Miracle-dz/UniFAR-FTUs huggingface dataset:
Entropy Regularized Reinforcement Learning with Cascading Networks

Deep Reinforcement Learning (Deep RL) has had incredible achievements on high dimensional problems, yet its learning process remains unstable even on the simplest tasks. Deep RL uses neural networks as function approximators. These neural models are largely inspired by developments in the (un)supervised machine learning community. Compared to these learning frameworks, one of the major difficulties of RL is the absence of i.i. d. data. One way to cope with this difficulty is to control the rate of change of the policy at every iteration. In this work, we challenge the common practices of the (un)supervised learning community of using a fixed neural architecture, by having a neural model that grows in size at each policy update. This allows a closed form entropy regularized policy update, which leads to a better control of the rate of change of the policy at each iteration and help cope with the non i.i. d. nature of RL. Initial experiments on classical RL benchmarks show promising results with remarkable convergence on some RL tasks when compared to other deep RL baselines, while exhibiting limitations on others.
Attention-Based Audio Embeddings for Query-by-Example

An ideal audio retrieval system efficiently and robustly recognizes a short query snippet from an extensive database. However, the performance of well-known audio fingerprinting systems falls short at high signal distortion levels. This paper presents an audio retrieval system that generates noise and reverberation robust audio fingerprints using the contrastive learning framework. Using these fingerprints, the method performs a comprehensive search to identify the query audio and precisely estimate its timestamp in the reference audio. Our framework involves training a CNN to maximize the similarity between pairs of embeddings extracted from clean audio and its corresponding distorted and time-shifted version. We employ a channel-wise spectral-temporal attention mechanism to better discriminate the audio by giving more weight to the salient spectral-temporal patches in the signal. Experimental results indicate that our system is efficient in computation and memory usage while being more accurate, particularly at higher distortion levels, than competing state-of-the-art systems and scalable to a larger database.
Row-wise LiDAR Lane Detection Network with Lane Correlation Refinement

Lane detection is one of the most important functions for autonomous driving. In recent years, deep learning-based lane detection networks with RGB camera images have shown promising performance. However, camera-based methods are inherently vulnerable to adverse lighting conditions such as poor or dazzling lighting. Unlike camera, LiDAR sensor is robust to the lighting conditions. In this work, we propose a novel two-stage LiDAR lane detection network with row-wise detection approach. The first-stage network produces lane proposals through a global feature correlator backbone and a row-wise detection head. Meanwhile, the second-stage network refines the feature map of the first-stage network via attention-based mechanism between the local features around the lane proposals, and outputs a set of new lane proposals. Experimental results on the K-Lane dataset show that the proposed network advances the state-of-the-art in terms of F1-score with 30% less GFLOPs. In addition, the second-stage network is found to be especially robust to lane occlusions, thus, demonstrating the robustness of the proposed network for driving in crowded environments.
Limited or Biased: Modeling Sub-Rational Human Investors in Financial Markets

Human decision-making in real-life deviates significantly from the optimal decisions made by fully rational agents, primarily due to computational limitations or psychological biases. While existing studies in behavioral finance have discovered various aspects of human sub-rationality, there lacks a comprehensive framework to transfer these findings into an adaptive human model applicable across diverse financial market scenarios. In this study, we introduce a flexible model that incorporates five different aspects of human sub-rationality using reinforcement learning. Our model is trained using a high-fidelity multi-agent market simulator, which overcomes limitations associated with the scarcity of labeled data of individual investors. We evaluate the behavior of sub-rational human investors using hand-crafted market scenarios and SHAP value analysis, showing that our model accurately reproduces the observations in the previous studies and reveals insights of the driving factors of human behavior. Finally, we explore the impact of sub-rationality on the investor's Profit and Loss (PnL) and market quality. Our experiments reveal that bounded-rational and prospect-biased human behaviors improve liquidity but diminish price efficiency, whereas human behavior influenced by myopia, optimism, and pessimism reduces market liquidity.
Measuring Ωm with the ROSAT Deep Cluster Survey

