Miracle-dz/UniFAR-FTUs huggingface dataset:
Uncertainty Guided Depth Fusion for Spike Camera

Depth estimation is essential for various important real-world applications such as autonomous driving. However, it suffers from severe performance degradation in high-velocity scenario since traditional cameras can only capture blurred images. To deal with this problem, the spike camera is designed to capture the pixel-wise luminance intensity at high frame rate. However, depth estimation with spike camera remains very challenging using traditional monocular or stereo depth estimation algorithms, which are based on the photometric consistency. In this paper, we propose a novel Uncertainty-Guided Depth Fusion (UGDF) framework to fuse the predictions of monocular and stereo depth estimation networks for spike camera. Our framework is motivated by the fact that stereo spike depth estimation achieves better results at close range while monocular spike depth estimation obtains better results at long range. Therefore, we introduce a dual-task depth estimation architecture with a joint training strategy and estimate the distributed uncertainty to fuse the monocular and stereo results. In order to demonstrate the advantage of spike depth estimation over traditional camera depth estimation, we contribute a spike-depth dataset named CitySpike20K, which contains 20K paired samples, for spike depth estimation. UGDF achieves state-of-the-art results on CitySpike20K, surpassing all monocular or stereo spike depth estimation baselines. We conduct extensive experiments to evaluate the effectiveness and generalization of our method on CitySpike20K. To the best of our knowledge, our framework is the first dual-task fusion framework for spike camera depth estimation. Code and dataset will be released.
BITS: Bi-level Imitation for Traffic Simulation

Simulation is the key to scaling up validation and verification for robotic systems such as autonomous vehicles. Despite advances in high-fidelity physics and sensor simulation, a critical gap remains in simulating realistic behaviors of road users. This is because devising first principle models for human-like behaviors is generally infeasible. In this work, we take a data-driven approach to generate traffic behaviors from real-world driving logs. The method achieves high sample efficiency and behavior diversity by exploiting the bi-level hierarchy of high-level intent inference and low-level driving behavior imitation. The method also incorporates a planning module to obtain stable long-horizon behaviors. We empirically validate our method with scenarios from two large-scale driving datasets and show our method achieves balanced traffic simulation performance in realism, diversity, and long-horizon stability. We also explore ways to evaluate behavior realism and introduce a suite of evaluation metrics for traffic simulation. Finally, as part of our core contributions, we develop and open source a software tool that unifies data formats across different driving datasets and converts scenes from existing datasets into interactive simulation environments. For video results and code release, see https://bit.ly/3L9uzj3.
LUCID: Exposing Algorithmic Bias through Inverse Design

AI systems can create, propagate, support, and automate bias in decision-making processes. To mitigate biased decisions, we both need to understand the origin of the bias and define what it means for an algorithm to make fair decisions. Most group fairness notions assess a model's equality of outcome by computing statistical metrics on the outputs. We argue that these output metrics encounter intrinsic obstacles and present a complementary approach that aligns with the increasing focus on equality of treatment. By Locating Unfairness through Canonical Inverse Design (LUCID), we generate a canonical set that shows the desired inputs for a model given a preferred output. The canonical set reveals the model's internal logic and exposes potential unethical biases by repeatedly interrogating the decision-making process. We evaluate LUCID on the UCI Adult and COMPAS data sets and find that some biases detected by a canonical set differ from those of output metrics. The results show that by shifting the focus towards equality of treatment and looking into the algorithm's internal workings, the canonical sets are a valuable addition to the toolbox of algorithmic fairness evaluation.
Normative Ethics Principles for Responsible AI Systems: Taxonomy and Future Directions

Responsible AI must be able to make decisions that consider human values and can be justified by human morals. Operationalising normative ethical principles inferred from philosophy supports responsible reasoning. We survey computer science literature and develop a taxonomy of 23 normative ethical principles which can be operationalised in AI. We describe how each principle has previously been operationalised, highlighting key themes that AI practitioners seeking to implement ethical principles should be aware of. We envision that this taxonomy will facilitate the development of methodologies to incorporate normative ethical principles in responsible AI systems.
Design and Analysis of Vortex Generator

