Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Randomized Subspace System Identification for Large I/O Data

In this article, a novel fast randomized subspace system identification method for estimating combined deterministic-stochastic LTI state-space models, is proposed. The algorithm is especially well-suited to identify high-order and multi-scale systems with both fast and slow dynamics, which typically require a large number of input-output data samples for accurate identification using traditional subspace methods. Instead of working with such large matrices, the dataset is compressed using randomized methods, which preserve the range-spaces of these matrices almost surely. A novel identification algorithm using this compressed dataset, is proposed. This method enables the handling of extremely large datasets, which often make conventional algorithms like N4SID, MOESP, etc. run out of computer memory. Moreover the proposed method outperforms these algorithms in terms of memory-cost, data-movement, flop count and computation time for cases where these algorithms still work in-spite of large data sizes. The effectiveness of the proposed algorithm is established by theoretical analysis and various real and simulated case studies.
Switched Lyapunov Function-Based Controller Synthesis for Networked Control Systems: A Computationally Inexpensive Approach

This letter presents a Lyapunov function based control strategy for networked control systems (NCS) affected by variable time delays and data loss. A special focus is put on the reduction of the computational complexity. The crucial step to achieving computational efficiency involves defining a specific buffering mechanism that introduces an additional delay not larger than one sampling period. This allows representing the buffered NCS as a switched system, thus leading to a significant simplification of the NCS model and subsequent controller synthesis. The novel approach does not only circumvent the need for any over-approximation technique, but also leads to a strongly decreased number of optimization variables and linear matrix inequalities, allowing hereby greater flexibility with respect to additional degrees of freedom affecting the transient behavior. The performance and computational efficiency of the control strategy are demonstrated in a simulation example.
Customer Churn Prediction Model using Explainable Machine Learning

It becomes a significant challenge to predict customer behavior and retain an existing customer with the rapid growth of digitization which opens up more opportunities for customers to choose from subscription-based products and services model. Since the cost of acquiring a new customer is five-times higher than retaining an existing customer, henceforth, there is a need to address the customer churn problem which is a major threat across the Industries. Considering direct impact on revenues, companies identify the factors that increases the customer churn rate. Here, key objective of the paper is to develop a unique Customer churn prediction model which can help to predict potential customers who are most likely to churn and such early warnings can help to take corrective measures to retain them. Here, we evaluated and analyzed the performance of various tree-based machine learning approaches and algorithms and identified the Extreme Gradient Boosting XGBOOST Classifier as the most optimal solution to Customer churn problem. To deal with such real-world problems, Paper emphasize the Model interpretability which is an important metric to help customers to understand how Churn Prediction Model is making predictions. In order to improve Model explainability and transparency, paper proposed a novel approach to calculate Shapley values for possible combination of features to explain which features are the most important/relevant features for a model to become highly interpretable, transparent and explainable to potential customers.
Targeted Adversarial Attacks Against Neural Machine Translation

Neural Machine Translation (NMT) systems are used in various applications. However, it has been shown that they are vulnerable to very small perturbations of their inputs, known as adversarial attacks. In this paper, we propose a new targeted adversarial attack against NMT models. In particular, our goal is to insert a predefined target keyword into the translation of the adversarial sentence while maintaining similarity between the original sentence and the perturbed one in the source domain. To this aim, we propose an optimization problem, including an adversarial loss term and a similarity term. We use gradient projection in the embedding space to craft an adversarial sentence. Experimental results show that our attack outperforms Seq2Sick, the other targeted adversarial attack against NMT models, in terms of success rate and decrease in translation quality. Our attack succeeds in inserting a keyword into the translation for more than 75% of sentences while similarity with the original sentence stays preserved1.
Domain Adaptation of Reinforcement Learning Agents based on Network Service Proximity

The dynamic and evolutionary nature of service requirements in wireless networks has motivated the telecom industry to consider intelligent self-adapting Reinforcement Learning (RL) agents for controlling the growing portfolio of network services. Infusion of many new types of services is anticipated with future adoption of 6G networks, and sometimes these services will be defined by applications that are external to the network. An RL agent trained for managing the needs of a specific service type may not be ideal for managing a different service type without domain adaptation. We provide a simple heuristic for evaluating a measure of proximity between a new service and existing services, and show that the RL agent of the most proximal service rapidly adapts to the new service type through a well defined process of domain adaptation. Our approach enables a trained source policy to adapt to new situations with changed dynamics without retraining a new policy, thereby achieving significant computing and cost-effectiveness. Such domain adaptation techniques may soon provide a foundation for more generalized RL-based service management under the face of rapidly evolving service types.
Learning to Detect Slip through Tactile Estimation of the Contact Force Field and its Entropy

