Miracle-dz/UniFAR-FTUs huggingface dataset:
Demystifying Dependency Bugs in Deep Learning Stack

Deep learning (DL) applications, built upon a heterogeneous and complex DL stack (e.g., Nvidia GPU, Linux, CUDA driver, Python runtime, and TensorFlow), are subject to software and hardware dependencies across the DL stack. One challenge in dependency management across the entire engineering lifecycle is posed by the asynchronous and radical evolution and the complex version constraints among dependencies. Developers may introduce dependency bugs (DBs) in selecting, using and maintaining dependencies. However, the characteristics of DBs in DL stack is still under-investigated, hindering practical solutions to dependency management in DL stack. To bridge this gap, this paper presents the first comprehensive study to characterize symptoms, root causes and fix patterns of DBs across the whole DL stack with 446 DBs collected from StackOverflow posts and GitHub issues. For each DB, we first investigate the symptom as well as the lifecycle stage and dependency where the symptom is exposed. Then, we analyze the root cause as well as the lifecycle stage and dependency where the root cause is introduced. Finally, we explore the fix pattern and the knowledge sources that are used to fix it. Our findings from this study shed light on practical implications on dependency management.
Quintessential cosmological tensions

Several cosmological tensions have emerged in light of recent data, most notably in the inferences of the parameters $H_0$ and $\sigma_8$. We explore the possibility of alleviating both these tensions {\it simultaneously} by means of the Albrecht-Skordis ``quintessence'' potential. The field can reduce the size of the sound horizon $r_s^*$ while concurrently suppressing the power in matter density fluctuations before it comes to dominate the energy density budget today. Interestingly, this rich set of dynamics is governed entirely by one free parameter that is of $\mathcal{O}(10)$ in Planck units. We find that the inferred value of $H_0$ can be increased, while that of $\sigma_8$ can be decreased, both by $\approx 1\sigma$ compared to the $\Lambda$CDM case. However, ultimately the model is disfavored by Planck and BAO data alone, compared to the standard $\Lambda$CDM model, with a $\Delta \chi^2 \approx +6$. When including large scale structure and supernova data $\Delta \chi^2 \approx +1$. We note that historically much attention has been focused on preserving the three angular scales $\theta_D$, $\theta_{EQ}$, and $\theta_s^*$ to their $\Lambda$CDM values. Our work presents an example of how, while doing so indeed maintains a relatively good fit to the CMB data for an increased number of ultra-relativistic species, it is a-priori insufficient in maintaining such a fit in more general model spaces.
A No-Code Low-Code Paradigm for Authoring Business Automations Using Natural Language

Most business process automation is still developed using traditional automation technologies such as workflow engines. These systems provide domain specific languages that require both business knowledge and programming skills to effectively use. As such, business users often lack adequate programming skills to fully leverage these code oriented environments. We propose a paradigm for the construction of business automations using natural language. The approach applies a large language model to translate business rules and automations described in natural language, into a domain specific language interpretable by a business rule engine. We compare the performance of various language model configurations, across various target domains, and explore the use of constrained decoding to ensure syntactically correct generation of output.
An exactly solvable model for first- and second-order transitions

The possibility of an exact analytical description of first-order and second-order transitions is demonstrated using a specific microscopic model. Predictions using the exactly calculated partition function are compared with those based on the Landau and Yang–Lee approaches. The model employed is an adsorbed polymer chain with an arbitrary number of links and an external force applied to its end, for which the variation of the partition function with the adsorption interaction parameter and the magnitude of the applied force is calculated. In the thermodynamic limit, the system has one isotropic and two anisotropic, ordered phases, each of which is characterized by two order parameters and between which first-order and second-order transitions occur and a bicritical point exists. The Landau free energy is found exactly as a function of each order parameter separately and, near the bicritical point, as a function of both of them simultaneously. An exact analytical formula is found for the distribution of the complex zeros of the partition function in first-order and second-order phase transitions. Hypotheses concerning the way in which the free energy and the positions of the complex zeros scale with the number of particles N in the system are verified.
Combustion imaging from electrical impedance measurements

