Miracle-dz/UniFAR-FTUs huggingface dataset:
HVH: Learning a Hybrid Neural Volumetric Representation for Dynamic Hair Performance Capture

Capturing and rendering life-like hair is particularly challenging due to its fine geometric structure, the complex physical interaction and its non-trivial visual appearance. Yet, hair is a critical component for believable avatars. In this paper, we address the aforementioned problems: 1) we use a novel, volumetric hair representation that is composed of thousands of primitives. Each primitive can be rendered efficiently, yet realistically, by building on the latest advances in neural rendering. 2) To have a reliable control signal, we present a novel way of tracking hair on the strand level. To keep the computational effort manageable, we use guide hairs and classic techniques to expand those into a dense hood of hair. 3) To better enforce temporal consistency and generalization ability of our model, we further optimize the 3D scene flow of our representation with multiview optical flow, using volumetric raymarching. Our method can not only create realistic renders of recorded multi-view sequences, but also create renderings for new hair configurations by providing new control signals. We compare our method with existing work on viewpoint synthesis and drivable animation and achieve state-of-the-art results. https://ziyanw1.github. io/hvh.
Software solutions for numerical modeling of wide-field telescopes

This paper presents an integrated modeling software to analyze the PSF of wide-field telescopes affected by misalignments. Even relatively small misalignments in the optical system of a telescope can significantly deteriorate the image quality by introducing large aberrations. In particular, wide-field telescopes are critically affected by these errors, insomuch that usually a closed-loop active optics system is adopted for a continuous correction, rather than for sporadic alignment procedures. Typically, a ray-tracing software such as Zemax OpticStudio is employed to accurately analyze the system during the optical design. However, an analytical model of the optical system is preferable when the PSF of the telescope must be reconstructed quickly for algorithmic purposes. Here the analytical model is derived through a hybrid approach and developed in a custom software package, designed to be general and flexible in order to be tailored to different optical configurations. First, leveraging on the Zemax OpticStudio API, the ray-tracing software is integrated into a Matlab pipeline. This allows to perform a statistical analysis by automatically simulating the system response in a variety of misaligned working conditions. Then, the resulting dataset is employed to populate a database of parameters describing the model.
Surrogate models for manufactured materials with random imperfections

Manufactured materials usually contain random imperfections due to the fabrication process, e.g., the 3D-printing, casting, etc. In this work, we present a new flexible class of digital surrogate models which imitate the manufactured material respecting the statistical features of random imperfections. The surrogate model is constructed as the level-set of a linear combination of the intensity field representing the topological shape and the Gaussian perturbation representing the imperfections. The mathematical design parameters of the model are related to physical ones and thus easy to comprehend. The calibration of the model parameters is performed using progressive batching sub-sampled quasi-Newtion minimization, using a designed distance measure between synthetic samples and the data. Then, owing to a fast sampling algorithm, we have access to an arbitrary number of synthetic samples that can be used in Monte Carlo type methods for uncertainty quantification of the material response. In particular, we illustrate the method with a surrogate model for an imperfect octet-truss lattice cell, which plays an important role in additive manufacturing. We also discuss potential model extensions.
Transient frailty induced by cell division. Observation, reasons and implications

We know that stress-factors, e.g. X-rays, have an effect on cells that is more lethal in rapid exponential growth than in stationary phase. It is this effect which makes radiotherapy effective in cancer treatment. This stress effect can be explained in two ways: (a) more vulnerability in the growth phase, (b) improved protection capacity and repair mechanisms in the stationary phase. Although the two explanations do not exclude each other, they are very different in the sense that (a) is a general mechanism whereas (b) is strain and stress-factor dependent. In this paper we explore major facets of (a). Firstly, we emphasize that (a) can account for known experimental stress-factor evidence. Secondly, we observe that (a) rightly predicts that slow exponential growth (meaning with a doubling time of several hours) results in a lower death rate than fast exponential growth (doubling time of a fraction of one hour), an effect that cannot be explained in the (b) framework because both organisms are in the same phase. Thirdly, we conjecture that the stress-factor effect can be extended to situations of chronic stress due to non-optimal environmental conditions. If correct, this conjecture would imply that even in normal culture conditions the natural death rate is lower in the stationary phase than in the growth phase. Finally, the paper closes with the description of several open questions and of appropriate test-experiments meant to address them.
Control-Tutored Reinforcement Learning: Towards the Integration of Data-Driven and Model-Based Control

