Miracle-dz/UniFAR-FTUs huggingface dataset:
Anchor-Free Distributed Localization in Sensor Networks

Many sensor network applications require that each node’s sensor stream be annotated with its physical location in some common coordinate system. Manual measurement and configuration methods for obtaining location don’t scale and are error-prone, and equipping sensors with GPS is often expensive and does not work in indoor and urban deployments. Sensor networks can therefore benefit from a self-configuring method where nodes cooperate with each other, estimate local distances to their neighbors, and converge to a consistent coordinate assignment. This paper describes a fully decentralized algorithm called AFL (Anchor-Free Localization) where nodes start from a random initial coordinate assignment and converge to a consistent solution using only local node interactions. The key idea in AFL is fold-freedom, where nodes first configure into a topology that resembles a scaled and unfolded version of the true configuration, and then run a force-based relaxation procedure. We show using extensive simulations under a variety of network sizes, node densities, and distance estimation errors that our algorithm is superior to previously proposed methods that incrementally compute the coordinates of nodes in the network, in terms of its ability to compute correct coordinates under a wider variety of conditions and its robustness to measurement errors.
A Comparison of Alternative Models Applied to Stroke

Predictions of cost over well-defined time horizons are frequently required in the analysis of clinical trials and social experiments, for decision models investigating the cost-effectiveness of interventions, and for macro-level estimates of the resource impact of disease. With rare exceptions, cost predictions used in such applications continue to take the form of deterministic point estimates. However, the growing availability of large administrative and clinical data sets offers new opportunities for a more general approach to disease cost forecasting: the estimation of multivariable cost functions that yield predictions at the individual level, conditional on intervention(s), patient characteristics, and other factors. This raises the fundamental question of how to choose the “best” cost model for a given application. The central purpose of this paper is to demonstrate how to evaluate competing models on the basis of predictive validity. This concept is operationalized according to three alternative criteria: 1) root mean square error (RMSE), for evaluating predicted mean cost; 2) mean absolute error (MAE), for evaluating predicted median cost; and 3) a logarithmic scoring rule (log score), an information-theoretic index for evaluating the entire predictive distribution of cost. To illustrate these concepts, the authors conducted a split-sample analysis of data from a national sample of Medicare-covered patients hospitalized for ischemic stroke in 1991 and followed to the end of 1993. Using test and training samples of about 500,000 observations each, they investigated five models: single-equation linear models, with
An Introduction to Economic Dynamics: Contents

An examples-driven treatment of introductory economic dynamics for students with a basic familiarity with spreadsheets. Shone approaches the subject with the belief that true understanding of a subject can only be achieved by students themselves setting out a problem and manipulating it experimentally. Although all economics students now have access to spreadsheets, they are often used for little more than graphing economic data. This book encourages students to go several stages further and set up and investigate simple dynamic models. The book presents the essentials of macro and micro economic dynamics, including: demand and supply dynamics, Keynesian dynamics, IS-LM, Inflation-unemployment, dynamics of the firm, rational expectations and saddle points, fiscal dynamics and the Maastricht Treaty and Chaos theory. The book contains 50 exercises. The support website contains an additional 100 questions for students and 100 for lecturers: www.cambridge. org/resources/economics.
Enterprise resource planning: componentizing the enterprise application packages

T he so-called enterprise resource planning applications market was one of the fastest growing and most profitable areas of the software industry during the last three years of the 1990s. Some of this was clearly attributable to the Y2K effect. If you still had time, replacement was often easier than continuing to throw good money after bad into aged legacy applications. But, also discernible, there was a strong desire in many organizations to acquire functionality rather than develop custom solutions, particularly for relatively unexciting transactional applications that did not differentiate the business significantly. But we should also note that some of the packaged application vendors acquired a poor reputation, being better known for the time and cost involved in implementation than the resulting business benefits. So, we observe with interest that enterprise application providers such as SAP, Peoplesoft, Oracle, Baan, JD Edwards, and many others have been investing heavily to upgrade the architecture of their applications over the past two years. No prizes are awarded for guessing why. The market-leading enterprise applications represent some of the largest, most complex applications on the planet. The complexity comes particularly from the highly generalized nature of the packaged applications and the need to adapt and rapidly evolve to meet requirements in many different situations. The packaged application providers therefore have had a genuinely mission-critical problem to solve. How to continue adding new functionality rapidly and at low cost, while making it easier for new customers to implement, and existing customers to upgrade to that new functionality? We should not be surprised therefore that these vendors were relatively quick to recognize the benefits of components. David Sprott
An overview of Madelung deformity

