Miracle-dz/UniFAR-FTUs huggingface dataset:
WarpingGAN: Warping Multiple Uniform Priors for Adversarial 3D Point Cloud Generation

We propose WarpingGAN, an effective and efficient 3D point cloud generation network. Unlike existing methods that generate point clouds by directly learning the mapping functions between latent codes and 3D shapes, Warping-GAN learns a unified local-warping function to warp multiple identical pre-defined priors (i.e., sets of points uniformly distributed on regular 3D grids) into 3D shapes driven by local structure-aware semantics. In addition, we also in-geniously utilize the principle of the discriminator and tai-lor a stitching loss to eliminate the gaps between different partitions of a generated shape corresponding to different priors for boosting quality. Owing to the novel gen-erating mechanism, WarpingGAN, a single lightweight network after one-time training, is capable of efficiently gen-erating uniformly distributed 3D point clouds with various resolutions. Extensive experimental results demonstrate the superiority of our WarpingGAN over state-of-the-art methods in terms of quantitative metrics, visual quality, and efficiency. The source code is publicly available at https://github.com/yztang4/WarpingGAN.git.
Reflectivity spectra as absorption ones -- is it correct ?

Approximate expressions for X-ray resonant and M\"ossbauer reflectivity in the total external reflection region are developed for the limiting cases of a semiinfinite mirror with a small resonant addition to the total susceptibility and for the case of an ultrathin resonant layer. It is shown that in this region the reflectivity can really show the linear dependence on the imaginary part of resonant susceptibility, therefore in these cases the consideration of reflectivity spectra (R-spectra) as absorption resonant spectra, undertaken in several experimental works, can be justified. However, we have selected several effects producing essential dispersive distortions of the R-spectrum shape even for very small grazing angles. It has been shown that the dispersive corrections to the R-spectrum shape are mostly essential if the nonresonant absorption is relatively large. Model calculations demonstrate that the quantitative spectroscopic information extracted from R-spectra by using the software developed for absorption spectra can be inaccurate.
Emergence of Hierarchical Reference Systems in Multi-agent Communication

In natural language, referencing objects at different levels of specificity is a fundamental pragmatic mechanism for efficient communication in context. We develop a novel communication game, the hierarchical reference game, to study the emergence of such reference systems in artificial agents. We consider a simplified world, in which concepts are abstractions over a set of primitive attributes (e.g., color, style, shape). Depending on how many attributes are combined, concepts are more general (“circle”) or more specific (“red dotted circle”). Based on the context, the agents have to communicate at different levels of this hierarchy. Our results show that the agents learn to play the game successfully and can even generalize to novel concepts. To achieve abstraction, they use implicit (omitting irrelevant information) and explicit (indicating that attributes are irrelevant) strategies. In addition, the compositional structure underlying the concept hierarchy is reflected in the emergent protocols, indicating that the need to develop hierarchical reference systems supports the emergence of compositionality.
A Reinforcement Approach for Detecting P2P Botnet Communities in Dynamic Communication Graphs

Peer-to-peer (P2P) botnets use decentralized command and control networks that make them resilient to disruptions. The P2P botnet overlay networks manifest structures in mutual-contact graphs, also called communication graphs, formed using network traffic information. It has been shown that these structures can be detected using community detection techniques from graph theory. These previous works, however, treat the communication graphs and the P2P botnet structures as static. In reality, communication graphs are dynamic as they represent the continuously changing network traffic flows. Similarly, the P2P botnets also evolve with time, as new bots join and existing bots leave either temporarily or permanently. In this paper we address the problem of detecting such evolving P2P botnet communities in dynamic communication graphs. We propose a reinforcement-based approach, suitable for large communication graphs, that improves precision and recall of P2P botnet community detection in dynamic communication graphs.
Poisson equation on Wasserstein space and diffusion approximations for McKean-Vlasov equation