Detection of slip during object grasping and manipulation plays a vital role in object handling. Existing solutions primarily rely on visual information to devise a strategy for grasping. However, for robotic systems to attain a level of proficiency comparable to humans, especially in consistently handling and manipulating unfamiliar objects, integrating artificial tactile sensing is increasingly essential. We introduce a novel physics-informed, data-driven approach to detect slip continuously in real time. We employ the GelSight Mini, an optical tactile sensor, attached to custom-designed grippers to gather tactile data. Our work leverages the inhomogeneity of tactile sensor readings during slip events to develop distinctive features and formulates slip detection as a classification problem. To evaluate our approach, we test multiple data-driven models on 10 common objects under different loading conditions, textures, and materials. Our results show that the best classification algorithm achieves a high average accuracy of 95.61%. We further illustrate the practical application of our research in dynamic robotic manipulation tasks, where our real-time slip detection and prevention algorithm is implemented.
MoSS: Monocular Shape Sensing for Continuum Robots

Continuum robots are promising candidates for interactive tasks in medical and industrial applications due to their unique shape, compliance, and miniaturization capability. Accurate and real-time shape sensing is essential for such tasks yet remains a challenge. Embedded shape sensing has high hardware complexity and cost, while vision-based methods require stereo setup and struggle to achieve real-time performance. This letter proposes a novel eye-to-hand monocular approach to continuum robot shape sensing. Utilizing a deep encoder-decoder network, our method, MoSSNet, eliminates the computation cost of stereo matching and reduces requirements on sensing hardware. In particular, MoSSNet comprises an encoder and three parallel decoders to uncover spatial, length, and contour information from a single RGB image, and then obtains the 3D shape through curve fitting. A two-segment tendon-driven continuum robot is used for data collection and testing, demonstrating accurate (mean shape error of 0.91 mm, or 0.36% of robot length) and real-time (70 fps) shape sensing on real-world data. Additionally, the method is optimized end-to-end and does not require fiducial markers, manual segmentation, or camera calibration.
GBMST: An Efficient Minimum Spanning Tree Clustering Based on Granular-Ball Computing

Most of the existing clustering methods are based on a single granularity of information, such as the distance and density of each data. This most fine-grained based approach is usually inefficient and susceptible to noise. Therefore, we propose a clustering algorithm that combines multi-granularity Granular-Ball and minimum spanning tree (MST). We construct coarsegrained granular-balls, and then use granular-balls and MST to implement the clustering method based on"large-scale priority", which can greatly avoid the influence of outliers and accelerate the construction process of MST. Experimental results on several data sets demonstrate the power of the algorithm. All codes have been released at https://github.com/xjnine/GBMST.
Measuring axiomatic soundness of counterfactual image models

We present a general framework for evaluating image counterfactuals. The power and flexibility of deep generative models make them valuable tools for learning mechanisms in structural causal models. However, their flexibility makes counterfactual identifiability impossible in the general case. Motivated by these issues, we revisit Pearl's axiomatic definition of counterfactuals to determine the necessary constraints of any counterfactual inference model: composition, reversibility, and effectiveness. We frame counterfactuals as functions of an input variable, its parents, and counterfactual parents and use the axiomatic constraints to restrict the set of functions that could represent the counterfactual, thus deriving distance metrics between the approximate and ideal functions. We demonstrate how these metrics can be used to compare and choose between different approximate counterfactual inference models and to provide insight into a model's shortcomings and trade-offs.
Adaptive Estimation of Intersection Bounds: a Classification Approach

This paper studies averages of intersection bounds -- the bounds defined by the infimum of a collection of regression functions -- and other similar functionals of these bounds, such as averages of saddle values. Examples of such parameters are Frechet-Hoeffding bounds, Makarov (1981) bounds on distributional effects. The proposed estimator classifies covariate values into the regions corresponding to the identity of the binding regression function and takes the sample average. The paper shows that the proposed moment function is insensitive to first-order classification mistakes, enabling various nonparametric and regularized/machine learning classifiers in the first (classification) step. The result is generalized to cover bounds on the values of linear programming problems and best linear predictor of intersection bounds.
Early Results from GLASS-JWST. XX. Unveiling a Population of “Red Excess” Galaxies in Abell2744 and in the Coeval Field