Madelung deformity is a rare deformity of the upper extremity that causes pain, decreased range of motion, and altered cosmesis of the wrist. Although its exact etiology still remains unknown, research has linked the disease with a genetic basis and other associated conditions. Once this condition is diagnosed, checking for these associated conditions is needed in addition to a treatment plan. The treatment plan can be aided by recent studies that looked at both radiographic measurements and MRI to define the anatomy of the deformity. Recent studies also have focused on corrective osteotomies of both the radius and ulna, isolated procedures of the ulna, Ilizarov correction, and total distal radioulnar joint arthroplasty. The purpose of this review was to provide insight into the current literature regarding the complex nature of Madelung deformity, its etiology and associated conditions and the updates on types of treatment.
Perspective on Petahertz Electronics and Attosecond Nanoscopy

The field of attosecond nanophysics, combining the research areas of attosecond physics with nanoscale physics, has experienced a considerable rise in recent years both experimentally and theoretically. Its foundation rests on the sub-cycle manipulation and sampling of the coupled electron and near-field dynamics on the nanoscale. Attosecond nanophysics not only addresses questions of strong fundamental interest in strong-field light-matter interactions at the nanoscale, but also could eventually lead to a considerable number of applications in ultrafast, petahertz-scale electronics, and ultrafast metrology for microscopy or nanoscopy. In this perspective, we outline the current frontiers, challenges, and future directions in the field, with particular emphasis on the development of petahertz electronics and attosecond nanoscopy.
Stochasticity in Feedback Loops: Great Expectations and Guaranteed Ruin

Stochastic components in a feedback loop introduce state behaviors that are fundamentally different from those observed in a deterministic system. The effect of injecting a stochastic signal additively in linear feedback systems can be viewed as the addition of filtered stochastic noise. If the stochastic signal enters the feedback loop in a multiplicative manner, a much richer set of state behaviors emerges. These phenomena are investigated for the simplest possible system: a multiplicative noise in a scalar, integrating feedback loop. The same dynamics arise when considering a first-order system in feedback with a stochastic gain. The dynamics of this form arise naturally in a number of domains, including compound investments in finance, chemical reaction dynamics, population dynamics, epidemiology, control over lossy communication channels, and adaptive control. Understanding the nature of such dynamics in a simple system is a precursor to recognizing them in more complex stochastic dynamical systems.
Lambda-Policy Iteration with Randomization for Contractive Models with Infinite Policies: Well-Posedness and Convergence (Extended Version)

Abstract dynamic programming models are used to analyze $\lambda$-policy iteration with randomization algorithms. Particularly, contractive models with infinite policies are considered and it is shown that well-posedness of the $\lambda$-operator plays a central role in the algorithm. The operator is known to be well-posed for problems with finite states, but our analysis shows that it is also well-defined for the contractive models with infinite states studied. Similarly, the algorithm we analyze is known to converge for problems with finite policies, but we identify the conditions required to guarantee convergence with probability one when the policy space is infinite regardless of the number of states. Guided by the analysis, we exemplify a data-driven approximated implementation of the algorithm for estimation of optimal costs of constrained linear and nonlinear control problems. Numerical results indicate potentials of this method in practice.
Sampling Good Latent Variables via CPP-VAEs: VAEs with Condition Posterior as Prior

In practice, conditional variational autoencoders (CVAEs) perform conditioning by combining two sources of information which are computed completely independently; CVAEs first compute the condition, then sample the latent variable, and finally concatenate these two sources of information. However, these two processes should be tied together such that the model samples a latent variable given the conditioning signal. In this paper, we directly address this by conditioning the sampling of the latent variable on the CVAE condition, thus encouraging it to carry relevant information. We study this specifically for tasks that leverage with strong conditioning signals and where the generative models have highly expressive decoders able to generate a sample based on the information contained in the condition solely. In particular, we experiments with the two challenging tasks of diverse human motion generation and diverse image captioning, for which our results suggest that unifying latent variable sampling and conditioning not only yields samples of higher quality, but also helps the model to avoid the posterior collapse, a known problem of VAEs with expressive decoders.
Probing Cosmic-Ray Transport with Radio Synchrotron Harps in the Galactic Center

