Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive observers with exponential rate of convergence

The problem of observing the state of an unknown, time invariant linear system from measurements of its input and output is considered. Instead of adapting the parameters in a Luenberger observer to solve the problem, as was done by earlier authors, the approach taken here proceeds from a so-called parametrized observer, which is only an alternative, equivalent representation of the Luenberger observer. However, the parametrized observer has a different structure where the state estimate is a linear function (and not a functional) of its parameters. Therefore, adapting the parameters in the parametrized observer results in a complete separation of the observer dynamics from the adaptive loop which substantially simplifies the design of suitable parameter adaptation schemes. Three such schemes are presented and proven to be globally exponentially rather than asymptotically convergent. In particular, the second and the third adaptation schemes allow the construction of adaptive observers with arbitrarily high (exponential) rates of convergence.
Frequency selectivity in diotic and dichotic masking conditions for normal‐hearing and hearing‐impaired listeners

Previous studies argued that, for broadband maskers, the operational binaural critical bandwidth is similar to the monaural critical bandwidth. The aim of the present study was to test this hypothesis using the notched noise paradigm. Thresholds were measured for a diotic masker and a signal that was either in phase or had an interaural phase difference of 180 degrees. For comparison, thresholds were also measured for a broadband masker (without a notch) at various masker levels to account for possible level effects of the binaural masking level difference (BMLD). Normal‐hearing and hard‐of‐hearing listeners with various degrees of sensorineural hearing impairment participated in the experiment. For all subjects, the thresholds decreased faster for the diotic than the dichotic signal as the notch width increased. The comparison with the data for the broadband masker indicated that this decrease of the BMLD is not due to the decrease of masker level within the auditory filter centered at the signal frequen.
KDSM: An uplift modeling framework based on knowledge distillation and sample matching

Uplift modeling aims to estimate the treatment effect on individuals, widely applied in the e-commerce platform to target persuadable customers and maximize the return of marketing activities. Among the existing uplift modeling methods, tree-based methods are adept at fitting increment and generalization, while neural-network-based models excel at predicting absolute value and precision, and these advantages have not been fully explored and combined. Also, the lack of counterfactual sample pairs is the root challenge in uplift modeling. In this paper, we proposed an uplift modeling framework based on Knowledge Distillation and Sample Matching (KDSM). The teacher model is the uplift decision tree (UpliftDT), whose structure is exploited to construct counterfactual sample pairs, and the pairwise incremental prediction is treated as another objective for the student model. Under the idea of multitask learning, the student model can achieve better performance on generalization and even surpass the teacher. Extensive offline experiments validate the universality of different combinations of teachers and student models and the superiority of KDSM measured against the baselines. In online A/B testing, the cost of each incremental room night is reduced by 6.5\%.
The Stellar Mass–Black Hole Mass Relation at z ∼ 2 down to BH∼107M⊙ Determined by HETDEX

We investigate the stellar mass–black hole mass ( *–BH ) relation with type 1 active galactic nuclei (AGNs) down to BH=107M⊙ , corresponding to a ≃ −21 absolute magnitude in rest-frame ultraviolet, at z = 2–2.5. Exploiting the deep and large-area spectroscopic survey of the Hobby–Eberly Telescope Dark Energy Experiment (HETDEX), we identify 66 type 1 AGNs with BH ranging from 107–1010 M ⊙ that are measured with single-epoch virial method using C iv emission lines detected in the HETDEX spectra. * of the host galaxies are estimated from optical to near-infrared photometric data taken with Spitzer, the Wide-field Infrared Survey Explorer, and ground-based 4–8 m class telescopes by CIGALE spectral energy distribution (SED) fitting. We further assess the validity of SED fitting in two cases by host-nuclear decomposition performed through surface brightness profile fitting on spatially resolved host galaxies with the James Webb Space Telescope/NIRCam CEERS data. We obtain the *–BH relation covering the unexplored low-mass ranges of BH∼107–108M⊙ , and conduct forward modeling to fully account for the selection biases and observational uncertainties. The intrinsic *–BH relation at z ∼ 2 has a moderate positive offset of 0.52 ± 0.14 dex from the local relation, suggestive of more efficient black hole growth at higher redshift even in the low-mass regime of BH∼107–108M⊙ . Our *–BH relation is inconsistent with the BH suppression at the low- * regime predicted by recent hydrodynamic simulations at a 98% confidence level, suggesting that feedback in the low-mass systems may be weaker than those produced in hydrodynamic simulations.
Very fast, approximate counterfactual explanations for decision forests