We present an architecture where a feedback controller derived on an approximate model of the environment assists the learning process to enhance its data efficiency. This architecture, which we term as Control-Tutored Q-learning (CTQL), is presented in two alternative flavours. The former is based on defining the reward function so that a Boolean condition can be used to determine when the control tutor policy is adopted, while the latter, termed as probabilistic CTQL (pCTQL), is instead based on executing calls to the tutor with a certain probability during learning. Both approaches are validated, and thoroughly benchmarked against Q-Learning, by considering the stabilization of an inverted pendulum as defined in OpenAI Gym as a representative problem.
A simple proof of three properties on Simpson's 4-slot Algorithm

In this paper we present an invariance proof of three properties on Simpson's 4-slot algorithm, i.e. data-race freedom, data coherence and data freshness, which together implies linearisability of the algorithm. It is an extension of previous works whose proof focuses mostly on data-race freedom. In addition, our proof uses simply inductive invariants and transition invariants, whereas previous work uses more sophisticated machinery like separation logics, rely-guarantee or ownership transfer.
Change Detection Meets Visual Question Answering

The Earth’s surface is continually changing, and identifying changes plays an important role in urban planning and sustainability. Although change detection techniques have been successfully developed for many years, these techniques are still limited to experts and facilitators in related fields. In order to provide every user with flexible access to change information and help them better understand land-cover changes, we introduce a novel task: change detection-based visual question answering (CDVQA) on multitemporal aerial images. In particular, multitemporal images can be queried to obtain high-level change-based information according to content changes between two input images. We first build a CDVQA dataset, including multitemporal image–question–answer triplets using an automatic question–answer generation method. Then, a baseline CDVQA framework is devised in this work, and it contains four parts: multitemporal feature encoding, multitemporal fusion, multimodal fusion, and answer prediction. In addition, we also introduce a change enhancing module to multitemporal feature encoding, aiming at incorporating more change-related information. Finally, the effects of different backbones and multitemporal fusion strategies are studied on the performance of CDVQA task. The experimental results provide useful insights for developing better CDVQA models, which are important for future research on this task. The dataset will be available at https://github.com/YZHJessica/CDVQA.
Differential EEG Characteristics during Working Memory Encoding and Re-encoding

Many studies have discussed the difference in brain activity related to encoding and retrieval of working memory (WM) tasks. However, it remains unclear if there is a change in brain activation associated with re-encoding. The main objective of this study was to compare different brain states (rest, encoding, and re-encoding) during the WM task. We recorded brain activity from thirty-seven participants using an electroencephalogram and calculated power spectral density (PSD) and phase-locking value (PLV) for different frequencies. In addition, the difference in phase-amplitude coupling (PAC) between encoding and re-encoding was investigated. Our results showed that alpha PSD decreased as the learning progressed, and theta PLV, beta PLV, and gamma PLV showed differences between brain regions. Also, there was a statistically significant difference in PAC. These findings suggest the possibility of improving the efficiency of learning during re-encoding by understanding the differences in neural correlation related to learning.
Acoustic Sensing-based Hand Gesture Detection for Wearable Device Interaction

Hand gesture recognition attracts great attention for interaction since it is intuitive and natural to perform. In this paper, we explore a novel method for interaction by using bone-conducted sound generated by finger movements while performing gestures. We design a set of gestures that generate unique sound features, and capture the resulting sound from the wrist using a commodity microphone. Next, we design a sound event detector and a recognition model to classify the gestures. Our system achieves an overall accuracy of 90.13% in quiet environments and 85.79% under noisy conditions. This promising technology can be deployed on existing smartwatches as a low power service at no additional cost, and can be used for interaction in augmented and virtual reality applications.
BPR CONCEPT AS THE FACTOR OF SUCCESSFUL ELECTRONIC BUSINESS OF A MODERN COMPANIES

We exist in the Internet age, of ever faster, drama tic, complex and unpredictable changes, which have various names: age of information, the age of third informa tics revolution, new economy, digital economy, digi tal revolution, web economy, economy of knowledge, information economy, etc. Internet requires new ways of modern business, and the concept of Business Process Reengineering (BPR) is a tool that can be achieve d. If the company had not applied the concept of BPR and hasn’t strong reengineered business processes its electronic business will be a nightmare, not a perf ect ideal.
Nalin: learning from Runtime Behavior to Find Name-Value Inconsistencies in Jupyter Notebooks

