Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulation and Learning for Urban Mobility: City-scale Traffic Reconstruction and Autonomous Driving

Traffic congestion has become one of the most critical issues worldwide. The costs due to traffic gridlock and jams are approximately $160 billion in the United States, more than £13 billion in the United Kingdom, and over one trillion dollars across the globe annually. As more metropolitan areas will experience increasingly severe traffic conditions, the ability to analyze, understand, and improve traffic dynamics becomes critical. This dissertation is an effort towards achieving such an ability. I propose various techniques combining simulation and machine learning to tackle the problem of traffic from two perspectives: city-scale traffic reconstruction and autonomous driving.
ProSper - A Python Library for Probabilistic Sparse Coding with Non-Standard Priors and Superpositions

ProSper is a python library containing probabilistic algorithms to learn dictionaries. Given a set of data points, the implemented algorithms seek to learn the elementary components that have generated the data. The library widens the scope of dictionary learning approaches beyond implementations of standard approaches such as ICA, NMF or standard L1 sparse coding. The implemented algorithms are especially well-suited in cases when data consist of components that combine non-linearly and/or for data requiring flexible prior distributions. Furthermore, the implemented algorithms go beyond standard approaches by inferring prior and noise parameters of the data, and they provide rich a-posteriori approximations for inference. The library is designed to be extendable and it currently includes: Binary Sparse Coding (BSC), Ternary Sparse Coding (TSC), Discrete Sparse Coding (DSC), Maximal Causes Analysis (MCA), Maximum Magnitude Causes Analysis (MMCA), and Gaussian Sparse Coding (GSC, a recent spike-and-slab sparse coding approach). The algorithms are scalable due to a combination of variational approximations and parallelization. Implementations of all algorithms allow for parallel execution on multiple CPUs and multiple machines for medium to large-scale applications. Typical large-scale runs of the algorithms can use hundreds of CPUs to learn hundreds of dictionary elements from data with tens of millions of floating-point numbers such that models with several hundred thousand parameters can be optimized. The library is designed to have minimal dependencies and to be easy to use. It targets users of dictionary learning algorithms and Machine Learning researchers.
Architecture Search by Estimation of Network Structure Distributions

The influence of deep learning is continuously expanding across different domains, and its new applications are ubiquitous. The question of neural network design thus increases in importance, as traditional empirical approaches are reaching their limits. Manual design of network architectures from scratch relies heavily on trial and error, while using existing pretrained models can introduce redundancies or vulnerabilities. Automated neural architecture design is able to overcome these problems, but the most successful algorithms operate on significantly constrained design spaces, assuming the target network to consist of identical repeating blocks. We propose a probabilistic representation of a neural network structure under the assumption of independence between layer types. A matrix of probabilities is equivalent to the population of models, but simpler to interpret and analyze. We construct an architecture search algorithm, inspired by the estimation of distribution algorithms, to take advantage of this representation. The probability matrix is tuned towards generating high-performance models by repeatedly sampling the architectures and evaluating the corresponding networks. Our algorithm is shown to discover models which are superior to handcrafted architectures and competitive with those produced by existing architecture search methods, both in accuracy and computational costs, while being conceptually simple and highly extensible.
Particle and energy cost of entanglement of Hawking radiation with the final vacuum state