We combine JWST/NIRCam imaging and MUSE data to characterize the properties of galaxies in different environmental conditions in the cluster Abell2744 (z = 0.3064) and in its immediate surroundings. We investigate how galaxy colors, morphology, and star-forming fractions depend on wavelength and on different parameterizations of environment. Our most striking result is the discovery of a “red excess” population in F200W−F444W colors in both the cluster regions and the field. These galaxies have normal F115W−F150W colors but are up to 0.8 mag redder than red sequence galaxies in F200W−F444W. They also have rather blue rest-frame B−V colors. Galaxies in the field and at the cluster virial radius are overall characterized by redder colors, but galaxies with the largest color deviations are found in the field and in the cluster core. Several results suggest that mechanisms taking place in these regions might be more effective in producing these colors. Looking at their morphology, many cluster galaxies show signatures consistent with ram pressure stripping, while field galaxies have features resembling interactions and mergers. Our hypothesis is that these galaxies are characterized by dust-enshrouded star formation: a JWST/NIRSpec spectrum for one of the galaxies is dominated by a strong PAH at 3.3 μm, suggestive of dust-obscured star formation. Larger spectroscopic samples are needed to understand whether the color excess is due exclusively to dust-obscured star formation, as well as the role of environment in triggering it.
Divergent absorption from spin-orbit interaction in distorted Landau levels

The eﬀect of spin-orbit (and Darwin) interaction on a 2D electron gas subject to a radial symmetric, inhomogeneous 1 /r -magnetic ﬁeld is discussed analytically in a perturbative and non-perturbative manner. For this purpose, we investigate the radial Hall conductivity that emerges from an additional homogeneous electric ﬁeld perturbation perpendicular to the 2D electron gas, which solely interacts via spin-orbit coupling. Numerical calculation of the absorptive spin-orbit spectra show for an ideal InSb electron gas a behaviour that is dominated by the localized (atomic) part of the distroted Landau levels. In contrast, however, we also ﬁnd analytically that a (non-local) divergent static response emerges for Fermi energies close to the ionization energy in the thermodynamic limit. The divergent linear response implies that the external electric ﬁeld is entirely absorbed by induced radial spin-orbit currents, where the induced ﬂow direction depends on the eﬀective g ∗ -factor of the material. In more detail, the divergent currents show a critical behaviour at g ∗ c = 2, where it abruptly switches direction. The observed diverging spin-orbit absorption relies on the presence of degenerate energies with allowed selection rules that are imposed by the radial symmetry of our inhomogeneous setup. We show analytically the emergence of a discrete Rydberg-like band structure that obeys these symmetry properties. While in our case this structure turns out to be of minor relevance, it is a promising property, which may facilitate the experimental realization with diﬀerent radial ﬁelds in the future. In a last step, we investigate the robustness of the spectra with respect to slow variations of the applied magnetic ﬁeld, by solving analytically the Dirac equation expanded up to order 1 / ( mc ) 2 . We ﬁnd that the spin-orbit interaction from the time-dependent ﬁeld variations cannot lift the static degeneracies, i.e., the degenerate states are protected with respect to slow changes of the 1 /r -magnetic ﬁeld.
A class of discrete time generalized Riccati equations

In this paper, a class of discrete-time backward non-linear equations defined on some ordered Hilbert spaces of symmetric matrices is considered. The problem of the existence of some global solutions is investigated. The class of considered discrete-time non-linear equations contains, as special cases, a great number of difference Riccati equations both from the deterministic and the stochastic framework. The results proved in the paper provide the sets of necessary and sufficient conditions that guarantee the existence of some special solutions of the considered equations as: the maximal solution, the stabilizing solution and the minimal positive semi-definite solution. These conditions are expressed in terms of the feasibility of some suitable systems of linear matrix inequalities (LMI). One shows that in the case of the equations with periodic coefficients to verify the conditions that guarantee the existence of the maximal or the stabilizing solution, we have to check the solvability of some systems of LMI with a finite number of inequations. The proofs are based on some suitable properties of discrete-time linear equations defined by the positive operators on some ordered Hilbert spaces chosen adequately. The results derived in this paper provide useful conditions that guarantee the existence of the maximal solution or the stabilizing solution for different classes of difference matrix Riccati equations involved in many problems of robust control both in the deterministic and the stochastic framework. The proofs are deterministic and are accessible to the readers less familiarized with the stochastic reasonings.
Subset-Based Instance Optimality in Private Estimation