Recent observations with the MeerKAT radio telescope reveal a unique population of faint nonthermal filaments pervading the central molecular zone, a region rich in molecular gas near the Galactic center. Some of those filaments are organized into groups of almost parallel filaments, seemingly sorted by their length, so that their morphology resembles a harp with radio-emitting “strings. ” We argue that the synchrotron-emitting GeV electrons of these radio harps have been consecutively injected by the same source (a massive star or pulsar) into spatially intermittent magnetic fiber bundles within a magnetic flux tube or via time-dependent injection events. After escaping from this source, the propagation of cosmic-ray (CR) electrons inside a flux tube is governed by the theory of CR transport. We propose to use observations of radio harp filaments to gain insight into the specifics of CR propagation along magnetic fields of which there are two principle modes: CRs could either stream with self-excited magnetohydrodynamic waves or diffuse along the magnetic field. To disentangle these possibilities, we conduct hydrodynamical simulations of either purely diffusing or streaming CR electrons and compare the resulting brightness distributions to the observed synchrotron profiles of the radio harps. We find compelling evidence that CR streaming is the dominant propagation mode for GeV CRs in one of the radio harps. Observations at higher angular resolution should detect more radio harps and may help to disentangle projection effects of the possibly three-dimensional flux-tube structure of the other radio harps.
GlobalTrack: A Simple and Strong Baseline for Long-term Tracking

A key capability of a long-term tracker is to search for targets in very large areas (typically the entire image) to handle possible target absences or tracking failures. However, currently there is a lack of such a strong baseline for global instance search. In this work, we aim to bridge this gap. Specifically, we propose GlobalTrack, a pure global instance search based tracker that makes no assumption on the temporal consistency of the target's positions and scales. GlobalTrack is developed based on two-stage object detectors, and it is able to perform full-image and multi-scale search of arbitrary instances with only a single query as the guide. We further propose a cross-query loss to improve the robustness of our approach against distractors. With no online learning, no punishment on position or scale changes, no scale smoothing and no trajectory refinement, our pure global instance search based tracker achieves comparable, sometimes much better performance on four large-scale tracking benchmarks (i.e., 52.1% AUC on LaSOT, 63.8% success rate on TLP, 60.3% MaxGM on OxUvA and 75.4% normalized precision on TrackingNet), compared to state-of-the-art approaches that typically require complex post-processing. More importantly, our tracker runs without cumulative errors, i.e., any type of temporary tracking failures will not affect its performance on future frames, making it ideal for long-term tracking. We hope this work will be a strong baseline for long-term tracking and will stimulate future works in this area.
Morphological Star–Galaxy Separation

We discuss the statistical foundations of morphological star–galaxy separation. We show that many of the star–galaxy separation metrics in common use today (e.g., by Sloan Digital Sky Survey or SExtractor) are closely related both to each other, and to the model odds ratio derived in a Bayesian framework by Sebok. While the scaling of these algorithms with the noise properties of the sources varies, these differences do not strongly differentiate their performance. We construct a model of the performance of a star–galaxy separator in a realistic survey to understand the impact of observational signal-to-noise ratio (S/N) (or equivalently, 5σ limiting depth) and seeing on classification performance. The model quantitatively demonstrates that, assuming realistic densities and angular sizes of stars and galaxies, 10% worse seeing can be compensated for by approximately 0.4 mag deeper data to achieve the same star–galaxy classification performance. We discuss how to probabilistically combine multiple measurements, either of the same type (e.g., subsequent exposures), or differing types (e.g., multiple bandpasses), or differing methodologies (e.g., morphological and color-based classification). These methods are increasingly important for observations at faint magnitudes, where the rapidly rising number density of small galaxies makes star–galaxy classification a challenging problem. However, because of the significant role that the S/N plays in resolving small galaxies, surveys with large-aperture telescopes, such as LSST, will continue to see improving star–galaxy separation as they push to these fainter magnitudes.
On the Bias-Variance Tradeoff: Textbooks Need an Update

The main goal of this thesis is to point out that the bias-variance tradeoff is not always true (e.g. in neural networks). We advocate for this lack of universality to be acknowledged in textbooks and taught in introductory courses that cover the tradeoff. We first review the history of the bias-variance tradeoff, its prevalence in textbooks, and some of the main claims made about the bias-variance tradeoff. Through extensive experiments and analysis, we show a lack of a bias-variance tradeoff in neural networks when increasing network width. Our findings seem to contradict the claims of the landmark work by Geman et al. (1992). Motivated by this contradiction, we revisit the experimental measurements in Geman et al. (1992). We discuss that there was never strong evidence for a tradeoff in neural networks when varying the number of parameters. We observe a similar phenomenon beyond supervised learning, with a set of deep reinforcement learning experiments. We argue that textbook and lecture revisions are in order to convey this nuanced modern understanding of the bias-variance tradeoff.
Provable Non-Convex Optimization and Algorithm Validation via Submodularity