A semiclassical analysis shows that in the process of black hole formation and evaporation, an initial pure state will evolve to a mixed state, i.e., information will be lost. One way of avoiding this conclusion without invoking drastic modifications of the local laws of physics in a low curvature regime would be for the information to be restored at the very end of the evaporation process. It is normally envisioned that this would require a final burst of particles entangled with the early time Hawking radiation. This would imply the emission of an extremely large number of particles from an object of Planck size and mass and would appear to be blatantly ruled out by energy considerations. However, Hotta, Schutzhold, and Unruh have analyzed a $(1+1)$-dimensional moving mirror analog of the Hawking process and have found that, in this model, information is restored via entanglement of the early time Hawking radiation with vacuum fluctuations in the spacetime region to the future of the event where the mirror returns to inertial motion. We analyze their model here and give a precise formulation of this entanglement by introducing the notion of "Milne particles. " We then analyze the inertial particle and energy cost of such an entanglement of Hawking radiation with vacuum fluctuations. We show that that, in fact, the entanglement of early time Hawking radiation with vacuum fluctuations requires the emission of at least as many late time inertial particles as Hawking particles. Although the energy cost can be made small in the $(1+1)$-dimensional mirror system, this should not be the case for the $(3+1)$-dimensional evaporating black hole system. Thus, vacuum entanglement has the same difficulties as the more usual burst scenarios for attempting to avoid information loss.
Semantic Source Code Search: A Study of the Past and a Glimpse at the Future

With the recent explosion in the size and complexity of source codebases and software projects, the need for efficient source code search engines has increased dramatically. Unfortunately, existing information retrieval-based methods fail to capture the query semantics and perform well only when the query contains syntax-based keywords. Consequently, such methods will perform poorly when given high-level natural language queries. In this paper, we review existing methods for building code search engines. We also outline the open research directions and the various obstacles that stand in the way of having a universal source code search engine.
General solution to the one-dimensional connectivity problem

We present a general method to obtain the connectivity properties of an arbitrary one-dimensional pairwise interacting n-body system in thermal equilibrium. As input, solely the pair density distribution associated to the equilibrium state is required. Accordingly, if exact analytic results exist for the pair density distribution, the pair connectivity can be determined equally exactly. This is illustrated for fully penetrable and impenetrable rods as well as a repulsive $1/r^2$ nearest-neighbor interaction potential. We also discuss implications of our work for long-ranged interactions, systems in external fields and higher dimensions.
Nonreciprocal response theory of non-Hermitian mechanical metamaterials: Response phase transition from the skin effect of zero modes

Nonreciprocal non-Hermitian systems provide an unconventional localization mechanism of topological zero modes via the non-Hermitian skin effect. While fundamental theoretical characterizations of this effect involve the biorthogonal system of right and left eigenmodes, the recent demonstration of this effect for a zero mode in a robotic metamaterial (Ghatak et al., arXiv:1907.11619) is based on the direct experimental observation of the conventional right eigenvectors. Here I show that such nonreciprocal mechanical metamaterials reveal their underlying biorthogonality in the directly observable response of the system to external excitation. Applied to the experiment, this nonreciprocal response theory predicts that the zero-mode skin effect coincides with an extended phase where the system is highly sensitive to physical perturbations, leading to a diverging response in the limit of a large system.
Feedback-based, Automated Failure Testing of Microservice-based Applications

Modern distributed applications are moving toward a microservice architecture, in which each service is developed and managed independently, and new features and updates are delivered continuously. A guiding principle of microservice architecture is that it must be built to anticipate and mitigate a variety of hardware and software failures. In order to test the fault handling capabilities of microservces, this paper presents IntelliFT, a feedback-based, automated failure testing technique for microservice based applications, which aims to expose the defects in the fault-handling logic quickly. The initial experimental result on a medium-size microservice benchmark system shows that the proposed approach is effective.
Quantum Expectation-Maximization Algorithm

Clustering algorithms are a cornerstone of machine learning applications. Recently, a quantum algorithm for clustering based on the $k$-means algorithm has been proposed by Kerenidis, Landman, Luongo, and Prakash. Based on their work, we propose a quantum expectation-maximization algorithm for Gaussian mixture models (GMMs). The robustness and quantum speedup of the algorithm are shown. We also show numerically the advantage of GMM over $k$-means algorithm for nontrivial cluster data.
Sloshing, Steklov and corners: Asymptotics of Steklov eigenvalues for curvilinear polygons