We consider finding a counterfactual explanation for a classification or regression forest, such as a random forest. This requires solving an optimization problem to find the closest input instance to a given instance for which the forest outputs a desired value. Finding an exact solution has a cost that is exponential on the number of leaves in the forest. We propose a simple but very effective approach: we constrain the optimization to input space regions populated by actual data points. The problem reduces to a form of nearest-neighbor search using a certain distance on a certain dataset. This has two advantages: first, the solution can be found very quickly, scaling to large forests and high-dimensional data, and enabling interactive use. Second, the solution found is more likely to be realistic in that it is guided towards high-density areas of input space.
Robust Multi-Modal Multi-LiDAR-Inertial Odometry and Mapping for Indoor Environments

Integrating multiple LiDAR sensors can significantly enhance a robot's perception of the environment, enabling it to capture adequate measurements for simultaneous localization and mapping (SLAM). Indeed, solid-state LiDARs can bring in high resolution at a low cost to traditional spinning LiDARs in robotic applications. However, their reduced field of view (FoV) limits performance, particularly indoors. In this paper, we propose a tightly-coupled multi-modal multi-LiDAR-inertial SLAM system for surveying and mapping tasks. By taking advantage of both solid-state and spinnings LiDARs, and built-in inertial measurement units (IMU), we achieve both robust and low-drift ego-estimation as well as high-resolution maps in diverse challenging indoor environments (e.g., small, featureless rooms). First, we use spatial-temporal calibration modules to align the timestamp and calibrate extrinsic parameters between sensors. Then, we extract two groups of feature points including edge and plane points, from LiDAR data. Next, with pre-integrated IMU data, an undistortion module is applied to the LiDAR point cloud data. Finally, the undistorted point clouds are merged into one point cloud and processed with a sliding window based optimization module. From extensive experiment results, our method shows competitive performance with state-of-the-art spinning-LiDAR-only or solid-state-LiDAR-only SLAM systems in diverse environments. More results, code, and dataset can be found at \href{https://github.com/TIERS/multi-modal-loam}{https://github.com/TIERS/multi-modal-loam}.
Flexible Gear Assembly with Visual Servoing and Force Feedback

This paper presents a vision-guided two-stage approach with force feedback to achieve high-precision and flexible gear assembly. The proposed approach integrates YOLO to coarsely localize the target workpiece in a searching phase and deep reinforcement learning (DRL) to complete the insertion. Specifically, DRL addresses the challenge of partial visibility when the on-wrist camera is too close to the workpiece of a small size. Moreover, we use force feedback to improve the robustness of the vision-guided assembly process. To reduce the effort of collecting training data on real robots, we use synthetic RGB images for training YOLO and construct an offline interaction environment leveraging sampled real-world data for training DRL agents. The proposed approach was evaluated in an industrial gear assembly experiment, which requires an assembly clearance of 0.3 mm, demonstrating high robustness and efficiency in gear searching and insertion from arbitrary positions.
ρ -CP: O PEN S OURCE D ISLOCATION D ENSITY B ASED C RYSTAL P LASTICITY F RAMEWORK FOR S IMULATING T HERMO -M ECHANICAL D EFORMATION

