Miracle-dz/UniFAR-FTUs huggingface dataset:
Enabling and Accelerating Dynamic Vision Transformer Inference for Real-Time Applications

Many state-of-the-art deep learning models for computer vision tasks are based on the transformer architecture. Such models can be computationally expensive and are typically statically set to meet the deployment scenario. However, in real-time applications, the resources available for every inference can vary considerably and be smaller than what state-of-the-art models require. We can use dynamic models to adapt the model execution to meet real-time application resource constraints. While prior dynamic work primarily minimized resource utilization for less complex input images, we adapt vision transformers to meet system dynamic resource constraints, independent of the input image. We find that unlike early transformer models, recent state-of-the-art vision transformers heavily rely on convolution layers. We show that pretrained models are fairly resilient to skipping computation in the convolution and self-attention layers, enabling us to create a low-overhead system for dynamic real-time inference without extra training. Finally, we explore compute organization and memory sizes to find settings to efficiency execute dynamic vision transformers. We find that wider vector sizes produce a better energy-accuracy tradeoff across dynamic configurations despite limiting the granularity of dynamic execution, but scaling accelerator resources for larger models does not significantly improve the latency-area-energy-tradeoffs. Our accelerator saves 20% of execution time and 30% of energy with a 4% drop in accuracy with pretrained SegFormer B2 model in our dynamic inference approach and 57% of execution time for the ResNet-50 backbone with a 4.5% drop in accuracy with the Once-For-All approach.
Enhancing Data-Awareness of Object-Centric Event Logs

When multiple objects are involved in a process, there is an opportunity for processes to be discovered from different angles with new information that previously might not have been analyzed from a single object point of view. This does require that all the information of event/object attributes and their values are stored within logs including attributes that have a list of values or attributes with values that change over time. It also requires that attributes can unambiguously be linked to an object, an event or both. As such, object-centric event logs are an interesting development in process mining as they support the presence of multiple types of objects. First, this paper shows that the current object-centric event log formats do not support the aforementioned aspects to their full potential since the possibility to support dynamic object attributes (attributes with changing values) is not supported by existing formats. Next, this paper introduces a novel enriched object-centric event log format tackling the aforementioned issues alongside an algorithm that automatically translates XES logs to this Data-aware OCEL (DOCEL) format.
An internal velocity study of the Rosette Nebula

Emission-line profiles of H-alpha were studied at about 700 points in the Rosette Nebula by using a multislit echelle spectrograph. Numerical analysis of the profiles indicates that variations in the line-of-sight velocity occur within the nebula, reaching about + or - 20 km/s in the inner regions. Evidence is presented that these highest velocities are more probably inward, favoring a model where the central cavity is due to a depletion resulting from rapid star formation
Domain-general Crowd Counting in Unseen Scenarios

Domain shift across crowd data severely hinders crowd counting models to generalize to unseen scenarios. Although domain adaptive crowd counting approaches close this gap to a certain extent, they are still dependent on the target domain data to adapt (e.g. finetune) their models to the specific domain. In this paper, we instead target to train a model based on a single source domain which can generalize well on any unseen domain. This falls into the realm of domain generalization that remains unexplored in crowd counting. We first introduce a dynamic sub-domain division scheme which divides the source domain into multiple sub-domains such that we can initiate a meta-learning framework for domain generalization. The sub-domain division is dynamically refined during the meta-learning. Next, in order to disentangle domain-invariant information from domain-specific information in image features, we design the domain-invariant and -specific crowd memory modules to re-encode image features. Two types of losses, i.e. feature reconstruction and orthogonal losses, are devised to enable this disentanglement. Extensive experiments on several standard crowd counting benchmarks i.e. SHA, SHB, QNRF, and NWPU, show the strong generalizability of our method. Our code is available at: https://github.com/ZPDu/Domain-general-Crowd-Counting-in-Unseen-Scenarios
Cooperative Guidance Strategy for Active Defense Spacecraft with Imperfect Information via Deep Reinforcement Learning

In this paper, an adaptive cooperative guidance strategy for the active protection of a target spacecraft trying to evade an interceptor was developed. The target spacecraft performs evasive maneuvers, launching an active defense vehicle to divert the interceptor. Instead of classical strategies, which are based on optimal control or differential game theory, the problem was solved by using the deep reinforcement learning method, and imperfect information was assumed for the interceptor maneuverability. To address the sparse reward problem, a universal reward design method and an increasingly difficult training approach were presented utilizing the shaping technique. Guidance law, reward function, and training approach were demonstrated through the learning process and Monte Carlo simulations. The application of the non-sparse reward function and increasingly difficult training approach accelerated the model convergence, alleviating the overfitting problem. Considering a standard optimal guidance law as a benchmark, the effectiveness, and the advantages, that guarantee the target spacecraft's escape and win rates in a multi-agent game, of the proposed guidance strategy were validated by the simulation results. The trained agent's adaptiveness to the interceptor maneuverability was superior to the optimal guidance law. Moreover, compared to the standard optimal guidance law, the proposed guidance strategy performed better with less prior knowledge.
A Time Series Approach to Explainability for Neural Nets with Applications to Risk-Management and Fraud Detection

