Miracle-dz/UniFAR-FTUs huggingface dataset:
Lifted Disjoint Paths with Application in Multiple Object Tracking

We present an extension to the disjoint paths problem in which additional \emph{lifted} edges are introduced to provide path connectivity priors. We call the resulting optimization problem the lifted disjoint paths problem. We show that this problem is NP-hard by reduction from integer multicommodity flow and 3-SAT. To enable practical global optimization, we propose several classes of linear inequalities that produce a high-quality LP-relaxation. Additionally, we propose efficient cutting plane algorithms for separating the proposed linear inequalities. The lifted disjoint path problem is a natural model for multiple object tracking and allows an elegant mathematical formulation for long range temporal interactions. Lifted edges help to prevent id switches and to re-identify persons. Our lifted disjoint paths tracker achieves nearly optimal assignments with respect to input detections. As a consequence, it leads on all three main benchmarks of the MOT challenge, improving significantly over state-of-the-art.
Disparate patterns of movements and visits to points of interest located in urban hotspots across US metropolitan cities during COVID-19

We examined the effect of social distancing on changes in visits to urban hotspot points of interest. In a pandemic situation, urban hotspots could be potential superspreader areas as visits to urban hotspots can increase the risk of contact and transmission of a disease among a population. We mapped census-block-group to point-of-interest (POI) movement networks in 16 cities in the United States. We adopted a modified coarse-grain approach to examine patterns of visits to POIs among hotspots and non-hotspots from January to May 2020. Also, we conducted chi-square tests to identify POIs with significant flux-in changes during the analysis period. The results showed disparate patterns across cities in terms of reduction in hotspot POI visitors. Sixteen cities were divided into two categories using a time series clustering method. In one category, which includes the cities of San Francisco, Seattle and Chicago, we observed a considerable decrease in hotspot POI visitors, while in another category, including the cities of Austin, Houston and San Diego, the visitors to hotspots did not greatly decrease. While all the cities exhibited overall decreased visitors to POIs, one category maintained the proportion of visitors to hotspot POIs. The proportion of visitors to some POIs (e.g. restaurants) remained stable during the social distancing period, while some POIs had an increased proportion of visitors (e.g. grocery stores). We also identified POIs with significant flux-in changes, indicating that related businesses were greatly affected by social distancing. The study was limited to 16 metropolitan cities in the United States. The proposed methodology could be applied to digital trace data in other cities and countries to study the patterns of movements to POIs during the COVID-19 pandemic.
THE EVOLUTION OF DOMINANCE: A THEORY WHOSE TIME HAS PASSED?

The evolution of dominance by the selection of modifiers of the phenotypes of deleterious mutations was proposed as a hypothesis by R. A. Fisher in 1928. It has been strongly criticized ever since, is regarded by many as having been made irrelevant by metabolic control theory, and most recently has been claimed to have been‘falsified’by H. A. Orr. Is it indeed not only obsolete but wrong? Its history is reviewed and its present status evaluated. We conclude (1) that it has a role as the explanation of the dominance found in many cases of selection through visual predation and (2) that the selection mechanism long claimed to be ineffective (the increase in frequency of a single modifier) will be effective under certain special conditions that may be different from those Fisher proposed.
BACKDOOR ATTACKS ON FACIAL RECONGITION IN THE PHYSICAL WORLD

Backdoor attacks embed hidden malicious behaviors inside deep neural networks (DNNs) that are only activated when a speciﬁc “trigger” is present in some input to the model. A variety of these attacks have been successfully proposed and evaluated, generally using digitally generated patterns or images as triggers. Despite signiﬁcant prior work on the topic, a key question remains unanswered: “can backdoor attacks be physically realized in the real world, and what limitations do attackers face in executing them? ” In this paper, we present results of a detailed study on DNN backdoor attacks in the physical world, speciﬁcally focused on the task of facial recognition. We take 3,205 photographs of 10 volunteers in a variety of settings and backgrounds, and train a facial recognition model using transfer learning from VGGFace. We evaluate the effectiveness of 9 accessories as potential triggers, and analyze impact from external factors such as lighting and image quality. First, we ﬁnd that triggers vary signiﬁcantly in efﬁcacy, and a key factor is that facial recognition models are heavily tuned to features on the face and less so to features around the periphery. Second, the efﬁcacy of most trigger objects is negatively impacted by lower image quality but unaffected by lighting. Third, most triggers suffer from false positives, where non-trigger objects unintentionally activate the back-door. Finally, we evaluate 4 backdoor defenses against physical backdoors. We show that they all perform poorly because physical triggers break key assumptions they made based on triggers in the digital domain. Our key takeaway is that implementing physical backdoors is much more challenging than described in literature for both attackers and defenders, and much more work is necessary to understand how backdoors work in the real world.
Prior-guided Bayesian Optimization