To save energy and to protect the global environment, fuel consumption reduction is primary concern of automotive development. In vehicle body development, reduction of drag is essential for improving fuel consumption and driving performance, and if an aerodynamically refined body is also aesthetically attractive, it will contribute much to increase the vehicle’s appeal to potential customers. However, as the passenger car must have enough capacity to accommodate passengers and baggage in addition to minimum necessary space for its engine and other components, it is extremely difficult to realize an aerodynamically ideal body shape. The car is therefore obliged to have a body shape that is rather aerodynamically bluff, not an ideal streamline shape. One way to overcome drag is to generate vortex flow at the rare end of the vehicle using vortex generators, reduction of drag in automobiles with vortex generators is already proved working in some previous studies, but no standard geometry is specified for the vortex generator. In this project we are going to compare the performance of different geometry’s of vortex generators using Ansys 15.0 fluent workbench, all the 3d models of vortex generators are developed in Catia v5 software.
Tinjauan atas Efektivitas Penggunaan Key Opinion Leader (KOL) dalam Penjualan Surat Utang Negara Ritel seri SBR011

Indonesian Ministry of Finance had endorsed 10 Key Opinion Leaders to help promoting government retail bonds SBR011 during selling period of 25 May-16 June 2022. This study analyzed eﬀectiveness of the endorsement by using several indicators; engagement rate, enthusiasm rate and sentiment analysis of feedbacks from KOL audiens. Data was gathered from social media Instagram and TikTok social platform used by the KOL to post their marketing contents. This paper found that the endorsement is quite eﬀective to promote the SBR011 and yields mostly positive feedback on the marketing campaign.
Global existence for quasilinear wave equations satisfying the null condition

We explore the global existence of solutions to systems of quasilinear wave equations satisfying the null condition when the initial data are suﬃciently small. We adapt an approach of Keel, Smith, and Sogge, which relies on integrated local energy estimates and a weighted Sobolev estimate that yields decay in | x | , by using the r p -weighted local energy estimates of Dafermos and Rodnianski. One advantage of this approach is that all time-dependent vector ﬁelds can be avoided and the proof can be readily adapted to address wave equations exterior to star-shaped obstacles.
Lib-SibGMU - A University Library Circulation Dataset for Recommender Systems Developmen

We opensource under CC BY 4.0 license Lib-SibGMU - a university library circulation dataset - for a wide research community, and benchmark major algorithms for recommender systems on this dataset. For a recommender architecture that consists of a vectorizer that turns the history of the books borrowed into a vector, and a neighborhood-based recommender, trained separately, we show that using the fastText model as a vectorizer delivers competitive results.
Splitting Method for Support Vector Machine with Lower Semi-continuous Loss

: In this paper, we study the splitting method based on alternating direction method of multipliers for support vector machine in reproducing kernel Hilbert space with lower semi-continuous loss function. If the loss function is lower semi-continuous and subanalytic, we use the Kurdyka-Lojasiewicz inequality to show that the iterative sequence induced by the splitting method globally converges to a stationary point. The numerical experiments also demonstrate the eﬀectiveness of the splitting method.
SVD-GCN: A Simplified Graph Convolution Paradigm for Recommendation

With the tremendous success of Graph Convolutional Networks (GCNs), they have been widely applied to recommender systems and have shown promising performance. However, most GCN-based methods rigorously stick to a common GCN learning paradigm and suffer from two limitations: (1) the limited scalability due to the high computational cost and slow training convergence; (2) the notorious over-smoothing issue which reduces performance as stacking graph convolution layers. We argue that the above limitations are due to the lack of a deep understanding of GCN-based methods. To this end, we first investigate what design makes GCN effective for recommendation. By simplifying LightGCN, we show the close connection between GCN-based and low-rank methods such as Singular Value Decomposition (SVD) and Matrix Factorization (MF), where stacking graph convolution layers is to learn a low-rank representation by emphasizing (suppressing) components with larger (smaller) singular values. Based on this observation, we replace the core design of GCN-based methods with a flexible truncated SVD and propose a simplified GCN learning paradigm dubbed SVD-GCN, which only exploits K-largest singular vectors for recommendation. To alleviate the over-smoothing issue, we propose a renormalization trick to adjust the singular value gap, resulting in significant improvement. Extensive experiments on three real-world datasets show that our proposed SVD-GCN not only significantly outperforms state-of-the-arts but also achieves over 100x and 10x speedups over LightGCN and MF, respectively.
A machine learning approach to predict the structural and magnetic properties of Heusler alloy families