This work presents an open source, dislocation density based crystal plasticity modeling framework, ρ -CP. A Kocks-type thermally activated ﬂow is used for accounting for the temperature and strain rate effects on the crystallograpphic shearing rate. Slip system-level mobile and immobile dislocation densities, as well slip system-level backstress, are used as internal state variables for representing the substructure evolution during plastic deformation. A fully implicit numerical integration scheme is presented for the time integration of the ﬁnite deformation plasticity model. The framework is implemented and integrated with the open source ﬁnite element solver, Multiphysics Object-Oriented Simulation Environment (MOOSE). Example applications of the model are demonstrated for predicting the anisotropic mechanical response of single and polycrystalline hcp magnesium, strain rate effects and cyclic deformation of polycrystalline fcc OFHC copper, and temperature and strain rate effects on the thermo-mechanical deformation of polycrystalline bcc tantanlum. Simulations of realistic Voronoi-tessellated microstructures as well as Electron Back Scatter Diffraction (EBSD) microstructures are demonstrated to highlight the model’s ability to predict large deformation and misorientation developement during plastic deformation.
Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language

Activity and property prediction models are the central workhorses in drug discovery and materials sciences, but currently they have to be trained or fine-tuned for new tasks. Without training or fine-tuning, scientific language models could be used for such low-data tasks through their announced zero- and few-shot capabilities. However, their predictive quality at activity prediction is lacking. In this work, we envision a novel type of activity prediction model that is able to adapt to new prediction tasks at inference time, via understanding textual information describing the task. To this end, we propose a new architecture with separate modules for chemical and natural language inputs, and a contrastive pre-training objective on data from large biochemical databases. In extensive experiments, we show that our method CLAMP yields improved predictive performance on few-shot learning benchmarks and zero-shot problems in drug discovery. We attribute the advances of our method to the modularized architecture and to our pre-training objective.
Learning to Influence Human Behavior with Offline Reinforcement Learning

In the real world, some of the most complex settings for learned agents involve interaction with humans, who often exhibit suboptimal, unpredictable behavior due to sophisticated biases. Agents that interact with people in such settings end up influencing the actions that these people take. Our goal in this work is to enable agents to leverage that influence to improve the human’s performance in collaborative tasks, as the task unfolds. Unlike prior work, we do not assume online training with people (which tends to be too expensive and unsafe), nor access to a high fidelity simulator of the environment. Our idea is that by taking a variety of previously observed human-human interaction data and labeling it with the task reward, offline reinforcement learning (RL) can learn to combine components of behavior, and uncover actions that lead to more desirable human actions. First, we show that offline RL can learn strategies to influence and improve human behavior, despite those strategies not appearing in the dataset, by utilizing components of diverse, suboptimal interactions. In addition, we demonstrate that offline RL can learn influence that adapts with humans, thus achieving long-term coordination with them even when their behavior changes. We evaluate our proposed method with real people in the Overcooked collaborative benchmark domain, and demonstrate successful improvement in human performance.
Online Optimization Problems with Functional Constraints under Relative Lipschitz Continuity and Relative Strong Convexity Conditions

Recently, there were introduced important classes of relatively smooth, relatively continuous, and relatively strongly convex optimization problems. These concepts have significantly expanded the class of problems for which optimal complexity estimates of gradient-type methods in high-dimensional spaces take place. Basing on some recent works devoted to online optimization (regret minimization) problems with both relatively Lipschitz continuous and relatively strongly convex objective function, we introduce algorithms for solving the strongly convex optimization problem with inequality constraints in the online setting. We propose a scheme with switching between productive and nonproductive steps for such types of problems and prove its convergence rate for the class of relatively Lipschitz and strongly convex minimization problems. We also provide an experimental comparison between the proposed method and AdaMirr, recently proposed for relatively Lipschitz convex problems.
Estimating the parameters of epidemic spread on two-layer random graphs: a classical and a neural network approach

