Miracle-dz/UniFAR-FTUs huggingface dataset:
Features of hadronic and deconfined matter from AGS to LHC energies

Previous extensive studies on the dependence of the average transverse momentum, its slope as a function of the hadron mass and the average transverse expansion on the particle multiplicity per unit rapidity and unit transverse overlap area of the colliding partners are extended to the ratio of the energy density to the entropy density. The behaviour of the ratio between the average transverse momentum and the square root of the particle multiplicity per unit rapidity and unit transverse overlap area $\langle p_T \rangle/\sqrt{\langle dN/dy\rangle/S_{\perp}}$ as a function of collision energy for a given centrality or as a function of centrality for a given collision energy supports the predictions of CGC and percolation based approaches. The dependence of the ratio of the energy density $\langle dE_{T}/dy\rangle/S_{\perp}$ to the entropy density $\langle dN/dy\rangle/S_{\perp}$ at different collision centralities for A-A collisions from AGS, SPS, RHIC and LHC energies is presented. The trend of this ratio towards a plateau at the highest RHIC energies followed by a steep rise at LHC energies is in agreement with theoretical predictions made 40 years ago that indicate this behaviour as a signature of a phase transition. This pattern strongly depends on the collision geometry, converging towards the dependence that characterizes the pp minimum bias (MB) collisions for the most peripheral A-A collisions. Expected similarities between pp and Pb-Pb collisions at LHC energies are confirmed.
Low-Rank Covariance Completion for Graph Quilting with Applications to Functional Connectivity

As a tool for estimating networks in high dimensions, graphical models are commonly applied to calcium imaging data to estimate functional neuronal connectivity, i.e. relationships between the activities of neurons. However, in many calcium imaging data sets, the full population of neurons is not recorded simultaneously, but instead in partially overlapping blocks. This leads to the Graph Quilting problem, as ﬁrst introduced by (Vinci et al., 2019), in which the goal is to infer the structure of the full graph when only subsets of features are jointly observed. In this paper, we study a novel two-step approach to Graph Quilting, which ﬁrst imputes the complete covariance matrix using low-rank covariance completion techniques before estimating the graph structure. We introduce three approaches to solve this problem: block singular value decomposition, nuclear norm penalization, and non-convex low-rank factorization. While prior works have studied low-rank matrix completion, we address the challenges brought by the blockwise missingness and are the ﬁrst to investigate the problem in the context of graph learning. We discuss theoretical properties of the two-step procedure, showing graph selection consistency of one proposed approach by proving novel (cid:96) ∞ -norm error bounds for matrix completion with block-missingness. We then investigate the empirical performance of the proposed methods on simulations and on real-world data examples, through which we show the eﬃcacy of these methods for estimating functional connectivity from calcium imaging data. for p = 100 and with K = 2 patches of o = 60 features. We see that the average Frobenius norm between the imputed and true full covariance matrices decreases with a larger number of observations. However, the F-1 scores for edge selection do not ﬂuctuate substantially with changing the number of observations, though the standard deviation of the estimates decreases with increasing the number samples. We also show the performance of the low-rank graph quilting methods when the low-rankness of the simulated full covariance matrix changes. We measure this by the ratio of the ﬁrst eigenvalue of the covariance matrix to the last; speciﬁcally, for n = 2000 and p = 100, with K = 2 patches of size o = 60, we look at λ 1 /λ p = 500 , 200 , 50 , 10 , and 3; here, a smaller ratio represents a weaker low-rank structure. The Frobenius norm of the imputed covariance matrices decreases when the leading eigenvalue is smaller, though this may be an impact of the generated full covariance matrices rather than the estimation procedure. Also, as we would expect, when the true covariance matrix has less of a clear rank 1 structure, all of the low-rank graph quilting methods perform worse in terms of edge selection for the resulting graphical model estimate. On the other hand, the MAD gq method performs much better. This matches with the assumptions made by the two diﬀerent types of methods.
The Royalflush System for VoxCeleb Speaker Recognition Challenge 2022

In this technical report, we describe the Royalflush submissions for the VoxCeleb Speaker Recognition Challenge 2022 (VoxSRC-22). Our submissions contain track 1, which is for supervised speaker verification and track 3, which is for semi-supervised speaker verification. For track 1, we develop a powerful U-Net-based speaker embedding extractor with a symmetric architecture. The proposed system achieves 2.06% in EER and 0.1293 in MinDCF on the validation set. Compared with the state-of-the-art ECAPA-TDNN, it obtains a relative improvement of 20.7% in EER and 22.70% in MinDCF. For track 3, we employ the joint training of source domain supervision and target domain self-supervision to get a speaker embedding extractor. The subsequent clustering process can obtain target domain pseudo-speaker labels. We adapt the speaker embedding extractor using all source and target domain data in a supervised manner, where it can fully leverage both domain information. Moreover, clustering and supervised domain adaptation can be repeated until the performance converges on the validation set. Our final submission is a fusion of 10 models and achieves 7.75% EER and 0.3517 MinDCF on the validation set.
Introspective Learning : A Two-Stage Approach for Inference in Neural Networks