We analyze the ROSAT Deep Cluster Survey (RDCS) to derive cosmological constraints from the evolution of the cluster X-ray luminosity distribution. The sample contains 103 galaxy clusters out to z ≃ 0.85 and flux limit Flim = 3 × 10-14 ergs s-1 cm-2 (RDCS-3) in the [0.5-2.0] keV energy band, with a high-redshift extension containing four clusters at 0.90 ≤ z ≤ 1.26 and brighter than Flim = 1 × 10-14 ergs s-1 cm-2 (RDCS-1). We assume cosmological models to be specified by the matter density parameter Ωm, the rms fluctuation amplitude at the 8 h-1 Mpc scale σ8, and the shape parameter for the cold dark matter-like power spectrum Γ. Model predictions for the cluster mass function are converted into the X-ray luminosity function in two steps. First, we convert mass into intracluster gas temperature by assuming hydrostatic equilibrium. Then, temperature is converted into X-ray luminosity by using the most recent data on the LX-TX relation for nearby and distant clusters. These include the Chandra data for six distant clusters at 0.57 ≤ z ≤ 1.27. From RDCS-3 we find Ωm = 0.35 and σ8 = 0.66 for a spatially flat universe with a cosmological constant, with no significant constraint on Γ (errors correspond to 1 σ confidence levels for three fitting parameters). Even accounting for both theoretical and observational uncertainties in the mass-X-ray luminosity conversion, an Einstein-de Sitter model is always excluded at far more than the 3 σ level. We also show that the number of X-ray-bright clusters in RDCS-1 at z > 0.9 is expected from the evolution inferred at z < 0.9 data.
Stress percolation criticality of glass to fluid transition in active cell layers

Using three-dimensional representation of confluent cell layers, we map the amorphous solid to fluid phase transition in active cell layers onto the two-dimensional (2D) site percolation universality class. Importantly, we unify two distinct, predominant, pathways associated with this transition; namely (i) cell-cell adhesion and (ii) active traction forces. For each pathway, we independently vary the corresponding control parameter and focus on the emergent mechanical stress patterns as the monolayer transitions from a glassy- to a fluid-like state. Through finite-size scaling analyses, our results lead us to establish the glassy- to fluid-like transition as a critical phenomena in terms of stress development in the cell layer and show that the associated criticality belongs to the 2D site percolation universality class. Our findings offer a fresh perspective on solid (glass-like) to fluid phase transition in active cell layers and can bridge our understanding of glassy behaviors in active matter with potential implications in biological processes such as wound healing, development, and cancer progression.
On Uncertainty in Deep State Space Models for Model-Based Reinforcement Learning

Improved state space models, such as Recurrent State Space Models (RSSMs), are a key factor behind recent advances in model-based reinforcement learning (RL). Yet, despite their empirical success, many of the underlying design choices are not well understood. We show that RSSMs use a suboptimal inference scheme and that models trained using this inference overestimate the aleatoric uncertainty of the ground truth system. We find this overestimation implicitly regularizes RSSMs and allows them to succeed in model-based RL. We postulate that this implicit regularization fulfills the same functionality as explicitly modeling epistemic uncertainty, which is crucial for many other model-based RL approaches. Yet, overestimating aleatoric uncertainty can also impair performance in cases where accurately estimating it matters, e.g., when we have to deal with occlusions, missing observations, or fusing sensor modalities at different frequencies. Moreover, the implicit regularization is a side-effect of the inference scheme and not the result of a rigorous, principled formulation, which renders analyzing or improving RSSMs difficult. Thus, we propose an alternative approach building on well-understood components for modeling aleatoric and epistemic uncertainty, dubbed Variational Recurrent Kalman Network (VRKN). This approach uses Kalman updates for exact smoothing inference in a latent space and Monte Carlo Dropout to model epistemic uncertainty. Due to the Kalman updates, the VRKN can naturally handle missing observations or sensor fusion problems with varying numbers of observations per time step. Our experiments show that using the VRKN instead of the RSSM improves performance in tasks where appropriately capturing aleatoric uncertainty is crucial while matching it in the deterministic standard benchmarks.
PeerDA: Data Augmentation via Modeling Peer Relation for Span Identification Tasks