In this paper, we study the spread of a classical SIR process on a two-layer random network, where the first layer represents the households, while the second layer models the contacts outside the households by a random scale-free graph. We build a three-parameter graph, called polynomial model, where the new vertices are connected to the existing ones either uniformly, or preferentially, or by forming random triangles. We examine the effect of the graph's properties on the goodness of the estimation of the infection rate $\tau$, which is the most important parameter, determining the reproduction rate of the epidemic. In the classical maximum likelihood approach, to estimate $\tau$ one needs to approximate the number of SI edges between households, since the graph itself is supposed to be unobservable. Our simulation study reveals that the estimation is poorer at the beginning of the epidemic, for larger preferential attachment parameter of the graph, and for larger $\tau$. We present two heuristic improvement algorithms and establish our method to be robust to changes in average clustering of the graph model. We also extend a graph neural network (GNN) approach for estimating contagion dynamics for our two-layered graphs. We find that dense networks offer better training datasets. Moreover, GNN perfomance is measured better using the $l_2$ loss function rather than cross-entropy.
Detecting Human-Object Contact in Images

Humans constantly contact objects to move and perform tasks. Thus, detecting human-object contact is important for building human-centered artificial intelligence. However, there exists no robust method to detect contact between the body and the scene from an image, and there exists no dataset to learn such a detector. We fill this gap with HOT (“Human-Object conTact”), a new dataset of human-object contacts in images. To build HOT, we use two data sources: (1) We use the PROX dataset of 3D human meshes moving in 3D scenes, and automatically annotate 2D image areas for contact via 3D mesh proximity and projection. (2) We use the V-COCO, HAKE and Watch-n-Patch datasets, and ask trained annotators to draw polygons around the 2D image areas where contact takes place. We also annotate the involved body part of the human body. We use our HOT dataset to train a new contact detector, which takes a single color image as input, and outputs 2D contact heatmaps as well as the body-part labels that are in contact. This is a new and challenging task, that extends current foot-ground or hand-object contact detectors to the full generality of the whole body. The detector uses a part-attention branch to guide contact estimation through the context of the surrounding body parts and scene. We evaluate our detector extensively, and quantitative results show that our model outperforms baselines, and that all components contribute to better performance. Results on images from an online repository show reasonable detections and generalizability. Our HOT data and model are available for research at https://hot.is. tue.mpg. de.
Evaluation of Confidence-based Ensembling in Deep Learning Image Classification

Ensembling is a successful technique to improve the performance of machine learning (ML) models. Conf-Ensemble is an adaptation to Boosting to create ensembles based on model confidence instead of model errors to better classify difficult edge-cases. The key idea is to create successive model experts for samples that were difficult (not necessarily incorrectly classified) by the preceding model. This technique has been shown to provide better results than boosting in binary-classification with a small feature space (~80 features). In this paper, we evaluate the Conf-Ensemble approach in the much more complex task of image classification with the ImageNet dataset (224x224x3 features with 1000 classes). Image classification is an important benchmark for AI-based perception and thus it helps to assess if this method can be used in safety-critical applications using ML ensembles. Our experiments indicate that in a complex multi-label classification task, the expected benefit of specialization on complex input samples cannot be achieved with a small sample set, i.e., a good classifier seems to rely on very complex feature analysis that cannot be well trained on just a limited subset of"difficult samples". We propose an improvement to Conf-Ensemble to increase the number of samples fed to successive ensemble members, and a three-member Conf-Ensemble using this improvement was able to surpass a single model in accuracy, although the amount is not significant. Our findings shed light on the limits of the approach and the non-triviality of harnessing big data.
Effect of punching on electrical steels: Experimental and numerical coupled analysis