Variable names are important to understand and maintain code. If a variable name and the value stored in the variable do not match, then the program suffers from a name-value inconsistency, which is due to one of two situations that developers may want to fix: Either a correct value is referred to through a misleading name, which negatively affects code understandability and maintainability, or the correct name is bound to a wrong value, which may cause unexpected runtime behavior. Finding name-value inconsistencies is hard because it requires an understanding of the meaning of names and knowledge about the values assigned to a variable at runtime. This paper presents Nalin, a technique to automatically detect name-value inconsistencies. The approach combines a dynamic analysis that tracks assignments of values to names with a neural machine learning model that predicts whether a name and a value fit together. To the best of our knowledge, this is the first work to formulate the problem of finding coding issues as a classification problem over names and runtime values. We apply Nalin to 106,652 real-world Python programs, where meaningful names are particularly important due to the absence of statically declared types. Our results show that the classifier detects name-value inconsistencies with high accuracy, that the warnings reported by Nalin have a precision of 80% and a recall of 76% w.r. t. a ground truth created in a user study, and that our approach complements existing techniques for finding coding issues.
Turbulent dispersion of breath by the wind

The pioneering work of Taylor on the turbulent dispersion of aerosols is one century old and provides an interesting way to introduce both diffusive processes and turbulence at an undergraduate level. Low mass particles transported by a turbulent flow exhibit a Brownian-like motion over time scales larger than the velocity correlation time. Aerosols and gases are, therefore, subjected to an effective turbulent diffusion at large length scales. However, the case of a source of pollutant much smaller than the integral scale is not completely understood. Here, we present experimental results obtained by undergraduate students in the context of the COVID-19 pandemic. The dispersion of a fog of oil droplets by a turbulent flow is studied in a wind tunnel designed for pedagogical purposes. It shows a ballistic-like regime at short distance, followed by Taylor's diffusive-like regime, suggesting that scale-free diffusion by the turbulent cascade process is bypassed. Measurements show that the dispersion of CO2 emitted when breathing in a natural, indoor air flow is not isotropic but rather along the flow axis. The transverse spread is ballistic-like, leading to the concentration decaying as the inverse-squared distance to the mouth. The experiment helps students understand the role of fluctuations in diffusive processes and in turbulence. A Langevin equation governing aerosol dispersion based on a single correlation time allows us to model the airborne transmission risk of pathogens, indoors and outdoors. The results obtained in this study have been used to provide public health policy recommendations to prevent transmission in shopping malls.
Advanced static compensation using a multilevel GTO thyristor inverter

This paper investigates the possibility of using the multilevel gate turn-off (GTO) thyristor inverter as an advanced static VAr compensator (STATCON). The studies have indicated that the design shows promise and that the fundamental frequency switching of the GTO thyristors is superior to various forms of pulse-width modulation. The main advantages of the multilevel STATCON over other types of STATCON is that each individual GTO thyristor is clamped to a DC voltage and can be switched independently. Hence a staircase type of output voltage can be used to reduce the harmonics, the filtering requirements and the switching losses. >
Analysis of the Pennsylvania Additive Classification Tool: Biases and Important Features

The Pennsylvania Additive Classification Tool (PACT) is a carceral algorithm used by the Pennsylvania Department of Corrections in order to determine the security level for an incarcerated person in the state's prison system. For a newly incarcerated person it is used in their initial classification. The initial classification can be overridden both for discretionary and administrative reasons. An incarcerated person is reclassified annually using a variant of the PACT and this reclassification can be overridden, too, and for similar reasons. In this paper, for each of these four processes (the two classifications and their corresponding overrides), we develop several logistic models, both binary and multinomial, to replicate these processes with high accuracy. By examining these models, we both identify which features are most important in the model and quantify and describe biases that exist in the PACT, its overrides, and its use in reclassification. Because the details of how the PACT operates have been redacted from public documents, it is important to know how it works and what disparate impact it might have on different incarcerated people.
Convergence Rate Analysis of Accelerated Forward-Backward Algorithm with Generalized Nesterov Momentum Scheme

Nesterov's accelerated forward-backward algorithm (AFBA) is an efficient algorithm for solving a class of two-term convex optimization models consisting of a differentiable function with a Lipschitz continuous gradient plus a nondifferentiable function with a closed form of its proximity operator. It has been shown that the iterative sequence generated by AFBA with a modified Nesterov's momentum scheme converges to a minimizer of the objective function with an $o\left(\frac{1}{k^2}\right)$ convergence rate in terms of the function value (FV-convergence rate) and an $o\left(\frac{1}{k}\right)$ convergence rate in terms of the distance between consecutive iterates (DCI-convergence rate). In this paper, we propose a more general momentum scheme with an introduced power parameter $\omega\in(0,1]$ and show that AFBA with the proposed momentum scheme converges to a minimizer of the objective function with an $o\left(\frac{1}{k^{2\omega}}\right)$ FV-convergence rate and an $o\left(\frac{1}{k^{\omega}}\right)$ DCI-convergence rate. The generality of the proposed momentum scheme provides us a variety of parameter selections for different scenarios, which makes the resulting algorithm more flexible to achieve better performance. We then employ AFBA with the proposed momentum scheme to solve the smoothed hinge loss $\ell_1$-support vector machine model. Numerical results demonstrate that the proposed generalized momentum scheme outperforms two existing momentum schemes.
Show and Write: Entity-aware News Generation with Image Information

