Miracle-dz/UniFAR-FTUs huggingface dataset:
When Demonstrations meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning

Offline inverse reinforcement learning (Offline IRL) aims to recover the structure of rewards and environment dynamics that underlie observed actions in a fixed, finite set of demonstrations from an expert agent. Accurate models of expertise in executing a task has applications in safety-sensitive applications such as clinical decision making and autonomous driving. However, the structure of an expert's preferences implicit in observed actions is closely linked to the expert's model of the environment dynamics (i.e. the ``world'' model). Thus, inaccurate models of the world obtained from finite data with limited coverage could compound inaccuracy in estimated rewards. To address this issue, we propose a bi-level optimization formulation of the estimation task wherein the upper level is likelihood maximization based upon a conservative model of the expert's policy (lower level). The policy model is conservative in that it maximizes reward subject to a penalty that is increasing in the uncertainty of the estimated model of the world. We propose a new algorithmic framework to solve the bi-level optimization problem formulation and provide statistical and computational guarantees of performance for the associated optimal reward estimator. Finally, we demonstrate that the proposed algorithm outperforms the state-of-the-art offline IRL and imitation learning benchmarks by a large margin, over the continuous control tasks in MuJoCo and different datasets in the D4RL benchmark.
Luminosity distance uncertainties from gravitational wave detections of binary neutron stars by third generation observatories

A new generation of terrestrial gravitational wave detectors is currently being planned for the next decade, and it is expected to detect most of the coalescences of compact objects in the universe with masses up to a thousand times the solar mass. Among the several possible applications of current and future detections, we focus on the impact on the measure of the luminosity distance of the sources, which is an invaluable tool for constraining the cosmic expansion history of the universe. We study two specific detector topologies, triangular and L-shaped, by investigating how topology and relative orientation of up to three detectors can minimize the uncertainty measure of the luminosity distance. While the precision in distance measurement is correlated with several geometric angles determining the source position and orientation, focusing on bright standard sirens and assuming redshift to be measured with high accuracy, we obtain analytic and numerical results for its uncertainty depending on type and number of detectors composing a network, as well as on the inclination angle of the binary plane with respect to the wave propagation direction. We also analyze the best relative location and orientation of two third generation detectors to minimize luminosity distance uncertainty, showing that prior knowledge of the inclination angle distribution plays an important role in precision recovery of luminosity distance, and that a suitably arranged network of detectors can reduce drastically the uncertainty measure, approaching the limit imposed by lensing effects intervening between source and detector at redshift $z \gtrsim 0.7$.
Influence of photon reabsorption on quasi-steady-state photoluminescence measurements on crystalline silicon

The influence of reabsorption of spontaneously emitted photons within silicon samples on quasi-steady-state photoluminescence lifetime measurements and on implied current voltage curves obtained from photoluminescence is analyzed. It is shown that experimental artifacts resulting from reabsorption can generally be expected to be small and that they are expected only if the relative carrier density profile varies strongly with injection level. Such strong variations in the relative carrier density profile are shown to be expected only if the effective minority carrier lifetime varies with injection level over the range from 1to50μs which even then results in an under- or overestimation of the effective minority carrier lifetime by less than 20%.
Waveform synthesis for the design and image reconstruction of step FMCW ultrasound imaging systems with conformal transducer arrays

In this paper, we present the image reconstruction algorithm developed for a conformal ultrasound array imaging system operating in the step frequency-modulated continuous wave (FMCW) mode. The image formation procedure is based on a key relationship that establishes the equivalence between pulse-echo and step FMCW modalities, and thus permits conversion between the data types. Prior step FMCW simulation work could then be merged with pulse-echo data collected experimentally to achieve full-scale synthesis between laboratory data and a structured theoretical framework. We describe how an experimentally acquired pulse-echo waveform was extracted and incorporated into a step FMCW imaging simulation to increase image accuracy and improve visualization of physical effects. With knowledge of the transducer element positions in a multistatic configuration, image reconstruction was achieved by mapping the complex range profiles over to a target region. Included in this paper are images reconstructed after waveform synthesis, which feature transducer elements uniformly spaced around a circular aperture imaging several enclosed targets with different bandwidths.
Risk Management Core—Toward an Explicit Representation of Risk in Automated Driving