The development of stray-immune circuits to measure very small floating capacitances has enabled the technique of electrical capacitance tomography (ECT) to be developed. Burning fuel, for example in an internal combustion engine, produces a high concentration of ions, thus modifying the dielectric constant of the combustion volume. Thus ECT can be applied to produce tomographic images inside a combustion chamber, independent of flame luminosity. However, the major effect of the increase in ion concentration is to increase the conductivity of the volume. The ECT technique has been extended to measure both the change in permittivity and change in conductivity, yielding additional information on the flame composition. Current spatial and temporal resolution is poor but developments are in hand to overcome these limitations. Alternatively, the way forward may well be to abandon images and instead use extremely simple and cheap sensors to monitor knock on a cycle-by-cycle basis. An alternative use of such a simple sensor would be to monitor cycle dispersion in a lean burn engine. Both these techniques could help minimize fuel consumption, whilst maximizing power output and keeping emissions low.
A amamentação na transição puerperal: o desvelamento pelo método de pesquisa-cuidado

Pesquisa que objetivou desvelar as percepcoes do ser puerpera sobre a amamentacao diante do processo de transicao ao papel materno. A metodologia e qualitativa, utilizando o metodo de pesquisa-cuidado por meio da consulta de enfermagem. O estudo foi realizado no ambulatorio de uma maternidade publica de Curitiba. Para compreender o significado dos depoimentos, optamos pela analise de conteudo tematica. Apos a analise dos discursos de 19 puerperas, emergiram 3 unidades de contexto e 12 unidades de registro. Considerando a importância do aleitamento materno na vivencia da transicao ao papel materno, apresentamos a analise da Unidade de Registro 11, `o enfrentamento da amamentacao'. Concluimos que a compreensao do significado e a forma como as mulheres vivenciam o aleitamento materno pode contribuir para que o cuidado de enfermagem ultrapasse a dimensao tecnica, tornando-se humanizado e individualizado.
Semantic-Aware Fine-Grained Correspondence

Establishing visual correspondence across images is a chal-lenging and essential task. Recently, an influx of self-supervised methods have been proposed to better learn representations for visual correspondence. However, we find that these methods often fail to leverage semantic information and over-rely on the matching of low-level features. In contrast, human vision is capable of distinguishing between distinct objects as a pretext to tracking. Inspired by this paradigm, we propose to learn semantic-aware fine-grained correspondence. Firstly, we demonstrate that semantic correspondence is implicitly available through a rich set of image-level self-supervised methods. We further design a pixel-level self-supervised learning objective which specifically targets fine-grained correspondence. For downstream tasks, we fuse these two kinds of complementary correspondence representations together, demonstrating that they boost performance synergistically. Our method surpasses previous state-of-the-art self-supervised methods using convolutional networks on a variety of visual correspondence tasks, including video object segmentation, human pose tracking, and human part tracking.
Calculation of shock problems by using four different schemes

Results are shown of the use of several different shock treatments in one- and two-dimensional Lagrangian code calculations of strong shock problems with known solutions. The shock treatments are (A) von Neumann-Richtmyer artificial viscosity; (B) Fixed length artificial viscosity; (C) Artificial energy diffusivity combined with artificial voscosity; and (D) Modified Godunov. The similarity test problems are plane and spherical implosions followed through a reflection. The problems are generated in the codes by imposing an inward directed velocity at one boundary of an initially, quiescent, gamma law gas with the other boundary fixed. Results are shown for calculations on uniform and non-uniform meshes. On non-uniform meshes, no method gives good results although method D is probably superior. Method B produces good appearing results with much shock smearing for the initial shock transit but the worst results after shock reflection. On uniform meshes, method C does the best job of handling the effects of shock initiation at boundaries while method A produces the worst results. Method D results in the smoothest flow field with less over-or-under-shooting, Gibbs phenomena. 14 references.
Intertwinings of tensor products, and the stochastic centralizer of dynamical systems