Submodularity is one of the most well-studied properties of problem classes in combinatorial optimization and many applications of machine learning and data mining, with strong implications for guaranteed optimization. In this thesis, we investigate the role of submodularity in provable non-convex optimization and validation of algorithms. A profound understanding which classes of functions can be tractably optimized remains a central challenge for non-convex optimization. By advancing the notion of submodularity to continuous domains (termed "continuous submodularity"), we characterize a class of generally non-convex and non-concave functions -- continuous submodular functions, and derive algorithms for approximately maximizing them with strong approximation guarantees. Meanwhile, continuous submodularity captures a wide spectrum of applications, ranging from revenue maximization with general marketing strategies, MAP inference for DPPs to mean field inference for probabilistic log-submodular models, which renders it as a valuable domain knowledge in optimizing this class of objectives. Validation of algorithms is an information-theoretic framework to investigate the robustness of algorithms to fluctuations in the input/observations and their generalization ability. We investigate various algorithms for one of the paradigmatic unconstrained submodular maximization problem: MaxCut. Due to submodularity of the MaxCut objective, we are able to present efficient approaches to calculate the algorithmic information content of MaxCut algorithms. The results provide insights into the robustness of different algorithmic techniques for MaxCut.
An Automated Deep Learning Approach for Bacterial Image Classification

Automated recognition and classification of bacteria species from microscopic images have significant importance in clinical microbiology. Bacteria classification is usually carried out manually by biologists using different shapes and morphologic characteristics of bacteria species. The manual taxonomy of bacteria types from microscopy images is time-consuming and a challenging task for even experienced biologists. In this study, an automated deep learning based classification approach has been proposed to classify bacterial images into different categories. The ResNet-50 pre-trained CNN architecture has been used to classify digital bacteria images into 33 categories. The transfer learning technique was employed to accelerate the training process of the network and improve the classification performance of the network. The proposed method achieved an average classification accuracy of 99.2%. The experimental results demonstrate that the proposed technique surpasses state-of-the-art methods in the literature and can be used for any type of bacteria classification tasks.
Lightweight and Robust Representation of Economic Scales from Satellite Imagery

Satellite imagery has long been an attractive data source providing a wealth of information regarding human-inhabited areas. While high-resolution satellite images are rapidly becoming available, limited studies have focused on how to extract meaningful information regarding human habitation patterns and economic scales from such data. We present READ, a new approach for obtaining essential spatial representation for any given district from high-resolution satellite imagery based on deep neural networks. Our method combines transfer learning and embedded statistics to efficiently learn the critical spatial characteristics of arbitrary size areas and represent such characteristics in a fixed-length vector with minimal information loss. Even with a small set of labels, READ can distinguish subtle differences between rural and urban areas and infer the degree of urbanization. An extensive evaluation demonstrates that the model outperforms state-of-the-art models in predicting economic scales, such as the population density in South Korea (R2=0.9617), and shows a high use potential in developing countries where district-level economic scales are unknown.
Learning the Globally Optimal Distributed LQ Regulator

We study model-free learning methods for the output-feedback Linear Quadratic (LQ) control problem in finite-horizon subject to subspace constraints on the control policy. Subspace constraints naturally arise in the field of distributed control and present a significant challenge in the sense that standard model-based optimization and learning leads to intractable numerical programs in general. Building upon recent results in zeroth-order optimization, we establish model-free sample-complexity bounds for the class of distributed LQ problems where a local gradient dominance constant exists on any sublevel set of the cost function. %which admit a local gradient dominance constant valid on the sublevel set of the cost function. We prove that a fundamental class of distributed control problems - commonly referred to as Quadratically Invariant (QI) problems - as well as others possess this property. To the best of our knowledge, our result is the first sample-complexity bound guarantee on learning globally optimal distributed output-feedback control policies.
A Hodge theoretic projective structure on Riemann surfaces

Given any compact Riemann surface $C$, there is a symmetric bidifferential $\hat{\eta}$ on $C\times C$, with a pole of order two on the diagonal $\Delta\subset C\times C$, which is uniquely determined by the following two properties: 1. the restriction of $\hat\eta$ to $\Delta$ coincides with the constant function $1$ on $\Delta$, and 2. the cohomology class in $H^2(C\times C, {\mathbb C})/\langle [\Delta]\rangle$ corresponding to $\hat\eta$ is of pure type $(1,1)$. The restriction of $\hat\eta$ to the nonreduced diagonal $3\Delta$ defines a projective structure on $C$. Since this projective structure on $C$ is completely intrinsic, it is natural to ask whether it coincides with the one given by the uniformization of $C$. Showing that the answer to it to be negative, we actually identify $\bar \partial s$, where $s$ is this section of the moduli of projective structures over the moduli space of curves, to be the pullback of the Siegel form by the Torelli map.