Random forest (RF) regression model is used to predict the lattice constant, magnetic moment and formation energies of full Heusler alloys, half Heusler alloys, inverse Heusler alloys and quaternary Heusler alloys based on existing as well as indigenously prepared databases. Prior analysis was carried out to check the distribution of the data points of the response variables and found that in most of the cases, the data is not normally distributed. The outcome of the RF model performance is sufficiently accurate to predict the response variables on the test data and also shows its robustness against overfitting, outliers, multicollinearity and distribution of data points. The parity plots between the machine learning predicted values against the computed values using density functional theory (DFT) shows linear behavior with adjusted R2 values lying in the range of 0.80 to 0.94 for all the predicted properties for different types of Heusler alloys. Feature importance analysis shows that the valence electron numbers plays an important feature role in the prediction for most of the predicted outcomes. Case studies with one full Heusler alloy and one quaternary Heusler alloy were also mentioned comparing the machine learning predicted results with our earlier theoretical calculated values and experimentally measured results, suggesting high accuracy of the model predicted results.
Learning energy-efficient driving behaviors by imitating experts

The rise of vehicle automation has generated significant interest in the potential role of future automated vehicles (AVs). In particular, in highly dense traffic settings, AVs are expected to serve as congestion-dampeners, mitigating the presence of instabilities that arise from various sources. However, in many applications, such maneuvers rely heavily on non-local sensing or coordination by interacting AVs, thereby rendering their adaptation to real-world settings a particularly difficult challenge. To address this challenge, this paper examines the role of imitation learning in bridging the gap between such control strategies and realistic limitations in communication and sensing. Treating one such controller as an “expert”, we demonstrate that imitation learning can succeed in deriving policies that, if adopted by 5% of vehicles, may boost the energy-efficiency of networks with varying traffic conditions by 15% using only local observations. Results and code are available online at https://sites.google. com/view/il-traffic/home.
Zero-Inflated Poisson Cluster-Weighted Models: Properties and Applications

In this paper, I propose a new class of Zero-Inﬂated Poisson models into the family of Cluster Weighted Models (CWMs) called Zero-Inﬂated Poisson CWMs (ZIPCWM). ZIPCWM extends Poisson cluster weighted models and other mixture models. I propose an Expectation-Maximization (EM) algorithm via an iteratively reweighted least squares for the model. I theoretically and ana-lytically investigate the identiﬁability of the proposed model through an extensive simulation study. Parameter recovery, classiﬁcation assessment, and performance of diﬀerent information criteria are investigated through broad simulation design. ZIPCWM is applied to real data which accounts for excess zeros of over 40%. We explore the classiﬁcation performance of ZIPCWM, Fixed Zero-inﬂated Poisson mixture model (FZIP), and Poisson cluster weighted model (PCWM) on the data. Based on the confusion matrix, ZIPCWM achieves 97 . 4% classiﬁcation power, PCWM achieves 67 . 30%, while FZIP has the worst classiﬁcation performance. In conclusion, ZIPCWM outperforms both PCWM and FZIP models. while
Play with Emotion: Affect-Driven Reinforcement Learning

This paper introduces a paradigm shift by viewing the task of affect modeling as a reinforcement learning (RL) process. According to the proposed paradigm, RL agents learn a policy (i.e. affective interaction) by attempting to maximize a set of rewards (i.e. behavioral and affective patterns) via their experience with their environment (i.e. context). Our hypothesis is that RL is an effective paradigm for interweaving affect elicitation and manifestation with behavioral and affective demonstrations. Importantly, our second hypothesis-building on Damasio's so-matic marker hypothesis-is that emotion can be the facilitator of decision-making. We test our hypotheses in a racing game by training Go-Blend agents to model human demonstrations of arousal and behavior; Go-Blend is a modified version of the Go-Explore algorithm which has recently showcased supreme performance in hard exploration tasks. We first vary the arousal-based reward function and observe agents that can effectively display a palette of affect and behavioral patterns according to the specified reward. Then we use arousal-based state selection mechanisms in order to bias the strategies that Go-Blend explores. Our findings suggest that Go-Blend not only is an efficient affect modeling paradigm but, more importantly, affect-driven RL improves exploration and yields higher performing agents, validating Damasio's hypothesis in the domain of games.
Algebraic Methods in Statistics and Probability II

