°C, respectively. Further, the device performance has been explored for variation in the number of quantum wells. The results present that an increase in the number of quantum wells has a negative impact on the performance parameters of ZnO-based MQWSC.
Tracking Targets with Known Spatial Extent Using Experimental Marine Radar Data

Endeavours to have a reliable and robust maritime traffic situation assessment are today leading to exploit the improved sensor resolution technology of radars. In such concerned tracking scenarios, multiple noisy scattered measurements arise from targets of interest's surfaces at each observation step. In this paper, we present a special version of the Multiplicative Error Model-Extended Kalman Filter* (MEM-EKF*) approach to recursively track the orientation and kinematics of targets having known physical dimensions from a maritime- and radar-based dataset. Altogether, we also introduce and thereby propose an open dataset part of a marine radar benchmark dedicated to extended target tracking to the community. The benefits and performance of the proposed approach adopted on our dataset are discussed taking into consideration the non-ideal but nonetheless prevailing effects of the quotidian marine radar such as beam spreading and false negatives. As compared to its original version, the proposed approach proved to be more computationally efficient.
Feasibility Study on Evaluating Degree of Polymerization of Insulating Paper by Ethanol Content Dissolved in Transformer Oil

Due to the shortcomings of electrical properties and furfural in characterizing the aging characteristics of paper insulation and the limitations of field application, this paper focuses on the feasibility of evaluating the polymerization degree of insulating paper by the ethanol (EtOH) content in transformer oil. The accelerated thermal aging test is conducted, during which ethanol is produced by degradation of insulating paper. For each oil-paper samples, with different ethanol concentrations are prepared by controlling the aging time. In this paper, a gas chromatograph equipped with a mass spectrometer (GC-MS) measurement method with headspace injection is applied to obtain a fitting curve between ethanol content in oil and average degree of polymerization (DPv), which is a method for judging the degree of paper insulation aging condition. Then, showing the influence of temperature on ethanol generated in this experiment. At last, it is indicated that it is feasible to evaluate the polymerization degree of insulating paper by using the ethanol content in transformer oil.
Realtime Compensation of Percieved Sound Source Direction for Bone-Conduction Headphones

Sounds perceived through bone-conduction stereo headphones incline toward the frontal center, comparing to the perceived direction through air-conduction stereo headphones. We previously developed a compensation method to address the above issue. This method compensates for the above difference in the form of streaming process. For this study, we implemented this method as a computer program with MATLAB and conducted a hearing experiment to evaluate its performance. It performed well in real time on a consumer-grade PC, and the shift in perceived sound direction was compensated. This is useful for the headphone amplifiers of bone-conduction headphones.
Partition Based Strategic Node Placement and Efficient Communication Method for WSN

Today, the Wireless Sensor Network (WSN) is having a very vast area in the various field of research and development. The main focus of WSN is to intensify the existence of WSN by decreasing the energy depletion. The lifetime of WSN is depended on many factors like energy depletion, transmission range, communication range and sensing range. But mainly, the lifetime of WSN is highly dependent on energy utilization. So we have to reduce the wastage of energy of sensor nodes. There is a way that can save the energy of nodes by placing them in an elegant manner. In this paper, we propose an Advanced Specific Node Formation (ASNF) strategy and communication method for WSNs. This strategy is performed in four rounds. In the first round, the sensor nodes are positioned in the circular pattern in inner region. In the second round, we positioned the nodes in hexagonal and pentagonal patterns called hexa circular node formation and penta circular node formation respectvely in outer region. The partition of whole area in distinct pattern is performed in third round. After that, the selection of CHs in respective areas takes place in fourth round. The selection of a Cluster Heads (CHs) can be based on many parameters like residual energy and the distance from the nodes etc. Particularly, under this strategy, we select the CHs with the premises of optimum energy. In this way, the whole area will be covered with least nodes. With these nodes, we can aggregate the information. So, with the help of this strategy, we can assess higher proficiency for network lifetime and energy depletion. The simulation results shows higher proficiency of energy depletion, dead node and delay etc.