Artificial intelligence is creating one of the biggest revolution across technology driven application fields. For the finance sector, it offers many opportunities for significant market innovation and yet broad adoption of AI systems heavily relies on our trust in their outputs. Trust in technology is enabled by understanding the rationale behind the predictions made. To this end, the concept of eXplainable AI emerged introducing a suite of techniques attempting to explain to users how complex models arrived at a certain decision. For cross-sectional data classical XAI approaches can lead to valuable insights about the models' inner workings, but these techniques generally cannot cope well with longitudinal data (time series) in the presence of dependence structure and non-stationarity. We here propose a novel XAI technique for deep learning methods which preserves and exploits the natural time ordering of the data.
Pixel2ISDF: Implicit Signed Distance Fields based Human Body Model from Multi-view and Multi-pose Images

In this report, we focus on reconstructing clothed humans in the canonical space given multiple views and poses of a human as the input. To achieve this, we utilize the geometric prior of the SMPLX model in the canonical space to learn the implicit representation for geometry reconstruction. Based on the observation that the topology between the posed mesh and the mesh in the canonical space are consistent, we propose to learn latent codes on the posed mesh by leveraging multiple input images and then assign the latent codes to the mesh in the canonical space. Specifically, we first leverage normal and geometry networks to extract the feature vector for each vertex on the SMPLX mesh. Normal maps are adopted for better generalization to unseen images compared to 2D images. Then, features for each vertex on the posed mesh from multiple images are integrated by MLPs. The integrated features acting as the latent code are anchored to the SMPLX mesh in the canonical space. Finally, latent code for each 3D point is extracted and utilized to calculate the SDF. Our work for reconstructing the human shape on canonical pose achieves 3rd performance on WCPA MVP-Human Body Challenge.
Learning Sampling Distributions for Model Predictive Control

Sampling-based methods have become a cornerstone of contemporary approaches to Model Predictive Control (MPC), as they make no restrictions on the differentiability of the dynamics or cost function and are straightforward to parallelize. However, their efficacy is highly dependent on the quality of the sampling distribution itself, which is often assumed to be simple, like a Gaussian. This restriction can result in samples which are far from optimal, leading to poor performance. Recent work has explored improving the performance of MPC by sampling in a learned latent space of controls. However, these methods ultimately perform all MPC parameter updates and warm-starting between time steps in the control space. This requires us to rely on a number of heuristics for generating samples and updating the distribution and may lead to sub-optimal performance. Instead, we propose to carry out all operations in the latent space, allowing us to take full advantage of the learned distribution. Specifically, we frame the learning problem as bi-level optimization and show how to train the controller with backpropagation-through-time. By using a normalizing flow parameterization of the distribution, we can leverage its tractable density to avoid requiring differentiability of the dynamics and cost function. Finally, we evaluate the proposed approach on simulated robotics tasks and demonstrate its ability to surpass the performance of prior methods and scale better with a reduced number of samples.
Solving Rearrangement Puzzles Using Path Defragmentation in Factored State Spaces

Rearrangement puzzles are variations of rearrangement problems in which the elements of a problem are potentially logically linked together. To efficiently solve such puzzles, we develop a motion planning approach based on a new state space that is logically factored, integrating the capabilities of the robot through factors of simultaneously manipulatable joints of an object. Based on this factored state space, we propose less-actions RRT (LA-RRT), a planner which optimizes for a low number of actions to solve a puzzle. At the core of our approach lies a new path defragmentation method, which rearranges and optimizes consecutive edges to minimize action cost. We solve six rearrangement scenarios with a Fetch robot, involving planar table puzzles and an escape room scenario. LA-RRT significantly outperforms the next best asymptotically-optimal planner by 4.01 to 6.58 times improvement in final action cost.
Q-Pensieve: Boosting Sample Efficiency of Multi-Objective RL Through Memory Sharing of Q-Snapshots