Span identification aims at identifying specific text spans from text input and classifying them into pre-defined categories. Different from previous works that merely leverage the Subordinate (SUB) relation (i.e. if a span is an instance of a certain category) to train models, this paper for the first time explores the Peer (PR) relation, which indicates that two spans are instances of the same category and share similar features. Specifically, a novel Peer Data Augmentation (PeerDA) approach is proposed which employs span pairs with the PR relation as the augmentation data for training. PeerDA has two unique advantages: (1) There are a large number of PR span pairs for augmenting the training data. (2) The augmented data can prevent the trained model from over-fitting the superficial span-category mapping by pushing the model to leverage the span semantics. Experimental results on ten datasets over four diverse tasks across seven domains demonstrate the effectiveness of PeerDA. Notably, PeerDA achieves state-of-the-art results on six of them.
Kondo effect in f-electron superlattices

We demonstrate the importance of the Kondo effect in artificially created {\it f}-electron superlattices. We show that the Kondo effect does not only change the density of states of the {\it f}-electron layers, but is also the cause of pronounced resonances at the Fermi energy in the density of states of the non-interacting layers in the superlattice, which are between the {\it f}-electron layers. Remarkably, these resonances strongly depend on the structure of the superlattice; due to interference, the density of states at the Fermi energy can be strongly enhanced or even shows no changes at all. Furthermore, we show that by inserting the Kondo lattice layer into a three-dimensional (3D) metal, the gap of the Kondo insulating state changes from a full gap to a pseudo gap with quadratically vanishing spectral weight around the Fermi energy. Due to the formation of the Kondo insulating state in the {\it f}-electron layer, the superlattice becomes strongly anisotropic below the Kondo temperature. We prove this by calculating the in-plane and out-of-plane conductivity of the superlattice.
Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding

Learning-based methods have shown promising performance for accelerating motion planning, but mostly in the setting of static environments. For the more challenging problem of planning in dynamic environments, such as multi-arm assembly tasks and human-robot interaction, motion planners need to consider the trajectories of the dynamic obstacles and reason about temporal-spatial interactions in very large state spaces. We propose a GNN-based approach that uses temporal encoding and imitation learning with data aggregation for learning both the embeddings and the edge prioritization policies. Experiments show that the proposed methods can significantly accelerate online planning over state-of-the-art complete dynamic planning algorithms. The learned models can often reduce costly collision checking operations by more than 1000x, and thus accelerating planning by up to 95%, while achieving high success rates on hard instances as well.
Distributed Estimation and Inference for Semi-parametric Binary Response Models

The development of modern technology has enabled data collection of unprecedented size, which poses new challenges to many statistical estimation and inference problems. This paper studies the maximum score estimator of a semi-parametric binary choice model under a distributed computing environment without pre-specifying the noise distribution. An intuitive divide-and-conquer estimator is computationally expensive and restricted by a non-regular constraint on the number of machines, due to the highly non-smooth nature of the objective function. We propose (1) a one-shot divide-and-conquer estimator after smoothing the objective to relax the constraint, and (2) a multi-round estimator to completely remove the constraint via iterative smoothing. We specify an adaptive choice of kernel smoother with a sequentially shrinking bandwidth to achieve the superlinear improvement of the optimization error over the multiple iterations. The improved statistical accuracy per iteration is derived, and a quadratic convergence up to the optimal statistical error rate is established. We further provide two generalizations to handle the heterogeneity of datasets and high-dimensional problems where the parameter of interest is sparse.
AMF-Placer 2.0: Open Source Timing-driven Analytical Mixed-size Placer for Large-scale Heterogeneous FPGA