While Bayesian Optimization (BO) is a very popular method for optimizing expensive black-box functions, it fails to leverage the experience of domain experts. This causes BO to waste function evaluations on commonly known bad regions of design choices, e.g., hyperparameters of a machine learning algorithm. To address this issue, we introduce Prior-guided Bayesian Optimization (PrBO). PrBO allows users to inject their knowledge into the optimization process in the form of priors about which parts of the input space will yield the best performance, rather than BO's standard priors over functions which are much less intuitive for users. PrBO then combines these priors with BO's standard probabilistic model to yield a posterior. We show that PrBO is more sample efficient than state-of-the-art methods without user priors and 10,000$\times$ faster than random search, on a common suite of benchmarks and a real-world hardware design application. We also show that PrBO converges faster even if the user priors are not entirely accurate and that it robustly recovers from misleading priors.
Contemporary niche contraction affects climate change predictions for elephants and giraffes

Climate change assessments are largely based on correlative species distribution models (SDMs) that are sensible to spatial biases or incompleteness of input distribution data. We tested whether changes on the species' climatic niche resulting from recent human‐induced range contractions have a significant influence on SDM predictions of future species distributions.
Predictions of Gaia’s prize microlensing events are flawed

Precision astrometry from the second Gaia data release has allowed astronomers to predict 5,787 microlensing events, with 528 of these having maximums within the extended Gaia mission (J2014.5 - J2026.5). Future analysis of the Gaia time-series astrometry of these events will, in some cases, lead to precise gravitational mass measurements of the lens. We find that 61% of events predicted during the extended Gaia mission with sources brighter than G = 18 are likely to be spurious, with the background source in these cases commonly being either a duplicate detection or a binary companion of the lens. We present quality cuts to identify these spurious events and a revised list of microlensing event candidates. Our findings imply that half of the predictable astrometric microlensing events during the Gaia mission have yet to be identified.
STORM: Foundations of End-to-End Empirical Risk Minimization on the Edge

Empirical risk minimization is perhaps the most influential idea in statistical learning, with applications to nearly all scientific and technical domains in the form of regression and classification models. To analyze massive streaming datasets in distributed computing environments, practitioners increasingly prefer to deploy regression models on edge rather than in the cloud. By keeping data on edge devices, we minimize the energy, communication, and data security risk associated with the model. Although it is equally advantageous to train models at the edge, a common assumption is that the model was originally trained in the cloud, since training typically requires substantial computation and memory. To this end, we propose STORM, an online sketch for empirical risk minimization. STORM compresses a data stream into a tiny array of integer counters. This sketch is sufficient to estimate a variety of surrogate losses over the original dataset. We provide rigorous theoretical analysis and show that STORM can estimate a carefully chosen surrogate loss for the least-squares objective. In an exhaustive experimental comparison for linear regression models on real-world datasets, we find that STORM allows accurate regression models to be trained.
Dynamics of reversed shear Alfvén eigenmode and energetic particles during current ramp-up

Hybrid MHD-gyrokinetic code simulations are used to investigate the dynamics of frequency sweeping reversed shear Alfvén eigenmode (RSAE) strongly driven by energetic particles (EPs) during plasma current ramp-up in a conventional tokamak configuration. A series of weakly reversed shear equilibria representing time slices of long timescale MHD equilibrium evolution is considered, where the self-consistent RSAE-EP resonant interactions on the short timescale are analyzed in detail. Both linear and non-linear RSAE dynamics are shown to be subject to the non-perturbative effect of EPs by maximizing wave-EP power transfer. In the linear stage, EPs induce evident mode structure and frequency shifts; meanwhile, RSAE saturates by radial decoupling with resonant EPs due to weak magnetic shear, and gives rise to global EP convective transport and fast frequency chirping in the non-adiabatic regime. The spatiotemporal scales of phase space wave-EP interactions are characterized by the perpendicular wavelength and wave-particle trapping time. The simulations provide insights into general as well as specific features of the RSAE spectra and EP transport in experimental observations, and illustrate the fundamental physics of wave-EP resonant interaction with the interplay of the magnetic geometry, plasma non-uniformity and non-perturbative EPs. Possible application for understanding the non-adiabatic frequency chirping as convective and relaxation branches is also discussed.
Incremental Training of Graph Neural Networks on Temporal Graphs under Distribution Shift