While current automotive safety standards provide implicit guidance on how unreasonable risk can be avoided, manufacturers are required to specify risk acceptance criteria for Automated Driving Systems (SAE Level 3 and higher). However, the ‘unreasonable’ level of risk of Automated Driving Systems is not yet concisely defined. Solely applying current safety standards to such novel systems could potentially not be sufficient for their acceptance. As risk is managed with implicit knowledge about safety measures in existing automotive standards, an explicit alignment with risk acceptance criteria is challenging. Hence, we propose an approach for an explicit representation and management of risk, which we call the Risk Management Core. The proposal of this process framework is based on requirements elicited from current safety standards and is applied to the task of specifying safe behavior for an Automated Driving System in an example scenario.
Instantaneous Optimal Wilson-Θ Control Method

A control algorithm based on the instantaneous optimal control method, is presented for on-line control of the civil engineering structures subjected to earthquake excitations. This algorithm employs the unconditionally stable Wilson-θ method to discretize the continuous second-order form of the dynamical equation of motion, and uses a new performance index having an acceleration term as well as velocity and displacement terms. This method is named the instantaneous optimal Wilson-θ method. An eight-story shear-type building frame using one active mass damper/driver mechanism installed on the roof is used to demonstrate the efficiency of the proposed control algorithm in comparison to the other optimal control methods. Behavior of different combinations of weighting matrices related to displacement, velocity, and acceleration response vectors of the entire building are examined, and compared with the complete feedback control of the response vectors.
FIDO2 the Rescue? Platform vs. Roaming Authentication on Smartphones

Modern smartphones support FIDO2 passwordless authentication using either external security keys or internal biometric authentication, but it is unclear whether users appreciate and accept these new forms of web authentication for their own accounts. We present the first lab study (N=87) comparing platform and roaming authentication on smartphones, determining the practical strengths and weaknesses of FIDO2 as perceived by users in a mobile scenario. Most participants were willing to adopt passwordless authentication during our in-person user study, but closer analysis shows that participants prioritize usability, security, and availability differently depending on the account type. We identify remaining adoption barriers that prevent FIDO2 from succeeding password authentication, such as missing support for contemporary usage patterns, including account delegation and usage on multiple clients.
The science case of the CHEOPS planet finder for VLT

The CHEOPS Planet Finder is one of the proposed second generation instruments for the VLT. Its purpose is to image and characterize giant extrasolar planets in different phases of their evolution: young, warm planets as well as old, cold ones. Imaging the last ones is the most challenging task because of the very large (>108) flux contrast with their star. Detection of such faint sources close to the stars from the ground requires a very high Strehl ratio and efficient suppression of the speckle noise. Two complementary strategies, based on imaging polarimetry using fast modulation and on integral field spectroscopy, are included as scientific channels of CHEOPS, after the high order adaptive optics module. The outputs of the two channels will allow a close insight into the main properties of detected extrasolar planets. In addition, the CHEOPS instrument is well suited for a number of astrophysical projects, which are briefly described.
A Wright-Fisher graph model and the impact of directional selection on genetic variation

We introduce a multi-allele Wright-Fisher model with non-recurrent, reversible mutation and directional selection. In this setting, the allele frequencies at a single locus track the path of a hybrid jump-diffusion process with state space given by the vertex and edge set of a graph. Vertices represent monomorphic population states and edge-positions mark the biallelic proportions of ancestral and derived alleles during polymorphic segments. We derive the stationary distribution in mutation-selection-drift equilibrium and obtain the expected allele frequency spectrum under large population size scaling. For the extended model with multiple independent loci we derive rigorous upper bounds for a wide class of associated measures of genetic variation. Within this framework we present mathematically precise arguments to conclude that the presence of directional selection reduces the magnitude of genetic variation, as constrained by the bounds for neutral evolution.
A comparison of template vs. direct model fitting for redshift-space distortions in BOSS

The growth of large-scale structure, as revealed in the anisotropic of clustering of galaxies in the low redshift Universe at z < 2, provides a stringent test of our cosmological model. The strongest current constraints come from the BOSS and eBOSS surveys, with uncertainties on σ 8, the amplitude of clustering on an 8 h -1Mpc scale, of less than 10 per cent. A number of different approaches have been taken to fitting this signal, leading to discrepancies of up to 1σ in the measurements of the amplitude of fluctuations at late times. We compare in some detail two of the leading approaches, one based on fitting a template cosmology whose amplitude and length scales are allowed to float with one based on varying the underlying parameters of a cosmological model directly, when fitting to the BOSS DR12 data. Holding the input data, scale cuts, window functions and modeling framework fixed we are able to isolate the cause of the differences and discuss the implications for future surveys.
LiveHand: Real-time and Photorealistic Neural Hand Rendering