Punching electrical steels drastically alters their magnetic properties near the cutting edges, which should be accounted for when designing machines. A simple magneto-mechanical coupled finite element analysis, suitable for fully processed grades, is proposed in order to model the consequences of this local degradation of the material on the global behavior of a machine. The model is used to study the effective section of the teeth of a punched stator; calculated results are compared with experimental data and a good agreement is found.
Kardar-Parisi-Zhang universality in the linewidth of non-equilibrium 1D quasi-condensates

We investigate the finite-size origin of the emission linewidth of a spatially-extended, one-dimensional non-equilibrium condensate. We show that the well-known Schawlow-Townes scaling of laser theory, possibly including the Henry broadening factor, only holds for small system sizes, while in larger systems the linewidth displays a novel scaling determined by Kardar-Parisi-Zhang physics. This is shown to lead to an opposite dependence of the linewidth on the optical nonlinearity in the two cases. We then study how sub-universal properties of the phase dynamics such as the higher moments of the phase-phase correlator are affected by the finite size and discuss the relation between the field coherence and the exponential of the phase-phase correlator. We finally identify a configuration with enhanced open boundary conditions, which supports a spatially uniform steady-state and facilitates experimental studies of the linewidth scaling.
A naturally occurring contrast agent for OCT imaging of smokers' lung

Optical coherence tomography (OCT) offers great potential for clinical applications in terms of its cost, safety and real-time imaging capability. Improvement of its resolution for revealing sub-layers or sub-cellular components within a tissue will further widen its application. In this study we report that carbon pigment, which is frequently present in the lungs of smokers, could be used as a contrast agent to improve the OCT imaging of lung tissue. Carbon produced an intense bright OCT image at a relatively deep location. The parallel histopathological section analysis confirmed the presence of carbon pigment in such tissues. The underlying mechanism of the OCT image formation has been discussed based on a model system in which carbon particles were dispersed in agar gel. Calculations and in-depth intensity profiles of OCT revealed that higher refractive index particles with a size close to or smaller than the wavelength would greatly increase backscattering and generate a sharp contrast, while a particle size several times larger than the wavelength would absorb or obstruct the light path. The naturally occurring contrast agent could provide a diagnostic biomarker of lung tissue in smokers. Furthermore, carbon under such circumstances, can be used as an effective exogenous contrast agent, with which specific components or tissues exhibiting early tumour formation can be optically labelled to delineate the location and boundary, providing potential for early cancer detection and its treatment.
Guiding Energy-based Models via Contrastive Latent Variables

An energy-based model (EBM) is a popular generative framework that offers both explicit density and architectural flexibility, but training them is difficult since it is often unstable and time-consuming. In recent years, various training techniques have been developed, e.g., better divergence measures or stabilization in MCMC sampling, but there often exists a large gap between EBMs and other generative frameworks like GANs in terms of generation quality. In this paper, we propose a novel and effective framework for improving EBMs via contrastive representation learning (CRL). To be specific, we consider representations learned by contrastive methods as the true underlying latent variable. This contrastive latent variable could guide EBMs to understand the data structure better, so it can improve and accelerate EBM training significantly. To enable the joint training of EBM and CRL, we also design a new class of latent-variable EBMs for learning the joint density of data and the contrastive latent variable. Our experimental results demonstrate that our scheme achieves lower FID scores, compared to prior-art EBM methods (e.g., additionally using variational autoencoders or diffusion techniques), even with significantly faster and more memory-efficient training. We also show conditional and compositional generation abilities of our latent-variable EBMs as their additional benefits, even without explicit conditional training. The code is available at https://github.com/hankook/CLEL.
Counterexamples to Minkowski's Uniqueness Conjecture and Escape of Mass in Positive Characteristic

We show that there are infinitely many counterexamples to Minkowski's conjecture in positive characteristic regarding uniqueness of the upper bound of the multiplicative covering radius, $\mu$, by constructing a sequence of compact $A$ orbits where $\mu$ obtains its conjectured upper bound. In addition, we show that these orbits, as well as a slightly larger sequence of orbits, must exhibit complete escape of mass.