Current graph neural networks (GNNs) are promising, especially when the entire graph is known for training. However, it is not yet clear how to efficiently train GNNs on temporal graphs, where new vertices, edges, and even classes appear over time. We face two challenges: First, shifts in the label distribution (including the appearance of new labels), which require adapting the model. Second, the growth of the graph, which makes it, at some point, infeasible to train over all vertices and edges. We address these issues by applying a sliding window technique, i.e., we incrementally train GNNs on limited window sizes and analyze their performance. For our experiments, we have compiled three new temporal graph datasets based on scientific publications and evaluate isotropic and anisotropic GNN architectures. Our results show that both GNN types provide good results even for a window size of just 1 time step. With window sizes of 3 to 4 time steps, GNNs achieve at least 95% accuracy compared to using the entire timeline of the graph. With window sizes of 6 or 8, at least 99% accuracy could be retained. These discoveries have direct consequences for training GNNs over temporal graphs. We provide the code (this https URL) and the newly compiled datasets (this https URL) for reproducibility and reuse.
Tangles: From Weak to Strong Clustering

We introduce a new approach to clustering by using tangles, a tool that originates in mathematical graph theory. Given a collection of "weak partitions" of a data set, tangles provide a framework to aggregate these weak partitions such that they "point in the direction of a cluster". As a result, a cluster is softly characterized by a set of consistent pointers. This mechanism provides a highly flexible way of solving soft clustering problems in a variety of setups, ranging from questionnaires over community detection in graphs to clustering points in metric spaces. Conceptually, tangles have many intriguing properties: (1) Similar to boosting, which combines many weak classifiers to a strong classifier, tangles provide a formal way to combine many weak partitions to obtain few strong clusters. (2) In terms of computational complexity, tangles allow us to use simple, fast algorithms to produce the weak partitions. The complexity of identifying the strong partitions is dominated by the number of weak partitions, not the number of data points, leading to an interesting trade-off between the two. (3) If the weak partitions are interpretable, so are the strong partitions induced by the tangles, resulting in one of the rare algorithms to produce interpretable clusters. (4) The output of tangles is of a hierarchical nature, inducing the notion of a soft dendrogram that can be helpful in data visualization.
Practical applications of metric space magnitude and weighting vectors

Metric space magnitude, an active subject of research in algebraic topology, originally arose in the context of biology, where it was used to represent the effective number of distinct species in an environment. In a more general setting, the magnitude of a metric space is a real number that aims to quantify the effective number of distinct points in the space. The contribution of each point to a metric space's global magnitude, which is encoded by the {\em weighting vector}, captures much of the underlying geometry of the original metric space. Surprisingly, when the metric space is Euclidean, the weighting vector also serves as an effective tool for boundary detection. This allows the weighting vector to serve as the foundation of novel algorithms for classic machine learning tasks such as classification, outlier detection and active learning. We demonstrate, using experiments and comparisons on classic benchmark datasets, the promise of the proposed magnitude and weighting vector-based approaches.
Cobordism Conjecture in AdS

McNamara and Vafa conjectured that any pair of consistent quantum gravity theories can be connected by a domain wall. We test the conjecture in the context of the AdS/CFT correspondence. There are topological constraints on existence of an interface between the corresponding conformal field theories. We discuss how to construct domain walls in AdS predicted by the conjecture when the corresponding conformal interfaces are prohibited by topological obstructions.
Diffusion-Weighted Magnetic Resonance Brain Images Generation with Generative Adversarial Networks and Variational Autoencoders: A Comparison Study

We show that high quality, diverse and realistic-looking diffusion-weighted magnetic resonance images can be synthesized using deep generative models. Based on professional neuroradiologists' evaluations and diverse metrics with respect to quality and diversity of the generated synthetic brain images, we present two networks, the Introspective Variational Autoencoder and the Style-Based GAN, that qualify for data augmentation in the medical field, where information is saved in a dispatched and inhomogeneous way and access to it is in many aspects restricted.
Minimum Cost Active Labeling

Labeling a data set completely is important for groundtruth generation. In this paper, we consider the problem of minimum-cost labeling: classifying all images in a large data set with a target accuracy bound at minimum dollar cost. Human labeling can be prohibitive, so we train a classifier to accurately label part of the data set. However, training the classifier can be expensive too, particularly with active learning. Our min-cost labeling uses a variant of active learning to learn a model to predict the optimal training set size for the classifier that minimizes overall cost, then uses active learning to train the classifier to maximize the number of samples the classifier can correctly label. We validate our approach on well-known public data sets such as Fashion, CIFAR-10, and CIFAR-100. In some cases, our approach has 6X lower overall cost relative to human labeling, and is always cheaper than the cheapest active learning strategy.
Coherent emission of erbium dopants in a high-Q resonator

The stability and outstanding coherence of dopants and other atom-like defects in tailored host crystals make them a leading platform for the implementation of distributed quantum information processing and sensing in quantum networks. Albeit the required efficient light-matter coupling can be achieved via the integration into nanoscale resonators, in this approach the proximity of interfaces is detrimental to the coherence of even the least-sensitive emitters. Here, we establish an alternative: By integrating a 19 micrometer thin erbium-doped crystal into a cryogenic Fabry-Perot resonator with a quality factor of nine million, we can demonstrate 59(6)-fold enhancement of the emission rate, corresponding to a two-level Purcell factor of 530(50), while preserving lifetime-limited optical coherence up to 0.54(1) ms. With its emission at the minimal-loss wavelength of optical fibers and its outcoupling efficiency of 46(8) %, our system enables coherent and efficient nodes for long-distance quantum networks.
Did You Remember To Test Your Tokens?