In this paper, we advocate for two stages in a neural network's decision making process. The first is the existing feed-forward inference framework where patterns in given data are sensed and associated with previously learned patterns. The second stage is a slower reflection stage where we ask the network to reflect on its feed-forward decision by considering and evaluating all available choices. Together, we term the two stages as introspective learning. We use gradients of trained neural networks as a measurement of this reflection. A simple three-layered Multi Layer Perceptron is used as the second stage that predicts based on all extracted gradient features. We perceptually visualize the post-hoc explanations from both stages to provide a visual grounding to introspection. For the application of recognition, we show that an introspective network is 4% more robust and 42% less prone to calibration errors when generalizing to noisy data. We also illustrate the value of introspective networks in downstream tasks that require generalizability and calibration including active learning, out-of-distribution detection, and uncertainty estimation. Finally, we ground the proposed machine introspection to human introspection for the application of image quality assessment.
Systematic Constructions of Bent-Negabent Functions, 2-Rotation Symmetric Bent-Negabent Functions and Their Duals

Bent-negabent functions have many important properties for their application in cryptography since they have the ﬂat absolute spectrum under the both Walsh-Hadamard transform and nega-Hadamard transform. In this paper, we present four new systematic constructions of bent-negabent functions on 4 k, 8 k, 4 k + 2 and 8 k + 2 variables, respectively, by modifying the truth tables of two classes of quadratic bent-negabent functions with simple form. The algebraic normal forms and duals of these constructed functions are also determined. We further identify necessary and suﬃcient conditions for those bent-negabent functions which have the maximum algebraic degree. At last, by modifying the truth tables of a class of quadratic 2-rotation symmetric bent-negabent functions, we present a construction of 2-rotation symmetric bent-negabent functions with any possible algebraic degrees. Considering that there are probably no bent-negabent functions in the rotation symmetric class, it is the ﬁrst signiﬁcant attempt to construct bent-negabent functions in the generalized rotation symmetric class.
The need for obscured supermassive black hole growth to explain quasar proximity zones in the epoch of reionization

Proximity zones of high-redshift quasars are unique probes of supermassive black hole formation, but simultaneously explaining proximity zone sizes and black hole masses has proved to be challenging. We study the robustness of some of the assumptions that are usually made to infer quasar lifetimes from proximity zone sizes. We show that small proximity zones can be readily explained by quasars that vary in brightness with a short duty cycle of fduty ∼ 0.1 and short bright periods of ton ∼ 104 yr, even for long lifetimes. We further show that reconciling this with black hole mass estimates requires the black hole to continue to grow and accrete during its obscured phase. The consequent obscured fractions of ≳ 0.7 or higher are consistent with low-redshift measurements and models of black hole accretion. Such short duty cycles and long obscured phases are also consistent with observations of large proximity zones, thus providing a simple, unified model for proximity zones of all sizes. The large dynamic range of our simulation, and its calibration to the Lyman-α forest, allows us to investigate the influence of the large-scale topology of reionization and the quasar’s host halo mass on proximity zones. We find that incomplete reionization can impede the growth of proximity zones and make them smaller up to 30 per cent, but the quasar host halo mass only affects proximity zones weakly and indirectly. Our work suggests that high-redshift proximity zones can be an effective tool to study quasar variability and black hole growth.
EMA-VIO: Deep Visual–Inertial Odometry With External Memory Attention

Accurate and robust localization is a fundamental need for mobile agents. Visual–inertial odometry (VIO) algorithms exploit the information from the camera and inertial sensors to estimate position and translation. Recent deep-learning-based VIO models attract attention as they provide pose information in a data-driven way, without the need of designing hand-crafted algorithms. Existing learning-based VIO models rely on recurrent models to fuse multimodal data and process sensor signals, which are hard to train and not efficient enough. We propose a novel learning-based VIO framework with external memory attention that effectively and efficiently combines visual and inertial features for state estimation. Our proposed model is able to estimate pose accurately and robustly, even in challenging scenarios, for example, on overcast days and water-filled ground, which are difficult for traditional VIO algorithms to extract visual features. Experiments validate that it outperforms both traditional and learning-based VIO baselines in different scenes.
Robust Reinforcement Learning Algorithm for Vision-based Ship Landing of UAVs