A dynamical system is called -simple if all its ergodic joinings of the second order (except for ) are measures concentrated on the graphs of finite-valued maps commuting with the system, the number of inequivalent graphs of this kind being at most countable. This class of dynamical systems contains, for example, horocycle flows and mixing actions of the group with partial cyclic approximation. It is proved in this paper that -simple mixing flows have multiple mixing, which is a consequence of results on stochastic intertwinings of flows. Properties of dynamical systems with general time are investigated in this direction, including actions with discrete and non-commutative time. The results obtained depend on the type of system.
Auto Machine Learning for Medical Image Analysis by Unifying the Search on Data Augmentation and Neural Architecture

Automated data augmentation, which aims at engineering augmentation policy automatically, recently draw a growing research interest. Many previous auto-augmentation methods utilized a Density Matching strategy by evaluating policies in terms of the test-time augmentation performance. In this paper, we theoretically and empirically demonstrated the inconsistency between the train and validation set of small-scale medical image datasets, referred to as in-domain sampling bias. Next, we demonstrated that the in-domain sampling bias might cause the inefficiency of Density Matching. To address the problem, an improved augmentation search strategy, named Augmented Density Matching, was proposed by randomly sampling policies from a prior distribution for training. Moreover, an efficient automatical machine learning(AutoML) algorithm was proposed by unifying the search on data augmentation and neural architecture. Experimental results indicated that the proposed methods outperformed state-of-the-art approaches on MedMNIST, a pioneering benchmark designed for AutoML in medical image analysis.
A convergence analysis of iterative methods for the solution of nonlinear ill-posed problems under affinely invariant conditions

For iterative methods for well-posed problems, invariance properties have been used to provide a unified framework for convergence analysis. We carry over this approach to iterative methods for nonlinear ill-posed problems and prove convergence with rates for the Landweber and the iteratively regularized Gauss-Newton methods. The conditions needed are weaker as far as the nonlinearity is concerned than those needed in earlier papers and apply also to severely ill-posed problems. With no additional effort, we can also treat multilevel versions of our methods.
Effect of some soil media types and growth regulators on lolium perenne

The present study was designed to explore the possibility of growing Lolium perenne turf grass in sandy amended soils rather than clay soil. In addition to reducing the expensive costs and time consumption due to frequent clipping by the application of some growth retardants. Four growing media types were tested (clay, sand, sand / clay and sand / compost). While cycocel and alar were foliar sprayed, three times at four weeks intervals at the concentrations of 1000 and 2000 ppm for each one. Growing Lolium perenne in clay soil greatly and significantly gave rise to the four tested vegetative growth parameters (plant height, covering density % and fresh and dry weights) as well as, the three photosynthetic pigments over the other three growing media types. On the other hand, sandy soil resulted consistently, in the lowest values, while the two amended sandy soil gave intermediate records. The four growth retardant treatments caused a considerable reduction in plant height and fresh and dry weights of the clippings, while resulted in remarkable increase in covering density % as well as, stimulated photosynthetic pigments. Meanwhile, alar proved to be more effective than cycocel in this concern. The interaction between growing media types and growth retardant treatments was almost significant for different vegetative growth parameters and photosynthetic pigments. It is recommended to grow Lolium perenne turf grass in sand / compost (2 : 1 v/v) amended growing media with the supplement of triple applications of alar at 2000 ppm in order to obtain reasonable reduction in plant height, along with a desirable and fast covering density % .
The dynamics of spanwise vorticity on a rotating flat plate in a starting motion