On modern field-programmable gate arrays (FPGAs), certain critical path portions of the designs might be prearranged into many multi-cell macros during synthesis. These movable macros with constraints of shape and resources lead to challenging mixed-size placement for FPGA designs which cannot be addressed by previous analytical placers. Moreover, general timing-driven placement algorithms are facing challenges when handling real-world application design and ultrascale FPGA architectures. In this work, we propose AMF-Placer 2.0, an open-source comprehensive timing-driven analytical mixed-size FPGA placer. It supports mixed-size placement of heterogeneous resources (e.g., LUT/FF/LUTRAM/MUX/CARRY/DSP/BRAM) on FPGA, with an interface to Xilinx Vivado. Standing upon the shoulders of AMF-Placer 1.0, AMFPlacer 2.0 is equipped with a series of new techniques for timing optimization, including a simple but effective timing model, placement-blockage-aware anchor insertion, WNS-aware timing-driven quadratic placement, and sector-guided detailed placement. Based on a set of the latest large open-source benchmarks from various domains for Xilinx Ultrascale FPGAs, experimental results indicate that critical path delays realized by AMF-Placer 2.0 are averagely 2.2% and 0.59% higher than those achieved by commercial tool Xilinx Vivavo 2020.2 and 2021.2 respectively. Meanwhile, the average runtime of placement procedure of AMF-Placer 2.0 is 14% and 8.5% higher than Xilinx Vivavo 2020.2 and 2021.2 respectively. Although limited by the absence of the exact timing model of the device, the information of design hierarchy and accurate routing feedback, AMF-Placer 2.0 is the first open-source FPGA placer which can handle the timingdriven mixed-size placement of practical complex designs with various FPGA resources and achieves the comparable quality to the latest commercial tools.
Min max method, shape, topological derivatives, averaged Lagrangian, homogenization, two scale convergence, Helmholtz equation

In this paper, we perform a rigourous version of shape and topological derivatives for optimizations problems under constraint Helmoltz problems. A shape and topological optimization problem is formulated by introducing cost functional. We derive first by considering the lagradian method the shape derivative of the functional. It is also proven a topological derivative with the same approach. An application to several unconstrained shape functions arising from differential geometry are also given.
MAGNETIC FIELD DRAGGING IN ACCRETION DISCS

Accretion discs are composed of ionized gas in motion around a central object. Sometimes, the disc is the source of powerful bipolar jets along its rotation axis. Theoretical models invoke the existence of a bipolar magnetic field crossing the disc and require two conditions to produce powerful jets: field lines need to be bent enough at the disc surface and the magnetic field needs to be close to equipartition. The work of Petrucci et al (2008) on the variability of X-ray binaries supposes that transitions between pure accretion phases and accretion-ejection phases are due to some variations of the disc magnetization. This rises the problem of the magnetic field dragging in accretion discs. We revisit the method developed by Lubow et al (1994) by including momentum and mass conservation equations in a time-dependent 1D MHD code.
Collision entropy and optimal uncertainty

We propose an alternative measure of quantum uncertainty for pairs of arbitrary observables in the two-dimensional case, in terms of collision entropies. We derive the optimal lower bound for this entropic uncertainty relation, which results in an analytic function of the overlap of the corresponding eigenbases. Besides, we obtain the minimum uncertainty states. We compare our relation with other formulations of the uncertainty principle.
Estimating the Enthalpy of Gasification of Acrylonitrile–Butadiene–Styrene Hybrid Rocket Fuels