This paper addresses the problem of developing an algorithm for autonomous ship landing of vertical take-off and landing (VTOL) capable unmanned aerial vehicles (UAVs), using only a monocular camera in the UAV for tracking and localization. Ship landing is a challenging task due to the small landing space, six degrees of freedom ship deck motion, limited visual references for localization, and adversarial environmental conditions such as wind gusts. We first develop a computer vision algorithm which estimates the relative position of the UAV with respect to a horizon reference bar on the landing platform using the image stream from a monocular vision camera on the UAV. Our approach is motivated by the actual ship landing procedure followed by the Navy helicopter pilots in tracking the horizon reference bar as a visual cue. We then develop a robust reinforcement learning (RL) algorithm for controlling the UAV towards the landing platform even in the presence of adversarial environmental conditions such as wind gusts. We demonstrate the superior performance of our algorithm compared to a benchmark nonlinear PID control approach, both in the simulation experiments using the Gazebo environment and in the real-world setting using a Parrot ANAFI quad-rotor and sub-scale ship platform undergoing 6 degrees of freedom (DOF) deck motion.
HGI-SLAM: Loop Closure With Human and Geometric Importance Features

We present Human and Geometric Importance SLAM (HGI-SLAM), a novel approach to loop closure using salient and geometric features. Loop closure is a key element of SLAM, with many established methods for this problem. However, current methods are narrow, using either geometric or salient based features. We merge their successes into a model that outperforms both types of methods alone. Our method utilizes inexpensive monocular cameras and does not depend on depth sensors nor Lidar. HGI-SLAM utilizes geometric and salient features, processes them into descriptors, and optimizes them for a bag of words algorithm. By using a concurrent thread and combing our loop closure detection with ORB-SLAM2, our system is a complete SLAM framework. We present extensive evaluations of HGI loop detection and HGI-SLAM on the KITTI and EuRoC datasets. We also provide a qualitative analysis of our features. Our method runs in real time, and is robust to large viewpoint changes while staying accurate in organic environments. HGI-SLAM is an end-to-end SLAM system that only requires monocular vision and is comparable in performance to state-of-the-art SLAM methods.
Value Summation: A Novel Scoring Function for MPC-based Model-based Reinforcement Learning

This paper proposes a novel scoring function for the planning module of MPC-based reinforcement learning methods to address the inherent bias of using the reward function to score trajectories. The proposed method enhances the learning efficiency of existing MPC-based MBRL methods using the discounted sum of values. The method utilizes optimal trajectories to guide policy learning and updates its state-action value function based on real-world and augmented onboard data. The learning efficiency of the proposed method is evaluated in selected MuJoCo Gym environments as well as in learning locomotion skills for a simulated model of the Cassie robot. The results demonstrate that the proposed method outperforms the current state-of-the-art algorithms in terms of learning efficiency and average reward return.
Multi-contact MPC for Dynamic Loco-manipulation on Humanoid Robots

This paper presents a novel method to control humanoid robot dynamic loco-manipulation with multiple contact modes via multi-contact Model Predictive Control (MPC) framework. The proposed framework includes a multi-contact dynamics model capable of capturing various contact modes in loco-manipulation, such as hand-object contact and foot-ground contacts. Our proposed dynamics model represents the object dynamics as an external force acting on the system, which simplifies the model and makes it feasible for solving the MPC problem. In numerical validations, our multi-contact MPC framework only needs contact timings of each task and desired states to give MPC the knowledge of changes in contact modes in the prediction horizons in loco-manipulation. The proposed framework can control the humanoid robot to complete multitasks dynamic loco-manipulation applications such as efficiently picking up and dropping off objects while turning and walking.
Analysis of Urban Visitor's Walkability Based On Mobile Data: The Case of Daejeon, Korea

The walkability of destinations plays a decisive role in obtaining a sense of place for urban visitors. For an improved walkability-based visit experience, the city structure is analyzed by dividing it into small units, starting with the destination. However, existing studies focus on the walkability of residents by administrative district and use limited contextual information and structural analysis tools. Therefore, this study aims to structurally understand walkability through network analysis with mobile-based visitor data collected from 822 point-of-interest (POI) in Daejeon. In this paper, the actual walking distance between 95,817 cells with visitors and POI is measured to calculate the edge weight and the influence of cells on POI. We analyze the POI-cell network through edge weight, extract nine communities, understand the city context through POI analysis and derive the following findings. Firstly, it is possible to determine the potential demand by the visitor’s walkability-based division. Second, our walkability measurement method follows a bottom-up approach, starting with a small unit cell, understanding the entire city as a community extraction model, and possibly extending to other cities in Korea. Third, POI-centered structural analysis is possible using the number of visitors and distance in the proposed network analysis method. distance between destination and cell spatial unit. We collect point-of-interest (POI) frequently visited and cell-based visitor data, and measure the actual walking distance between POI and cell. The measured distance and the number of visitors on the cell are reflected in the edge weight for network visualization. Through this network, we extract the communities and analyze the community-related POI based on the walkability of visitors. The contribution of this study is three-fold. First, the potential demand, the key to urban tourism, can be identified by understanding the units of the visitor’s walkability-based area in Daejeon. Second, the proposed walkability measurement method is a bottom-up method based on actual data, starting with a small spatial cell, structurally understanding the entire city, and possibly expanding to other cities in Korea. Third, the destination-oriented structural analysis is performed by connecting the actual walking distance and the number of visitors to the network.
Effect of Sn on Generalized Stacking Fault Energy Surfaces in Zirconium and its Hydrides