We consider the fully-coupled McKean-Vlasov equation with multi-time-scale potentials, and all the coefficients depend on the distributions of both the slow component and the fast motion. By studying the smoothness of the solution of the non-linear Poisson equation on Wasserstein space, we derive the asymptotic limit as well as the quantitative error estimate of the convergence for the slow process. Extra homogenized drift term containing derivative in the measure argument of the solution of the Poisson equation appears in the limit, which seems to be new and is unique for systems involving the fast distribution.
Privileged Attribution Constrained Deep Networks for Facial Expression Recognition

Facial Expression Recognition (FER) is crucial in many research domains because it enables machines to better understand human behaviours. FER methods face the problems of relatively small datasets and noisy data that don’t allow classical networks to generalize well. To alleviate these issues, we guide the model to concentrate on specific facial areas like the eyes, the mouth or the eyebrows, which we argue are decisive to recognise facial expressions. We propose the Privileged Attribution Loss (PAL), a method that directs the focus of the model towards the most salient facial regions by encouraging its attribution maps to correspond to a heatmap formed by facial landmarks. Furthermore, we introduce several channel strategies that allow the model to have more degrees of freedom. The proposed method is independent of the backbone architecture and doesn’t need additional semantic information at test time. Finally, experimental results show that the proposed PAL method outperforms current state-of-the-art methods on both RAF-DB and AffectNet.
Learning Dense Correspondence from Synthetic Environments

Estimation of human shape and pose from a single image is a challenging task. It is an even more difficult problem to map the identified human shape onto a 3D human model. Existing methods map manually labelled human pixels in real 2D images onto the 3D surface, which is prone to human error, and the sparsity of available annotated data often leads to sub-optimal results. We propose to solve the problem of data scarcity by training 2D-3D human mapping algorithms using automatically generated synthetic data for which exact and dense 2D-3D correspondence is known. Such a learning strategy using synthetic environments has a high generalisation potential towards real-world data. Using different camera parameter variations, background and lighting settings, we created precise ground truth data that constitutes a wider distribution. We evaluate the performance of models trained on synthetic using the Common Objects In Context (COCO) dataset and validation framework. Results show that training 2D-3D mapping network models on synthetic data is a viable alternative to using real data.
Integrality of Stickelberger elements and annihilation of natural Galois modules

To each Galois extension L/K of number ﬁelds with Galois group G and each integer r ≤ 0 one can associate Stickelberger elements in the centre of the rational group ring Q [ G ] in terms of values of Artin L -series at r . We show that the denominators of their coeﬃcients are bounded by the cardinality of the commutator subgroup G ′ of G whenever G is nilpotent. Moreover, we show that, after multiplication by | G ′ | and away from 2-primary parts, they annihilate the class group of L if r = 0 and higher Quillen K -groups of the ring of integers in L if r < 0. This generalizes recent progress on conjectures of Brumer and of Coates and Sinnott from abelian to nilpotent extensions. For arbitrary G we show that the denominators remain bounded along the cyclotomic Z p -tower of L for every odd prime p . This allows us to give an aﬃrmative answer to a question of Greenberg and of Gross on the behaviour of p -adic Artin L -series at zero.
Lang-n-Send: Processes That Send Languages

We present L ANG - N -S END , a π -calculus that is equipped with language deﬁnitions. Processes can deﬁne languages in operational semantics, and use them to execute programs. Furthermore, processes can send and receive pieces of operational semantics through channels. We present a reduction semantics for L ANG - N -S END , and we offer examples that demonstrate some of the scenarios that L ANG - N -S END captures.
An adjustable single degree-of-freedom system to guide natural walking movement for rehabilitation