We obtain asymptotic formulae for the Steklov eigenvalues and eigenfunctions of curvilinear polygons in terms of their side lengths and angles. These formulae are quite precise: the errors tend to zero as the spectral parameter tends to infinity. The Steklov problem on planar domains with corners is closely linked to the classical sloshing and sloping beach problems in hydrodynamics; as we show it is also related to quantum graphs. Somewhat surprisingly, the arithmetic properties of the angles of a curvilinear polygon have a significant effect on the boundary behaviour of the Steklov eigenfunctions. Our proofs are based on an explicit construction of quasimodes. We use a variety of methods, including ideas from spectral geometry, layer potential analysis, and some new techniques tailored to our problem.
GAPS BETWEEN CONSECUTIVE UNTWISTING NUMBERS

Abstract For p ≥ 1, one can define a generalisation of the unknotting number tup called the pth untwisting number, which counts the number of null-homologous twists on at most 2p strands required to convert the knot to the unknot. We show that for any p ≥ 2 the difference between the consecutive untwisting numbers tup–1 and tup can be arbitrarily large. We also show that torus knots exhibit arbitrarily large gaps between tu1 and tu2.
Some Aspects of Geometric Computer Vision for Analysing Dynamical Scenes focusing Automotive Applications

This draft summarizes some basics about geometric computer vision needed to implement efficient computer vision algorithms for applications that use measurements from at least one digital camera mounted on a moving platform with a special focus on automotive applications processing image streams taken from cameras mounted on a car. Our intention is twofold: On the one hand, we would like to introduce well-known basic geometric relations in a compact way that can also be found in lecture books about geometric computer vision like [1, 2]. On the other hand, we would like to share some experience about subtleties that should be taken into account in order to set up quite simple but robust and fast vision algorithms that are able to run in real time. We added a conglomeration of literature, we found to be relevant when implementing basic algorithms like optical flow, visual odometry and structure from motion. The reader should get some feeling about how the estimates of these algorithms are interrelated, which parts of the algorithms are critical in terms of robustness and what kind of additional assumptions can be useful to constrain the solution space of the underlying usually non-convex optimization problems.
Approximation Guarantees of Stochastic Greedy Algorithms for Non-monotone Submodular Maximization with a Size Constraint

The stochastic greedy algorithm (SG) is a randomized version of the greedy algorithm for submodular maximization with a size constraint. SG is highly practical since it is fast, delivers high empirical performance, and is easy to implement. However, its approximation guarantee has been proved only for monotone objective functions; this is natural since the original greedy algorithm is known to perform arbitrarily poorly for nonmonotone objectives in general. In this paper, contrary to the expectation, we prove an interesting result: Thanks to the randomization, SG (with slight modification) can achieve almost 1/4-approximation guarantees in expectation even for non-monotone objective functions. Our result provides practical and theoretically guaranteed algorithms for non-monotone submodular maximization with size a constraint, which run far faster and achieve as good objective values as existing algorithms.
Adabot: Fault-Tolerant Java Decompiler

Reverse Engineering(RE) has been a fundamental task in software engineering. However, most of the traditional Java reverse engineering tools are strictly rule defined, thus are not fault-tolerant, which pose serious problem when noise and interference were introduced into the system. In this paper, we view reverse engineering as a statistical machine translation task instead of rule-based task, and propose a fault-tolerant Java decompiler based on machine translation models. Our model is based on attention-based Neural Machine Translation (NMT) and Transformer architectures. First, we measure the translation quality on both the redundant and purified datasets. Next, we evaluate the fault-tolerance(anti-noise ability) of our framework on test sets with different unit error probability (UEP). In addition, we compare the suitability of different word segmentation algorithms for decompilation task. Experimental results demonstrate that our model is more robust and fault-tolerant compared to traditional Abstract Syntax Tree (AST) based decompilers. Specifically, in terms of BLEU-4 and Word Error Rate (WER), our performance has reached 94.50% and 2.65% on the redundant test set; 92.30% and 3.48% on the purified test set.
Carbon-Related Bilayers: Nanoscale Building Blocks for Self-Assembly Nanomanufacturing