Many real-world continuous control problems are in the dilemma of weighing the pros and cons, multi-objective reinforcement learning (MORL) serves as a generic framework of learning control policies for different preferences over objectives. However, the existing MORL methods either rely on multiple passes of explicit search for finding the Pareto front and therefore are not sample-efficient, or utilizes a shared policy network for coarse knowledge sharing among policies. To boost the sample efficiency of MORL, we propose Q-Pensieve, a policy improvement scheme that stores a collection of Q-snapshots to jointly determine the policy update direction and thereby enables data sharing at the policy level. We show that Q-Pensieve can be naturally integrated with soft policy iteration with convergence guarantee. To substantiate this concept, we propose the technique of Q replay buffer, which stores the learned Q-networks from the past iterations, and arrive at a practical actor-critic implementation. Through extensive experiments and an ablation study, we demonstrate that with much fewer samples, the proposed algorithm can outperform the benchmark MORL methods on a variety of MORL benchmark tasks.
ISAACS: Iterative Soft Adversarial Actor-Critic for Safety

The deployment of robots in uncontrolled environments requires them to operate robustly under previously unseen scenarios, like irregular terrain and wind conditions. Unfortunately, while rigorous safety frameworks from robust optimal control theory scale poorly to high-dimensional nonlinear dynamics, control policies computed by more tractable"deep"methods lack guarantees and tend to exhibit little robustness to uncertain operating conditions. This work introduces a novel approach enabling scalable synthesis of robust safety-preserving controllers for robotic systems with general nonlinear dynamics subject to bounded modeling error by combining game-theoretic safety analysis with adversarial reinforcement learning in simulation. Following a soft actor-critic scheme, a safety-seeking fallback policy is co-trained with an adversarial"disturbance"agent that aims to invoke the worst-case realization of model error and training-to-deployment discrepancy allowed by the designer's uncertainty. While the learned control policy does not intrinsically guarantee safety, it is used to construct a real-time safety filter (or shield) with robust safety guarantees based on forward reachability rollouts. This shield can be used in conjunction with a safety-agnostic control policy, precluding any task-driven actions that could result in loss of safety. We evaluate our learning-based safety approach in a 5D race car simulator, compare the learned safety policy to the numerically obtained optimal solution, and empirically validate the robust safety guarantee of our proposed safety shield against worst-case model discrepancy.
FastIoT -- A framework and holistic approach for rapid development of IIoT systems

While lots of research has been conducted on the architecture of Industrial Internet of Things (IIoT) systems, concepts of structuring their development processes are missing. Therefore, we propose a holistic approach supporting organizations in rapid development of IIoT systems. It includes the structuring of the development process into multiple projects sharing project conventions. Utilizing a single configurable build script for all projects, our goal is to make the integration of code from various projects into broader IIoT systems easy and the systems decomposable with minimal effort.
On the Dual Interpretation of Nouns as Types and Predicates in Semantic Type Theories

In order to mediate the debate on whether common nouns are better interpreted as types or as predicates in type-theoretical semantic frameworks, the present paper shows that type theories whose models in category theory are toposes have access to a property draw-ing a one-to-one correspondence between ﬁrst-order predicates and base types, thus enabling more ﬂexibility for common noun interpretation. Using this ﬂexibility and linguistic arguments based on negative predications, a subse-quent proposal is made to interpret nouns as predicates with reﬁned argument types.
DATA DISCRETIZATION FOR THE TRANSFER ENTROPY IN FINANCIAL MARKET

Recently, an information theoretic inspired concept of transfer entropy has been introduced by Schreiber. It aims to quantify in a nonparametric and explicitly nonsymmetric way the flow of information between two time series. This model-free based on Shannon entropy approach in principle allows us to detect statistical dependencies of all types, i.e., linear and nonlinear temporal correlations. However, we always analyze the transfer entropy based on the data, which is discretized into three partitions by some coarse graining. Naturally, we are interested in investigating the effect of the data discretization of the two series on the transfer entropy. In our paper, we analyze the results based on the data which are generated by the linear modeling and the ARFIMA modeling, as well as the dataset consists of seven indices during the period 1992–2002. The results show that the higher the degree of data discretization get, the larger the value of the transfer entropy will be, besides, the direction of the information flow is unchanged along with the degree of data discretization.
Diffractional Scattering of Fast Deuterons by Nuclei

The cross section for elastic scattering and the cross section for diffraction splitting of fast deuterons by completely black nuclei are determfned. The energy distribution of the disintegration products is found. The cross section for splitting of a fast deuteron by a completely black nucleus is calculated, taking the diffraction and Coulomb interactions into account. Expressions are obtained for the cross sections for elastic deuteron scatiering and splitting, taking into account the semi-transparency of the nucleus. (auth)
Does Economic Policy Uncertainty Affect Green Bond Markets? Evidence from Wavelet-Based Quantile Analysis

ABSTRACT This paper investigates the wavelet-based quantile dependence between Economic Policy Uncertainty (EPU) and green bond markets over 2014–2021. We first determine how the connectivity between EPU and green bonds differs across different investment horizons by decomposing EPU and green bond series into various frequency bands. Next, we provide a quantile-based framework to characterize the reliance between EPU and green bond markets across various market circumstances. Our findings show that the Granger causality from EPU to the green bond market is non-linear and varies across time scales. Our results benefit policymakers with a policy design to mitigate systematic volatility caused by external shocks in the green bond markets.
Analysis of Variance