Automatically writing long articles is a complex and challenging language generation task. Prior work has primarily focused on generating these articles using human-written prompt to provide some topical context and some metadata about the article. That said, for many applications, such as generating news stories, these articles are often paired with images and their captions or alt-text, which in turn are based on real-world events and may reference many different named entities that are difﬁcult to be correctly recognized and predicted by language models. To address these two problems, this paper introduces an Entity-aware News Generation method with Image iNformation, E NGIN , to incorporate news image information into language models. E NGIN produces news articles conditioned on both metadata and information such as captions and named entities extracted from images. We also propose an Entity-aware mechanism to help our model better recognize and predict the entity names in news. We perform experiments on two public large-scale news datasets, Good-News and VisualNews. Quantitative results show that our approach improves article perplexity by 4-5 points over the base models. Qualitative results demonstrate the text generated by E NGIN is more consistent with news images. We also perform article quality annotation experiment on the generated articles to validate that our model produces higher-quality articles. Finally, we investigate the effect E NGIN has on methods that automatically detect machine-generated articles.
Learning a Sequential Policy of Efficient Actions for Tangled-Prone Parts in Robotic Bin Picking

—This paper introduces an autonomous bin picking system for cable harnesses - an extremely challenging object in bin picking task. Currently cable harnesses are unsuitable to be imported to automated production due to their length and elusive structures. Considering the task of robotic bin picking where the harnesses are heavily entangled, it is challenging for a robot to pick harnesses up one by one using conventional bin picking methods. In this paper, we present an efﬁcient approach to overcoming the difﬁculties when dealing with entangled-prone parts. We develop several motion schemes for the robot to pick up a single harness avoiding any entanglement. Moreover, we proposed a learning-based bin picking policy to select both grasps and designed motion schemes in a reasonable sequence. Our method is unique due to the novelty for sufﬁciently solving the entanglement problem in picking cluttered cable harnesses. We demonstrate our approach on a set of real-world experiments, during which the proposed method is capable to perform the sequential bin picking task with both effectiveness and accuracy under a variety of cluttered scenarios.
360-DFPE: Leveraging Monocular 360-Layouts for Direct Floor Plan Estimation

We present 360-DFPE, a sequential floor plan estimation method that directly takes 360-images as input without relying on active sensors or 3D information. Our approach leverages a loosely coupled integration between a monocular visual SLAM solution and a monocular 360-room layout approach, which estimate camera poses and layout geometries, respectively. Since our task is to sequentially capture the floor plan using monocular images, the entire scene structure, room instances, and room shapes are unknown. To tackle these challenges, we first handle the scale difference between visual odometry and layout geometry via formulating an entropy minimization process, which enables us to directly align 360-layouts without knowing the entire scene in advance. Second, to sequentially identify individual rooms, we propose a novel room identification algorithm that tracks every room along the camera exploration using geometry information. Lastly, to estimate the final shape of the room, we propose a shortest path algorithm with an iterative coarse-to-fine strategy, which improves prior formulations with higher accuracy and faster run-time. Moreover, we collect a new floor plan dataset with challenging large-scale scenes, providing both point clouds and sequential 360-image information. Experimental results show that our monocular solution achieves favorable performance against the current state-of-the-art algorithms that rely on active sensors and require the entire scene reconstruction data in advance.
Right Fractional Sobolev Space via Riemann–Liouville Derivatives on Time Scales and an Application to Fractional Boundary Value Problem on Time Scales

Using the concept of fractional derivatives of Riemann–Liouville on time scales, we first introduce right fractional Sobolev spaces and characterize them. Then, we prove the equivalence of some norms in the introduced spaces, and obtain their completeness, reflexivity, separability and some embeddings. Finally, as an application, we propose a recent method to study the existence of weak solutions of fractional boundary value problems on time scales by using variational methods and critical point theory, and, by constructing an appropriate variational setting, we obtain two existence results of the problem.