Using a first-principles total energy methodology, we investigated the properties of graphene-like carbon mono and bilayers, functionalized with nitrogen and boron atoms. The resulting stable structures were explored in terms of their potential use as nanoscale two-dimensional building blocks for self-assembly of macroscopic structures. We initially considered graphene monolayers functionalized with nitrogen and boron, but none of them was dynamically stable in terms of the respective layer phonon spectra. Then, we considered the functionalized graphene-like bilayers (labeled as NCCN, NCNC, BCCB, and NCCB), analyzing their stability, electronic and mechanical properties, and chemical reactivity. We found that although the NCCN, NCNC, and NCCB bilayers were stable, the BCCB one was not. Additionally, the NCCN and NCCB bilayers were explored as potential two-dimensional building blocks for nanostructure self-assembly, which could form stable bulk structures. Particularly, the NCCB bilayer seemed to be the b.
The Euler characteristic of a transitive Lie algebroid

We apply the Atiyah-Singer index theorem and tensor products of elliptic complexes to the cohomology of transitive Lie algebroids. We prove that the Euler characteristic of a representation of a transitive Lie algebroid $A$ over a compact manifold $M$ vanishes unless $A=TM$, and prove a general Kunneth formula. As applications we give a short proof of a vanishing result for the Euler characteristic of a principal bundle calculated using invariant differential forms, and show that the cohomology of certain Lie algebroids are exterior algebras. The latter result can be seen as a generalization of Hopf's theorem regarding the cohomology of compact Lie groups.
Nakamoto Consensus with Verifiable Delay Puzzle

This paper summarizes our work-in-progress on a new consensus protocol based on verifiable delay function. First, we introduce the concept of verifiable delay puzzle (VDP), which resembles the hashing puzzle used in the PoW mechanism but can only be solved sequentially. We then present a VDP implementation based on Pietrzak's verifiable delay function. Further, we show that VDP can be combined with the Nakamoto consensus in a proof-of-stake/proof-of-delay hybrid protocol. We analyze the persistence and liveness of the protocol, and show that compared to PoW, our proposal consumes much less energy; compared to BFT based consensus algorithms which usually place an upper limit on the number of consensus nodes, our proposal is much more scalable and can thus achieve a higher level of decentralization.
An Estimation Method of Calorie Consumption in Activities of Daily Living based on METs Values

It is important to know the calorie consumption at the time of life activities for prevention of Lifestyle diseases. In this paper, we propose a method of measuring calorie consumption with high precision. Although there are some studies to calculate the calorie consumption by METs values adapting the state estimation method of the person using an accelerometer, it is insufficient to calculate calorie consumption with high precision for the estimation of the states such as road condition and moving speed. The proposed method aims to calculate calorie consumption with high precision during the life activity by estimating road condition and moving speed in addition to the items of conventional estimation using the accelerometer of smartphone. Finally, from the accuracy evaluation of the proposed method, estimation errors were able to be reduced by about 83% from the conventional methods.
Further Evidence on the Uniqueness of Community Banks and Boundaries of the Firm : Service Quality and Small Business

Using survey data from 1987 through 2001, this paper analyzes whether a comparative advantage exists for community banks in service delivery to small businesses. This performance measure is based on ratings of a set of characteristics related to service delivery: accessibility of loan officer, number of services offered, capability of staff, staff turnover. After controlling for the self-selection of small banks based on characteristics that are important to the conduct of their banking business, owners of small firms are found to rate small bank service delivery better than large bank service delivery. This conclusion holds over the entire period that includes dramatic consolidation within the banking system as well as technological change in the delivery of financial services.
Oral or Intramuscular Vitamin D Replacement ?