This chapter discusses the analysis of variance and presents the introduction of a procedure for testing the equality of two or more means. The two statistical designs, the completely randomized and the randomized block designs, are considered. The comparison of two or more means is based on partitioning the variation in the dependent variable into its components, and hence the method is called the analysis of variance (ANOVA). It was introduced by Sir Ronald A. Fisher and has been used in many fields of research. The chapter also discusses the assumptions made when the ANOVA is used. It also presents an analysis of the randomized block design, an example of a two-way ANOVA, and a two-way ANOVA with interaction. The chapter further presents a linear model representation of the ANOVA.
DFT mechanistic study of Pt Sub-monolayer adsorption on BiAlO3 (0001) polar surfaces

Abstract The adsorption mechanism of Pt sub-monolayer on the negative and positive BiAlO3 (0001) polar surfaces has been established by the first-principles density functional theory calculations. The detailed analysis of adsorption sites and electronic properties revealed that the Pt sub-monolayer on the oppositely polarized BiAlO3 (0001) surfaces exhibits different adsorption features. Also, the results of this work demonstrate that the different coverage rates of Pt adatoms deposited on BiAlO3 (0001) negative and positive polar surfaces result to different adsorption behaviors. These results imply that the catalytic activities of the Pt/BiAlO3 (0001) systems should depend on the surface and degree of loading.
The effect of mole fraction on the phase transition of BaxSr1-xTiO3 ceramic from cubic to tetragonal assisted by Rietveld analysis

Abstract The displacive phase transition in BaxSr1-xTiO3 (BST) ceramic due to the mole fraction differences was investigated using x-ray diffraction assisted by Rietveld analysis. Detailed Rietveld analysis showed that BST undergoes a structural phase transformation from cubic to tetragonal phase. Average crystallite size and microstrain present in the powders were studied using the Wilson and Debye–Scherrer equation. The crystal structure of samples was refined by the Rietveld method, and an expansion on the unit cell parameters was determined. These analyses revealed an increment in the unit cell strain and an increase in the crystallite size when the mole fraction was increased.
Improvement microwave dielectric properties of Ba2MgWO6 ceramics by substituting Mg2+ with Zn2+

Abstract The microwave dielectric properties of Ba2Mg1– x Zn x WO6 ceramics were examined with a view to their exploitation for mobile communication. The Ba2Mg1– x Zn x WO6 ceramics were prepared by the conventional solid-state method. The X-ray diffraction patterns of the Ba2Mg1– x Zn x WO6 ceramics revealed that Ba2MgWO6 is the main crystalline phase, which is accompanied by small amounts of BaWO4 and Ba2WO5 as secondary phases. A maximum density of 6.72 g/cm3 was obtained for Ba2Mg0.95Zn0. 05WO6 ceramic, sintered at 1650 °C for 4 h. Dielectric constant ( ) of 17.9, quality factor (Q f) of 157,000 GHz, and temperature coefficient of resonant frequency ( ) of −35 ppm/°C were obtained for Ba2Mg0.95Zn0. 05WO6 ceramics that were sintered at 1650 °C for 4 h.
On approximation of the solutions of delay differential equations by using piecewise constant arguments

By using the Gronwall Bellman inequality we prove some limit relations between the solutions of delay differential equations with continuous arguments and the solutions of some related delay differential equations with piecewise constant arguments(EPCA). EPCA are strongly related to some discrete difference equations arising in numerical analysis, therefore the results can be used to compute numerical solutions of delay differential equations. We also consider the delay differential equations of neutral type by applying a generalization of the Gronwall Bellman inequality.
Speech, spatial, and qualities of hearing scale (SSQ): Normative data from young, normal-hearing listeners

The Speech, Spatial, and Qualities of Hearing Scale (SSQ) was developed to assess listeners' subjective sense of listening ability and listening experience in everyday complex situations that often involve spatial hearing. The SSQ is one of the very few instruments designed to measure such quantities, and has been used extensively to assess functional hearing impairment and benefit resulting from hearing remediation strategy. Although a recent study examined the psychometric properties of the SSQ with a large sample of hearing-impaired listeners, little published data exist from normal-hearing listeners. The data that have been published suggest that even young normal-hearing listeners do not rate their subjective listening abilities at the most-proficient end of the scale in all the listening situations probed by the SSQ. The goal of this study was to examine this issue more fully, using a sample of 233 young (median age 21.2 years, 3.1-year interquartile range), normal-hearing listeners (pure tone thres.