Authentication is a critical security feature for confirming the identity of a system's users, typically implemented with help from frameworks like Spring Security. It is a complex feature which should be robustly tested at all stages of development. Unit testing is an effective technique for fine-grained verification of feature behaviors that is not widely-used to test authentication. Part of the problem is that resources to help developers unit test security features are limited. Most security testing guides recommend test cases in a “black box” or penetration testing perspective. These resources are not easily applicable to developers writing new unit tests, or who want a security-focused perspective on coverage. In this paper, we address these issues by applying a grounded theory-based approach to identify common (unit) test cases for token authentication through analysis of 481 JUnit tests exercising Spring Security-based authentication implementations from 53 open source Java projects. The outcome of this study is a developer-friendly unit testing guide organized as a catalog of 53 test cases for token authentication, representing unique combinations of 17 scenarios, 40 conditions, and 30 expected outcomes learned from the data set in our analysis. We supplement the test guide with common test smells to avoid. To verify the accuracy and usefulness of our testing guide, we sought feedback from selected developers, some of whom authored unit tests in our dataset.
SOAC: The Soft Option Actor-Critic Architecture

The option framework has shown great promise by automatically extracting temporally-extended sub-tasks from a long-horizon task. Methods have been proposed for concurrently learning low-level intra-option policies and high-level option selection policy. However, existing methods typically suffer from two major challenges: ineffective exploration and unstable updates. In this paper, we present a novel and stable off-policy approach that builds on the maximum entropy model to address these challenges. Our approach introduces an information-theoretical intrinsic reward for encouraging the identification of diverse and effective options. Meanwhile, we utilize a probability inference model to simplify the optimization problem as fitting optimal trajectories. Experimental results demonstrate that our approach significantly outperforms prior on-policy and off-policy methods in a range of Mujoco benchmark tasks while still providing benefits for transfer learning. In these tasks, our approach learns a diverse set of options, each of whose state-action space has strong coherence.
Accelerating Training in Artificial Neural Networks with Dynamic Mode Decomposition

Training of deep neural networks (DNNs) frequently involves optimizing several millions or even billions of parameters. Even with modern computing architectures, the computational expense of DNN training can inhibit, for instance, network architecture design optimization, hyper-parameter studies, and integration into scientific research cycles. The key factor limiting performance is that both the feed-forward evaluation and the back-propagation rule are needed for each weight during optimization in the update rule. In this work, we propose a method to decouple the evaluation of the update rule at each weight. At first, Proper Orthogonal Decomposition (POD) is used to identify a current estimate of the principal directions of evolution of weights per layer during training based on the evolution observed with a few backpropagation steps. Then, Dynamic Mode Decomposition (DMD) is used to learn the dynamics of the evolution of the weights in each layer according to these principal directions. The DMD model is used to evaluate an approximate converged state when training the ANN. Afterward, some number of backpropagation steps are performed, starting from the DMD estimates, leading to an update to the principal directions and DMD model. This iterative process is repeated until convergence. By fine-tuning the number of backpropagation steps used for each DMD model estimation, a significant reduction in the number of operations required to train the neural networks can be achieved. In this paper, the DMD acceleration method will be explained in detail, along with the theoretical justification for the acceleration provided by DMD. This method is illustrated using a regression problem of key interest for the scientific machine learning community: the prediction of a pollutant concentration field in a diffusion, advection, reaction problem.
Class-Similarity Based Label Smoothing for Generalized Confidence Calibration

Since modern neural networks are known to be overconfident, several techniques have been recently introduced to address this problem and improve calibration. However, the current notion of calibration is overly simple since only single prediction confidence is considered while the information regarding the rest of the classes is ignored. The output of a neural network is a probability distribution where the scores are estimated confidences of the input belonging to the corresponding classes, and hence they represent a complete estimate of the output likelihood that should be calibrated. In this paper, we first introduce a generalized definition of confidence calibration, which motivates the development of a novel form of label smoothing where the value of each class label is based on its similarity with the reference class. We adopt different similarity measurements, including those that capture semantic similarity, and demonstrate through extensive experiments the advantage of our method over both uniform label smoothing and other techniques.