This volume is based on lectures presented at the AMS Special Session on Algebraic Methods in Statistics and Probability--held March 27-29, 2009, at the University of Illinois at Urbana-Champaign--and on contributed articles solicited for this volume. A decade after the publication of Contemporary Mathematics Vol. 287, the present volume demonstrates the consolidation of important areas, such as algebraic statistics, computational commutative algebra, and deeper aspects of graphical models. In statistics, this volume includes, among others, new results and applications in cubic regression models for mixture experiments, multidimensional Fourier regression experiments, polynomial characterizations of weakly invariant designs, toric and mixture models for the diagonal-effect in two-way contingency tables, topological methods for multivariate statistics, structural results for the Dirichlet distributions, inequalities for partial regression coefficients, graphical models for binary random variables, conditional independence and its relation to sub-determinants covariance matrices, connectivity of binary tables, kernel smoothing methods for partially ranked data, Fourier analysis over the dihedral groups, properties of square non-symmetric matrices, and Wishart distributions over symmetric cones. In probability, this volume includes new results related to discrete-time semi Markov processes, weak convergence of convolution products in semigroups, Markov bases for directed random graph models, functional analysis in Hardy spaces, and the Hewitt-Savage zero-one law.
Differentially Private Triangle and 4-Cycle Counting in the Shuffle Model

Subgraph counting is fundamental for analyzing connection patterns or clustering tendencies in graph data. Recent studies have applied LDP (Local Differential Privacy) to subgraph counting to protect user privacy even against a data collector in social networks. However, existing local algorithms suffer from extremely large estimation errors or assume multi-round interaction between users and the data collector, which requires a lot of user effort and synchronization. In this paper, we focus on a one-round of interaction and propose accurate subgraph counting algorithms by introducing a recently studied shuffle model. We first propose a basic technique called wedge shuffling to send wedge information, the main component of several subgraphs, with small noise. Then we apply our wedge shuffling to counting triangles and 4-cycles -- basic subgraphs for analyzing clustering tendencies -- with several additional techniques. We also show upper bounds on the estimation error for each algorithm. We show through comprehensive experiments that our one-round shuffle algorithms significantly outperform the one-round local algorithms in terms of accuracy and achieve small estimation errors with a reasonable privacy budget, e.g., smaller than 1 in edge DP.
Ischaemic preconditioning blunts exercise‐induced mitochondrial dysfunction, speeds oxygen uptake kinetics but does not alter severe‐intensity exercise capacity

What is the central question of this study? Ischaemic preconditioning is a novel pre‐exercise priming strategy. We asked whether ischaemic preconditioning would alter mitochondrial respiratory function and pulmonary oxygen uptake kinetics and improve severe‐intensity exercise performance. What is the main finding and its importance? Ischaemic preconditioning expedited overall pulmonary oxygen uptake kinetics and appeared to prevent an increase in leak respiration, proportional to maximal electron transfer system and ADP‐stimulated respiration, that was evoked by severe‐intensity exercise in sham‐control conditions. However, severe‐intensity exercise performance was not improved. The results do not support ischaemic preconditioning as a pre‐exercise strategy to improve exercise performance in recreationally active participants.
Epidemiology of the HPV in Greece and other Countries of the World

Cervical cancer is the fourth most common type of cancer among women worldwide. It is estimated that more than half a million women are infected by an HPV genotype. The purpose of the epidemiological review is to determine the prevalence of human papillomavirus (HPV) infection and the distribution of its genotypes among various countries worldwide, as well as in Greece and to record the characteristics of the subtype incidence. We recorded published studies concerning epidemiological data of the prevalence of the HPV subtypes, according to the geographical location and HPV detection rates for both healthy and infected women. The studies were heterogeneous with regard to sample selection. The prevalence of any subtype of the HPV is about 11%. HPV16 is the most common genotype reported in most countries. The substantial differences in the prevalence and distribution of the virus among the various geographical sites, deserve further analysis in global epidemiological studies, including populations with common characteristics.
Associations among body size, body image perceptions, and weight loss attempts among African American, youth: A test of a Mediational model

: ObjectiveLittle is known about influences on weight loss attempts, yet about one-half report making such attempts during adolescence. The aim was to examine the relationships among weight loss attempts, body size, and body perception in racially/ethnically diverse young adolescents.3,954 African American, Latino, and White 5th-graders completed the Self-Perception Profile-Physical Objective Little is known about influences on weight loss attempts, yet about one-half report making such attempts during adolescence. The aim was to examine the relationships among weight loss attempts, body size, and body perception in racially/ethnically diverse young adolescents. Methods 3954 African American, Latino, and White 5th-graders completed the Self-Perception Profile-Physical Appearance Scale and questions regarding body perceptions and past and current weight loss attempts, and had their weight and height measured. Results Latino youth most often and White youth least often reported weight loss attempts. Larger body size and negative body perception were related to more reported weight loss attempts in White and Latino youth. Body perception mediated the relationship between body size and weight loss attempts for White youth. Conclusion Motivations to lose weight appear to differ among racial/ethnic groups, suggesting that interventions for healthy weight control in youth may need to target racial/ethnic groups differently.