Author(s): Tsuge, BY; McCarthy, JM | Abstract: © 2016 by ASME. This paper presents a linkage system designed to guide a natural ankle trajectory with the corresponding foot orientation. A six-bar linkage was designed to coordinate the joint angles of an RR chain (R denotes a revolute or hinged joint) that models the leg to achieve the desired ankle trajectory. The design is shown to be adjustable to meet a range of trajectories obtained in an individual's normal gait. Control of the foot position is obtained using a cam-driven parallel chain that has the same input as the six-bar linkage. The design of the linkage was carried out using linkage synthesis theory and optimization methods. The result is a one degree-of-freedom system that guides a natural walking movement of the leg and foot. A solid model of the complete device is presented. The results of this research provide a procedure that focuses on the kinematics and mechanical design of a device named the UCI gait mechanism.
A Preliminary Research on Space Situational Awareness Based on Event Cameras

Event camera is a new type of sensor that is different from traditional cameras. Each pixel is triggered asynchronously by an event. The trigger event is the change of the brightness irradiated on the pixel. If the increment or decrement is higher than a certain threshold, the event is output. Compared with traditional cameras, event cameras have the advantages of high temporal resolution, low latency, high dynamic range, low bandwidth and low power consumption. We carried out a series of observation experiments in a simulated space lighting environment. The experimental results show that the event camera can give full play to the above advantages in space situational awareness. This article first introduces the basic principles of the event camera, then analyzes its advantages and disadvantages, then introduces the observation experiment and analyzes the experimental results, and finally, a workflow of space situational awareness based on event cameras is given.
Random Forest Regression for continuous affect using Facial Action Units

In this paper we describe our approach to the arousal and valence track of the 3rd Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW). We extracted facial features using OpenFace and used them to train a multiple output random forest regressor. Our approach performed comparable to the baseline approach.
Vision-Based Manipulators Need to Also See from Their Hands

We study how the choice of visual perspective affects learning and generalization in the context of physical manipulation from raw sensor observations. Compared with the more commonly used global third-person perspective, a hand-centric (eye-in-hand) perspective affords reduced observability, but we find that it consistently improves training efficiency and out-of-distribution generalization. These benefits hold across a variety of learning algorithms, experimental settings, and distribution shifts, and for both simulated and real robot apparatuses. However, this is only the case when hand-centric observability is sufficient; otherwise, including a third-person perspective is necessary for learning, but also harms out-of-distribution generalization. To mitigate this, we propose to regularize the third-person information stream via a variational information bottleneck. On six representative manipulation tasks with varying hand-centric observability adapted from the Meta-World benchmark, this results in a state-of-the-art reinforcement learning agent operating from both perspectives improving its out-of-distribution generalization on every task. While some practitioners have long put cameras in the hands of robots, our work systematically analyzes the benefits of doing so and provides simple and broadly applicable insights for improving end-to-end learned vision-based robotic manipulation.
Large-eddy simulations to define building-specific similarity relationships for natural ventilation flow rates

Abstract Natural ventilation can play an important role towards preventing the spread of airborne infections in indoor environments. However, quantifying natural ventilation flow rates is a challenging task due to significant variability in the boundary conditions that drive the flow. In the current study, we propose and validate an efficient strategy for using computational fluid dynamics to assess natural ventilation flow rates under variable conditions, considering the test case of a single-room home in a dense urban slum. The method characterizes the dimensionless ventilation rate as a function of the dimensionless ventilation Richardson number and the wind direction. First, the high-fidelity large-eddy simulation (LES) predictions are validated against full-scale ventilation rate measurements. Next, simulations with identical Richardson numbers, but varying dimensional wind speeds and temperatures, are compared to verify the proposed similarity relationship. Last, the functional form of the similarity relationship is determined based on 32 LES. Validation of the surrogate model against full-scale measurements demonstrates that the proposed strategy can efficiently inform accurate building-specific similarity relationships for natural ventilation flow rates in complex urban environments.
Wideband precision stabilization of the -18.6kV retarding voltage for the KATRIN spectrometer