The human hand is the main medium through which we interact with our surroundings, making its digitization an important problem. While there are several works modeling the geometry of hands, little attention has been paid to capturing photo-realistic appearance. Moreover, for applications in extended reality and gaming, real-time rendering is critical. We present the first neural-implicit approach to photo-realistically render hands in real-time. This is a challenging problem as hands are textured and undergo strong articulations with pose-dependent effects. However, we show that this aim is achievable through our carefully designed method. This includes training on a low-resolution rendering of a neural radiance field, together with a 3D-consistent super-resolution module and mesh-guided sampling and space canonicalization. We demonstrate a novel application of perceptual loss on the image space, which is critical for learning details accurately. We also show a live demo where we photo-realistically render the human hand in real-time for the first time, while also modeling pose-and view-dependent appearance effects. We ablate all our design choices and show that they optimize for rendering speed and quality. Video results and our code can be accessed from https://vcai.mpi-inf.mpg. de/projects/LiveHand/
Deep Convolutional Neural Network for Plume Rise Measurements in Industrial Environments

Determining the height of plume clouds is crucial for various applications, including global climate models. Smokestack plume rise refers to the altitude at which the plume cloud travels downwind until its momentum dissipates and the temperatures of the plume cloud and its surroundings become equal. While most air-quality models employ different parameterizations to forecast plume rise, they have not been effective in accurately estimating it. This paper introduces a novel framework that utilizes Deep Convolutional Neural Networks (DCNNs) to monitor smokestack plume clouds and make real-time, long-term measurements of plume rise. The framework comprises three stages. In the first stage, the plume cloud is identified using an enhanced Mask R-CNN, known as the Deep Plume Rise Network (DPRNet). Next, image processing analysis and least squares theory are applied to determine the plume cloud’s boundaries and fit an asymptotic model to its centerlines. The z-coordinate of the critical point of this model represents the plume rise. Finally, a geometric transformation phase converts image measurements into real-world values. This study’s findings indicate that the DPRNet outperforms conventional smoke border detection and recognition networks. In quantitative terms, the proposed approach yielded a 22% enhancement in the F1 score, compared to its closest competitor, DeepLabv3.
Search for neutrino counterparts to the gravitational wave sources from LIGO/Virgo O3 run with the ANTARES detector

Since 2015 the LIGO and Virgo interferometers have detected gravitational waves from almost one hundred coalescences of compact objects (black holes and neutron stars). This article presents the results of a search performed with data from the ANTARES telescope to identify neutrino counterparts to the gravitational wave sources detected during the third LIGO/Virgo observing run and reported in the catalogues GWTC-2, GWTC-2.1, and GWTC-3. This search is sensitive to all-sky neutrinos of all flavours and of energies > 100 GeV, thanks to the inclusion of both track-like events (mainly induced by νμ charged-current interactions) and shower-like events (induced by other interaction types). Neutrinos are selected if they are detected within ± 500 s from the GW merger and with a reconstructed direction compatible with its sky localisation. No significant excess is found for any of the 80 analysed GW events, and upper limits on the neutrino emission are derived. Using the information from the GW catalogues and assuming isotropic emission, upper limits on the total energy E tot,ν emitted as neutrinos of all flavours and on the ratio fν = E tot,ν /E GW between neutrino and GW emissions are also computed. Finally, a stacked analysis of all the 72 binary black hole mergers (respectively the 7 neutron star-black hole merger candidates) has been performed to constrain the typical neutrino emission within this population, leading to the limits: E tot,ν < 4.0 × 1053 erg and fν < 0.15 (respectively, E tot,ν < 3.2 × 10^53 erg and fν < 0.88) for E -2 spectrum and isotropic emission. Other assumptions including softer spectra and non-isotropic scenarios have also been tested.
Explicit bounds for the layer number of the grid

The number of steps required to exhaust a point set by iteratively removing the vertices of its convex hull is called the layer number of the point set. This article presents a short proof that the layer number of the grid $\{1,2,\dots,n\}^d$ is at most $\frac{1}{4}dn^2+1$, significantly improving the dependence on $d$ in the best-known upper bound. We also prove a lower bound of $\frac{1}{2}d(n-1)+1$, which shows that the layer number of the grid is linear in $d$.
A Case Study on Record Matching of Individuals in Historical Archives of Indigenous Databases