The starting rotational motion of flat, rectangular plates in quiescent flow was studied experimentally using two-dimensional and stereoscopic particle image velocimetry. The study examined the vortex dynamics of spanwise vorticity created on the suction surface of plates of aspect ratio 2 and 4, which consists primarily of a leading-edge vortex and a region of counter-rotating vorticity on the surface, directly below the leading-edge vortex. Reynolds numbers of 4,000, 8,000, and 16,000 based on the tip velocity and angles of attack of 25◦, 35◦, and 45◦ were investigated at five different azimuthal locations (ψ = 90◦, 180◦, 235◦, 270◦, and 320◦). The 25% and 50% spanwise positions for the aspect ratio 4 plate and 50% spanwise position for the aspect ratio 2 plate were studied. For the 25% and 50% spanwise location for the aspect ratio 4 and 2 plate, respectively, the leading-edge vortex structure’s shape and coherence evolve in time as the plate begins its initial motion. The circulation of the leading-edge vortex was found to vary only weakly with azimuthal position in most cases and in many cases, exhibited variations that were commensurate with the circulation of the counter-rotating region beneath it. The leading-edge vortex circulation was shown to be proportional to the theoretical bound circulation of thin airfoil theory with the aspect ratio 2 plate at the 50% spanwise position being approximately 1.25 times greater than the theoretical values while the aspect ratio 4 plate generated circulation values approximately double the theoretical values. It was apparent from these calculations that for both aspect ratios, the ratio of measured to theoretical
Surface defects, flavored modular differential equations, and modularity

Every 4d N = 2 SCFT T corresponds to an associated VOA V(T ), which is in general non-rational with a more involved representation theory. Null states in V(T ) can give rise to non-trivial flavored modular differential equations, which must be satisfied by the refined/flavored character of all the V(T )-modules. Taking some A1 theories Tg,n of class-S as examples, we construct the flavored modular differential equations satisfied by the Schur index. We show that three types of surface defect indices give rise to common solutions to these differential equations, and therefore are sources of V(T )-module characters. These equations transform almost covariantly under modular transformations, ensuring the presence of logarithmic solutions which may correspond to characters of logarithmic modules. August 4, 2022 ar X iv :2 20 7. 10 46 3v 2 [ he pth ] 3 A ug 2 02 2
An integer GARCH model for a Poisson process with time-varying zero-inflation

A serially dependent Poisson process with time-varying zero-inflation is proposed. Such formulations have the potential to model count data time series arising from phenomena such as infectious diseases that ebb and flow over time. The model assumes that the intensity of the Poisson process evolves according to a generalized autoregressive conditional heteroscedastic (GARCH) formulation and allows the zero-inflation parameter to vary over time and be governed by a deterministic function or by an exogenous variable. Both the expectation maximization (EM) and the maximum likelihood estimation (MLE) approaches are presented as possible estimation methods. A simulation study shows that both parameter estimation methods provide good estimates. Applications to two real-life data sets on infant deaths due to influenza show that the proposed integer-valued GARCH (INGARCH) model provides a better fit in general than existing zero-inflated INGARCH models. We also extended a non-linear INGARCH model to include zero-inflation and an exogenous input. This extended model performed as well as our proposed model with respect to some criteria, but not with respect to all.
Provably tuning the ElasticNet across instances

An important unresolved challenge in the theory of regularization is to set the regularization coefficients of popular techniques like the ElasticNet with general provable guarantees. We consider the problem of tuning the regularization parameters of Ridge regression, LASSO, and the ElasticNet across multiple problem instances, a setting that encompasses both cross-validation and multi-task hyperparameter optimization. We obtain a novel structural result for the ElasticNet which characterizes the loss as a function of the tuning parameters as a piecewise-rational function with algebraic boundaries. We use this to bound the structural complexity of the regularized loss functions and show generalization guarantees for tuning the ElasticNet regression coefficients in the statistical setting. We also consider the more challenging online learning setting, where we show vanishing average expected regret relative to the optimal parameter pair. We further extend our results to tuning classification algorithms obtained by thresholding regression fits regularized by Ridge, LASSO, or ElasticNet. Our results are the first general learning-theoretic guarantees for this important class of problems that avoid strong assumptions on the data distribution. Furthermore, our guarantees hold for both validation and popular information criterion objectives.
Multi‐Event‐Camera Depth Estimation and Outlier Rejection by Refocused Events Fusion