The Karlsruhe Tritium Neutrino Experiment (KATRIN) measures the effective electron anti-neutrino mass with an unprecedented design sensitivity of 0.2 eV (90 % C.L.). In this experiment, the energy spectrum of beta electrons near the tritium decay endpoint is analyzed with a highly accurate spectrometer. To reach the KATRIN sensitivity target, the retarding voltage of this spectrometer must be stable to the ppm (1 × 10-6) level and well known on various time scales (μs up to months), for values around -18.6 kV. A custom-designed high-voltage regulation system mitigates the impact of interference sources in the absence of a closed electric shield around the large spectrometer vessel. In this article, we describe the regulation system and its integration into the KATRIN setup. Independent monitoring methods demonstrate a stability within 2 ppm, exceeding KATRIN's specifications.
Local accumulation times in a diffusion-trapping model of synaptic receptor dynamics

The lateral diﬀusion and trapping of neurotransmitter receptors within the postsynaptic membrane of a neuron plays a key role in determining synaptic strength and plasticity. Trapping is mediated by the reversible binding of receptors to scaﬀolding proteins (slots) within a synapse. In this paper we introduce a new method for analyzing the transient dynamics of synapses in a diﬀusion-trapping model of receptor traﬃcking. Given a population of spatially distributed synapses, each of which has a ﬁxed number of slots, we calculate the rate of relaxation to the steady-state distribution of bound slots (synaptic weights) in terms of a set of local accumulation times. Assuming that the rates of exocytosis and endocytosis are suﬃciently slow, we show that the steady-state synaptic weights are independent of each other (purely local). On the other hand, the local accumulation time of a given synapse depends on the number of slots and the spatial location of all the synapses, indicating a form of transient heterosynaptic plasticity. This suggests that local accumulation time mea-surements could provide useful information regarding the distribution of synaptic weights within a dendrite.
On the Birth of the Universe and Time

A theory of the initial state of the universe is proposed within the framework of the Euclidean quantum theory of gravity. The theory is based on a quantum representation in which the action functional is implemented as an operator on the space of wave functionals depending on the 4D space metric and matter fields. The initial construction object is the eigenvalue of the action operator in the area of the origin of the universe with the given values of the 3D metric and matter fields on the boundary. The wave function of the initial state is plotted as an exponential of this eigenvalue, after a Wick rotation in the complex plane of the radial variable of the Euclidean 4D space. An estimate of the initial radius of the universe is proposed.
Efficient Answering of Historical What-if Queries

We introduce historical what-if queries, a novel type of what-if analysis that determines the effect of a hypothetical change to the transactional history of a database. For example, "how would revenue be affected if we would have charged an additional $6 for shipping? " We develop efficient techniques for answering historical what-if queries, i.e., determining how a modified history affects the current database state. Our techniques are based on reenactment, a replay technique for transactional histories. We optimize this process using program and data slicing techniques that determine which updates and what data can be excluded from reenactment without affecting the result. Using an implementation of our techniques in Mahif (a Middleware for Answering Historical what-IF queries) we demonstrate their effectiveness experimentally.
MERLIN - Malware Evasion with Reinforcement LearnINg

In addition to signature-based and heuristics-based detection techniques, machine learning (ML) is widely used to generalize to new, never-before-seen malicious software (malware). However, it has been demonstrated that ML models can be fooled by tricking the classifier into returning the incorrect label. These studies, for instance, usually rely on a prediction score that is fragile to gradient-based attacks. In the context of a more realistic situation where an attacker has very little information about the outputs of a malware detection engine, modest evasion rates are achieved. In this paper, we propose a method using reinforcement learning with DQN and REINFORCE algorithms to challenge two state-of-the-art ML-based detection engines (MalConv \&EMBER) and a commercial AV classified by Gartner as a leader AV. Our method combines several actions, modifying a Windows portable execution (PE) file without breaking its functionalities. Our method also identifies which actions perform better and compiles a detailed vulnerability report to help mitigate the evasion. We demonstrate that REINFORCE achieves very good evasion rates even on a commercial AV with limited available information.
Fast Prediction of the Landing Point of Reentry Capsules Based on Horizontal Wind Effect Compensation