This report investigates the pyrolysis properties of both extruded and additively manufactured acrylonitrile–acrylonitrile–styrene at material surface temperatures from 350 to 725°C. These temperatures approximate the range of surface temperatures reached during combustion when acrylonitrile–acrylonitrile–styrene is employed as a hybrid rocket propellant. This temperature range is significantly higher than temperatures evaluated during previous acrylonitrile–acrylonitrile–styrene fire-safety combustion and pyrolysis studies. Linear regression rates of acrylonitrile–acrylonitrile–styrene fuel grain material specimens are measured during pyrolysis and used to derive estimates for both the molar and mass-based enthalpies of gasification. Molar values for enthalpy of gasification are estimated from the regression rate data using an Arrhenius-type process model. The mass-based enthalpy of ablation is calculated from the power duty cycle of the heating element used to produce the fuel pyrolysis. Test results co.
ournal of C osmology and A stroparticle hysics J Curvature perturbation spectrum in two-ﬁeld inﬂation with a turning trajectory

We revisit a two-component inﬂaton model with a turning trajectory in the ﬁeld space, where the ﬁeld slowly rolls down along the trajectory. We consider the case when the eﬀective mass in the direction perpendicular to the trajectory, namely the isocurvature direction, is either of the same order as or much larger than the Hubble parameter. Assuming that the turning angular velocity is small, we compute analytically the corrections to the power spectrum of curvature perturbation caused by the mediation of the heavy isocurvature perturbation, and compare our analytic results with the numerical ones. Especially, when M 2eﬀ ≫ H 2 , we ﬁnd that it is proportional to M − 2 eﬀ . This result is consistent with the one obtained previously by an eﬀective ﬁeld theory approach.
Magnetic fields, non-thermal radiation and particle acceleration in colliding winds of WR-O stars

Non-thermal emission has been detected in WR-stars for many years at long wavelengths spectral range, in general attributed to synchrotron emission. Two key ingredients are needed to explain such emissions, namely magnetic fields and relativistic particles. Particles can be accelerated to relativistic speeds by Fermi processes at strong shocks. Therefore, strong synchrotron emission is usually attributed to WR binarity. The magnetic field may also be amplified at shocks, however the actual picture of the magnetic field geometry, intensity, and its role on the acceleration of particles at WR binary systems is still unclear. In this work we discuss the recent developments in MHD modelling of wind-wind collision regions by means of numerical simulations, and the coupled particle acceleration processes related.
Triadic resonances in nonlinear simulations of a fluid flow in a precessing cylinder

We present results from three-dimensional nonlinear hydrodynamic simulations of a precession driven flow in cylindrical geometry. The simulations are motivated by a dynamo experiment currently under development at Helmholtz-Zentrum Dresden-Rossendorf in which the possibility of generating a magnetohydrodynamic dynamo will be investigated in a cylinder filled with liquid sodium and simultaneously rotating around two axes. In this study, we focus on the emergence of non-axisymmetric time-dependent flow structures in terms of inertial waves which—in cylindrical geometry—form so-called Kelvin modes. For a precession ratio (Poincaré number) Po = &OHgr; p / &OHgr; c = 0.014 ? > considered by us, the amplitude of the forced Kelvin mode reaches up to one fourth of the rotation velocity of the cylindrical container confirming that precession provides a rather efficient flow driving mechanism even at moderate values of Po. More relevant for dynamo action might be free Kelvin modes with higher azimuthal wave number. These free Kelvin modes are triggered by nonlinear interactions and may constitute a triadic resonance with the fundamental forced mode when the height of the container matches their axial wave lengths. Our simulations reveal triadic resonances at aspect ratios close to those predicted by the linear theory except around the primary resonance of the forced mode. In that regime we still identify various free Kelvin modes, however, all of them exhibit a retrograde drift around the symmetry axis of the cylinder and none of them can be assigned to a triadic resonance. The amplitudes of the free Kelvin modes always remain below the forced mode but may reach up to 6% of the of the container’s angular velocity. The properties of the free Kelvin modes, namely their amplitude and their frequency, will be used in future simulations of the magnetic induction equation to investigate their ability to provide for dynamo action.