Vitamin D deficiency has been shown to be extremely common condition even in sunny climate. We conducted a retrospective analysis on 123 cases to compare oral versus intramuscular cholecalciferol replacement in cases of vitamin D deficiency as well as different doses of intramuscular supplement as assessed by serum levels of total 25 hydroxy vitamin D3. There was a significant increase in vitamin D level after therapy in all regimens (oral, intramuscular 300,000 IU and 600,000 IU). However, the difference in percent change in vitamin D level as a result of therapy was not significant between the 3 groups.
OPTIMIZATION OF NEURAL NETWORK PERFORMANCES BY MEANS OF EXOGENOUS INPUT VARIABLES FOR THE FORECAST OF OZONE POLLUTANT IN ROME URBAN AREA

INTRODUCTION Air quality problems produced by high levels of ozone affect human health and are related to respiratory problems. Ozone is a reactive gas and presents concentrations which are dependent both from the meteorological conditions and seasonal effects. The time forecasting of Ozone levels is very complicated to obtain as described in different studies [2] [5]. For Ozone models the most difficult problems to deal with are the simulation of chemical reactions that occur in atmosphere, the contribution due to long range transport and the turbulence conditions [3]. Among the complex systems, an important tool in order to forecast air pollution data is the neural network (NN) [8] that can be used in assessing the non linear dynamics of such systems. Another tool we used to forecast ozone is the support vector machine (SVM). Both models (NN and SVM) have been used to forecast ozone using data at different temporal lags, and utilizing different input during training phase.
Efekty restrykcyjnej polityki fiskalnej w zmodyfikowanym modelu Mankiwa-Summersa

Model IS-LM miał duży udział w rozpowszechnieniu poglądu o ujemnej – w krótkim okresie – zależności produktu od zmiany salda sektora finansów publicznych. Tymczasem w wyniku modyfikacji choćby pojedynczego założenia tego modelu znak tej zależności może zmienić się na niekeynesowski. Artykuł przedstawia skutki modyfikacji funkcji popytu na pieniądz zaproponowanej przez Mankiwa i Summersa (1984). Dodatkowo gospodarkę w modelu otwarto na międzynarodową wymianę handlową i przepływy kapitału. Pokazano, że im więcej zależności zakładanych w modelu IS-LM przybliży się do występujących w rzeczywistości, tym bardziej niepewna staje się keynesowska reakcja produktu na zmiany deficytu.
APPLICATION DE TECHNIQUES DE REDUCTION DE LA DIMENSIONNALITE A LA MAINTENANCE PREDICTIVE DE TURBOMACHINES AERONAUTIQUES

Monitoring of gas turbines can lead to optimize their performance, improve safety and lower operating costs through better predictive maintenance. To do so, up to a few hundreds of internal engine and external environmental parameters are measured in real time in order to building statistical models of predictive maintenance. Most of such parameters are multivariate time series exhibiting very complex patterns, from which information has to be extracted by a first set of dimension reduction techniques known as Feature Extraction. This first phase generates predictors for subsequent statistical models. However, such predictors can number by the hundreds and a second set of dimension reduction techniques, known as Feature Subset Selection in the literature, has to be applied to select the 10 most relevant predictors. The communication will present an overview of the techniques available on each of the two phases of dimensionality reduction and compare their performance on a high-dimensional dataset comprising real industrial data from a leading manufacturer of jet engines.
Pedestrian Detection using HOG and SVM in Automotives

This technical report describes the problem of pedestrian detection using Histograms of Oriented Gradients in combination with linear Support Vector Machines. This particular pairing has, for years, shown beneficial performance over other methods approaching similar problems. This report aims to explain why this is the case by explaining both methods. In addition, we outline why this particular combination of algorithms is specifically useful in the automotive setting and may support function such as pedestrian detection, which eventually may lead to automatic breaking assist functionality in a car. We provide empirical results of the described approach in form of qualitative and quantitative evaluation on two different datasets.