A fine reentry trajectory multi-body dynamics model of parachute-vehicle system is presented to study the significant effect of horizontal wind on landing of parachute-vehicle systems.A new reentry capsule landing point fast prediction method based on horizontal wind effect compensation is proposed.The method is tested with SZ-8 and SZ-9 reentry data and comparisons of the results with those of existing methods show that the accuracy of horizontal wind effect compensation is increased by 30%. Besides,the method helps to shorten the time of computation and narrow down the area of search and rescue.
Renormalization approach to the superconducting Kondo model

An approach to bound states based on unitary transformations of Hamiltonians is presented. The method is applied to study the interaction between electrons in a BCS s-wave superconductor and a quantum spin. It is shown that known results from the t-matrix method and numerical studies are reproduced by this new method. As a main advantage, the method can straightforwardly be extended to study the topological properties of combined bound states in chains of many magnetic impurities. It also provides a uniform picture of the interplay between the Yu-Shiba-Rusinov (YSR) bound states and the Kondo singlet state.
Learning Stable Models for Prediction and Control

In this article, we demonstrate the benefits of imposing stability on data-driven Koopman operators. The data-driven identification of stable Koopman operators (DISKO) is implemented using an algorithm [1] that computes the nearest stable matrix solution to a least-squares reconstruction error. As a first result, we derive a formula that describes the prediction error of Koopman representations for an arbitrary number of time steps, and which shows that stability constraints can improve the predictive accuracy over long horizons. As a second result, we determine formal conditions on basis functions of Koopman operators needed to satisfy the stability properties of an underlying nonlinear system. As a third result, we derive formal conditions for constructing Lyapunov functions for nonlinear systems out of stable data-driven Koopman operators, which we use to verify stabilizing control from data. Finally, we demonstrate the benefits of DISKO in prediction and control with simulations using a pendulum and a quadrotor and experiments with a pusher-slider system. The paper is complemented with a video: https://sites.google. com/view/learning-stable-koopman.
Effect of Whole-, Upper-, and Lower-Body High-Intensity Rowing Exercise on Skin Temperature Measured by Thermography

ABSTRACT Purpose: Despite the growing works analyzing exercise-induced thermoregulatory adjustments through thermography, the skin temperature (Tsk) response of the same muscle groups underwent to different exercise demands has not been investigated. This study analyzed the behavior of Tsk of the same muscle groups when exercised with different demands in rowing. Methods: Eighteen men underwent three performance tests on a rowing ergometer: whole-body 2,000 m test (RTWB), upper-body (RTUB), and lower-body (RTLB) tests. In each condition, thermograms were recorded before (pre), immediately after test (post), and at 10 (REC10), 20 (REC20), and 30 (REC30) minutes post-exercise recovery. Tsk was measured at the pectoral (control body region), upper back, quadriceps, brachial biceps, and forearm. Results: Pectoral-Tsk reduced comparably in response to all testing conditions (p < .05). Upper back-Tsk decreased post (p < .001) and returned to baseline in the RTUB (REC10, p = 1.0) and RTWB (REC30, p = .128), while remained reduced in the RTLB (p < .001). Quadriceps-Tsk reduced post (p < .05) and returned to baseline in the RTWB and RTLB at REC10 (p = 1.0), remaining reduced in the RTUB during recovery (p < .05). Regarding the upper limbs, Tsk increased more markedly in the RTUB versus RTWB during the recovery period (p < .05); in the RTLB, biceps-Tsk remained below baseline over time (p < .05), whereas the forearm-Tsk was restored at REC10 (p = 1.0). Conclusion: Manipulating the muscle groups involved in rowing alters the Tsk response within equal ROI. Exercise-induced Tsk changes can reflect local hemodynamic and thermoregulatory adjustments.