Digitization of historical records has produced a significant amount of data for analysis and interpretation. A critical challenge is the ability to relate historical information across different archives to allow for the data to be framed in the appropriate historical context. This paper presents a real-world case study on historical information integration and record matching with the goal to improve the historical value of archives containing data in the period 1800 to 1920. The archives contain unique information about M\'etis and Indigenous people in Canada and interactions with European settlers. The archives contain thousands of records that have increased relevance when relationships and interconnections are discovered. The contribution is a record linking approach suitable for historical archives and an evaluation of its effectiveness. Experimental results demonstrate potential for discovering historical linkage with high precision enabling new historical discoveries.
Transience of simple random walks with linear entropy growth

Using the technique of evolving sets, we explore the connection between entropy growth and transience for simple random walks on connected infinite graphs with bounded degree. In particular we show that for a simple random walk starting at a vertex $x_0$, if the entropy after $n$ steps, $E_n$ is at least $Cn$ where the $C$ is independent of $x_0$, then the random walk is transient. We also give an example which demonstrates that the condition of $C$ being independent of $x_0$ is necessary.
Quantum Learning Theory Beyond Batch Binary Classification

Arunachalam and de Wolf (2018) showed that the sample complexity of quantum batch learning of boolean functions, in the realizable and agnostic settings, has the same form and order as the corresponding classical sample complexities. In this paper, we extend this, ostensibly surprising, message to batch multiclass learning, online boolean learning, and online multiclass learning. For our online learning results, we first consider an adaptive adversary variant of the classical model of Dawid and Tewari (2022). Then, we introduce the first (to the best of our knowledge) model of online learning with quantum examples.
Path Integral Method for Pricing Proportional Step Double-Barrier Option with Time Dependent Parameters

Path integral method in quantum mechanics provides a new thinking for barrier option pricing. For proportional double-barrier step (PDBS) options, the option price changing process is analogous to a particle moving in a finite symmetric square potential well. We have derived the pricing kernel of PDBS options with time dependent interest rate and volatility. Numerical results of option price as a function of underlying asset price are shown as well. Path integral method can be easily generalized to the pricing of PDBS options with curved boundaries.
Improved Online Conformal Prediction via Strongly Adaptive Online Learning

We study the problem of uncertainty quantification via prediction sets, in an online setting where the data distribution may vary arbitrarily over time. Recent work develops online conformal prediction techniques that leverage regret minimization algorithms from the online learning literature to learn prediction sets with approximately valid coverage and small regret. However, standard regret minimization could be insufficient for handling changing environments, where performance guarantees may be desired not only over the full time horizon but also in all (sub-)intervals of time. We develop new online conformal prediction methods that minimize the strongly adaptive regret, which measures the worst-case regret over all intervals of a fixed length. We prove that our methods achieve near-optimal strongly adaptive regret for all interval lengths simultaneously, and approximately valid coverage. Experiments show that our methods consistently obtain better coverage and smaller prediction sets than existing methods on real-world tasks, such as time series forecasting and image classification under distribution shift.
Confidence Score Based Speaker Adaptation of Conformer Speech Recognition Systems

Speaker adaptation techniques provide a powerful solution to customise automatic speech recognition (ASR) systems for individual users. Practical application of unsupervised model-based speaker adaptation techniques to data intensive end-to-end ASR systems is hindered by the scarcity of speaker-level data and performance sensitivity to transcription errors. To address these issues, a set of compact and data efficient speaker-dependent (SD) parameter representations are used to facilitate both speaker adaptive training and test-time unsupervised speaker adaptation of state-of-the-art Conformer ASR systems. The sensitivity to supervision quality is reduced using a confidence score-based selection of the less erroneous subset of speaker-level adaptation data. Two lightweight confidence score estimation modules are proposed to produce more reliable confidence scores. The data sparsity issue, which is exacerbated by data selection, is addressed by modelling the SD parameter uncertainty using Bayesian learning. Experiments on the benchmark 300-hour Switchboard and the 233-hour AMI datasets suggest that the proposed confidence score-based adaptation schemes consistently outperformed the baseline speaker-independent (SI) Conformer model and conventional non-Bayesian, point estimate-based adaptation using no speaker data selection. Similar consistent performance improvements were retained after external Transformer and LSTM language model rescoring. In particular, on the 300-hour Switchboard corpus, statistically significant WER reductions of 1.0%, 1.3%, and 1.4% absolute (9.5%, 10.9%, and 11.3% relative) were obtained over the baseline SI Conformer on the NIST Hub5’00, RT02, and RT03 evaluation sets respectively. Similar WER reductions of 2.7% and 3.3% absolute (8.9% and 10.2% relative) were also obtained on the AMI development and evaluation sets.