Event cameras are bio‐inspired sensors that offer advantages over traditional cameras. They operate asynchronously, sampling the scene at microsecond resolution and producing a stream of brightness changes. This unconventional output has sparked novel computer vision methods to unlock the camera's potential. Here, the problem of event‐based stereo 3D reconstruction for SLAM is considered. Most event‐based stereo methods attempt to exploit the high temporal resolution of the camera and the simultaneity of events across cameras to establish matches and estimate depth. By contrast, this work investigates how to estimate depth without explicit data association by fusing disparity space images (DSIs) originated in efficient monocular methods. Fusion theory is developed and applied to design multi‐camera 3D reconstruction algorithms that produce state‐of‐the‐art results, as confirmed by comparisons with four baseline methods and tests on a variety of available datasets.
StreamYOLO: Real-time Object Detection for Streaming Perception

—The perceptive models of autonomous driving re- quire fast inference within a low latency for safety. While existing works ignore the inevitable environmental changes after processing, streaming perception jointly evaluates the latency and accuracy into a single metric for video online perception, guiding the previous works to search trade-offs between accuracy and speed. In this paper, we explore the performance of real time models on this metric and endow the models with the capacity of predicting the future, signiﬁcantly improving the results for streaming perception. Speciﬁcally, we build a simple framework with two effective modules. One is a Dual Flow Perception module (DFP). It consists of dynamic ﬂow and static ﬂow in parallel to capture moving tendency and basic detection feature, respectively. Trend Aware Loss (TAL) is the other module which adaptively generates loss weight for each object with its moving speed. Realistically, we consider multiple velocities driving scene and further propose Velocity-awared streaming AP (VsAP) to jointly evaluate the accuracy. In this realistic setting, we design a efﬁcient mix-velocity training strategy to guide detector perceive any velocities. Our simple method achieves the state-of-the-art performance on Argoverse-HD dataset and improves the sAP and VsAP by 4.7% and 8.2% respectively compared to the strong baseline, validating its effectiveness.
UFO: Unified Feature Optimization

This paper proposes a novel Unified Feature Optimization (UFO) paradigm for training and deploying deep models under real-world and large-scale scenarios, which requires a collection of multiple AI functions. UFO aims to benefit each single task with a large-scale pretraining on all tasks. Compared with the well known foundation model, UFO has two different points of emphasis, i.e., relatively smaller model size and NO adaptation cost: 1) UFO squeezes a wide range of tasks into a moderate-sized unified model in a multi-task learning manner and further trims the model size when transferred to down-stream tasks. 2) UFO does not emphasize transfer to novel tasks. Instead, it aims to make the trimmed model dedicated for one or more already-seen task. With these two characteristics, UFO provides great convenience for flexible deployment, while maintaining the benefits of large-scale pretraining. A key merit of UFO is that the trimming process not only reduces the model size and inference consumption, but also even improves the accuracy on certain tasks. Specifically, UFO considers the multi-task training and brings two-fold impact on the unified model: some closely related tasks have mutual benefits, while some tasks have conflicts against each other. UFO manages to reduce the conflicts and to preserve the mutual benefits through a novel Network Architecture Search (NAS) method. Experiments on a wide range of deep representation learning tasks (i.e., face recognition, person re-identification, vehicle re-identification and product retrieval) show that the model trimmed from UFO achieves higher accuracy than its single-task-trained counterpart and yet has smaller model size, validating the concept of UFO. Besides, UFO also supported the release of 17 billion parameters computer vision (CV) foundation model which is the largest CV model in the industry.