Hydrogen embrittlement in Zr alloy fuel cladding is a primary safety concern for water based nuclear reactors. Here we investigated the stabilisation of planar defects within the forming hydrides by Sn, the primary alloying element of Zircaloy-4 used in the cladding. In order to explain formation of hydrides and planar defects observed in our experiments, we performed atomic-scale ab initio calculations focusing on the solute interactions with generalized stacking faults in hcp α -Zr and fcc zirconium hydrides. Our calculations showed that an increase in Sn concentration leads to a stabilisation of stacking faults in both α -Zr and hydride phases. However, the solution enthalpy of Sn is lower in the α -Zr as compared to the other hydride phases indicative of two competing processes of Sn deple-tion/enrichment at the Zr hydride/matrix interface. This is corroborated by experimental ﬁndings, where Sn is repelled by hydrides and is mostly found trapped at interfaces and planar defects indicative of stacking faults inside the hydride phases. Our systematic investigation enables us to understand the presence and distribution of solutes in the hydride phases, which provides a deeper insight into the microstructural evolution of such alloy’s properties during its service lifetime.
Towards mobility reports with user-level privacy

ABSTRACT The importance of human mobility analyses is growing in both research and practice, especially as applications for urban planning and mobility rely on them. Aggregate statistics and visualizations play an essential role as building blocks of data explorations and summary reports, the latter being increasingly released to third parties such as municipal administrations or in the context of citizen participation. However, such explorations already pose a threat to privacy as they reveal potentially sensitive location information, and thus should not be shared without further privacy measures. There is a substantial gap between state-of-the-art research on privacy methods and their utilization in practice. We thus conceptualize a mobility report with differential privacy guarantees and implement it as open-source software to enable a privacy-preserving exploration of key aspects of mobility data in an easily accessible way. Moreover, we evaluate the benefits of limiting user contributions using three data sets relevant to research and practice. Our results show that even a strong limit on user contribution alters the original geospatial distribution only within a comparatively small range, while significantly reducing the error introduced by adding noise to achieve privacy guarantees.
Classical and Quantum Cosmological Solutions in Teleparallel Dark Energy with Anisotropic Background Geometry

We investigate exact and analytic solutions for the field equations in the teleparallel dark energy model, where the physical space is described by the locally rotational symmetric Bianchi I, Bianchi III and Kantowski-Sachs geometries. We make use of the property that a point-like Lagrangian exists for the description of the field equations, and variational symmetries are applied for the construction of invariant functions and conservation laws. The latter are used for the derivation of new analytic solutions for the classical field equations and exact function forms for the wavefunction in the quantum limit.
APPDIA: A Discourse-aware Transformer-based Style Transfer Model for Offensive Social Media Conversations

Using style-transfer models to reduce offensiveness of social media comments can help foster a more inclusive environment. However, there are no sizable datasets that contain offensive texts and their inoffensive counterparts, and fine-tuning pretrained models with limited labeled data can lead to the loss of original meaning in the style-transferred text. To address this issue, we provide two major contributions. First, we release the first publicly-available, parallel corpus of offensive Reddit comments and their style-transferred counterparts annotated by expert sociolinguists. Then, we introduce the first discourse-aware style-transfer models that can effectively reduce offensiveness in Reddit text while preserving the meaning of the original text. These models are the first to examine inferential links between the comment and the text it is replying to when transferring the style of offensive Reddit text. We propose two different methods of integrating discourse relations with pretrained transformer models and evaluate them on our dataset of offensive comments from Reddit and their inoffensive counterparts. Improvements over the baseline with respect to both automatic metrics and human evaluation indicate that our discourse-aware models are better at preserving meaning in style-transferred text when compared to the state-of-the-art discourse-agnostic models.
Probing dynamical criticality near quantum phase transitions

We reveal a prethermal temporal regime upon suddenly quenching to the vicinity of a quantum phase transition in the time evolution of 1D spin chains. The prethermal regime is analytically found to be self-similar, and its duration is governed by the ground-state energy gap. Based on analytical insights and numerical evidence, we show that this critically prethermal regime universally exists independently of the location of the probe site, the presence of weak interactions, or the initial state. Moreover, the resulting prethermal dynamics leads to an out-of-equilibrium scaling function of the order parameter in the vicinity of the transition.