We propose a new definition of instance optimality for differentially private estimation algorithms. Our definition requires an optimal algorithm to compete, simultaneously for every dataset $D$, with the best private benchmark algorithm that (a) knows $D$ in advance and (b) is evaluated by its worst-case performance on large subsets of $D$. That is, the benchmark algorithm need not perform well when potentially extreme points are added to $D$; it only has to handle the removal of a small number of real data points that already exist. This makes our benchmark significantly stronger than those proposed in prior work. We nevertheless show, for real-valued datasets, how to construct private algorithms that achieve our notion of instance optimality when estimating a broad class of dataset properties, including means, quantiles, and $\ell_p$-norm minimizers. For means in particular, we provide a detailed analysis and show that our algorithm simultaneously matches or exceeds the asymptotic performance of existing algorithms under a range of distributional assumptions.
PuSHR: A Multirobot System for Nonprehensile Rearrangement

We focus on the problem of rearranging a set of objects with a team of car-like robot pushers built using off-the-shelf components. Maintaining control of pushed objects while avoiding collisions in a tight space demands highly coordinated motion that is challenging to execute on constrained hardware. Centralized replanning approaches become intractable even for small-sized problems whereas decentralized approaches often get stuck in deadlocks. Our key insight is that by carefully assigning pushing tasks to robots, we could reduce the complexity of the rearrangement task, enabling robust performance via scalable decentralized control. Based on this insight, we built PuSHR, a system that optimally assigns pushing tasks and trajectories to robots offline, and performs trajectory tracking via decentralized control online. Through an ablation study in simulation, we demonstrate that PuSHR dominates baselines ranging from purely centralized to fully decentralized in terms of success rate and time efficiency across challenging tasks with up to 4 robots. Hardware experiments demonstrate the transfer of our system to the real world and highlight its robustness to model inaccuracies. Our code can be found at https://github.com/prl-mushr/pushr, and videos from our experiments at https://youtu.be/nyUn9mHoR8Y.
Discrete-time sliding mode control in the presence of system uncertainty

A discrete-time sliding-mode controller is presented in this paper, based on the theory of variable structure systems. It is assumed that the parameters of the system are uncertain and external disturbances are present. A measure of these effects is obtained through the deviations from the desired sliding surface, and this information is incorporated in the control law. A predictive corrective scheme is utilized in the calculation of the control inputs and, as a result, no a priori knowledge of the uncertainty bounds is required. This approach leads to a non-switching type of control, thereby eliminating the fundamental cause of chatter. The convergence and disturbance rejection properties of the approach are verified, and its performance is tested for the tracking control of an industrial robot.
Neurologic outcome following liver transplantation for methylmalonic aciduria

Liver and liver/kidney transplantation are increasingly used in methylmalonic aciduria, but little is known on their impact on CNS. The effect of transplantation on neurological outcome was prospectively assessed in six patients pre‐ and post‐transplant by clinical evaluation and by measuring disease biomarkers in plasma and CSF, in combination with psychometric tests and brain MRI studies. Primary (methylmalonic‐ and methylcitric acid) and secondary biomarkers (glycine and glutamine) significantly improved in plasma, while they remained unchanged in CSF. Differently, biomarkers of mitochondrial dysfunction (lactate, alanine, and related ratios) significantly decreased in CSF. Neurocognitive evaluation documented significant higher post‐transplant developmental/cognitive scores and maturation of executive functions corresponding to improvement of brain atrophy, cortical thickness, and white matter maturation indexes at MRI. Three patients presented post‐transplantation reversible neurological events, which were differentiated, by means of biochemical and neuroradiological evaluations, into calcineurin inhibitor‐induced neurotoxicity and metabolic stroke‐like episode. Our study shows that transplantation has a beneficial impact on neurological outcome in methylmalonic aciduria. Early transplantation is recommended due to the high risk of long‐term complications, high disease burden, and low quality of life.
Discrepancy of the location of depression on the soft tissue and the bone in isolated zygomatic arch fracture

Background When performing reduction of zygomatic arch fractures, locating the inward portion of the fracture can be difficult. Therefore, this study investigated the discrepancy between the locations of the depression on the soft tissue and bone and sought to identify how to determine the inward portion of the fracture on the patient’s face. Methods We conducted a retrospective review of chart with isolated zygomatic arch fractures of type V in the Nam and Jung classification from March 2013 to February 2022. For consistent measurements, a reference point (RP), at the intersection between a vertical line passing through the end point of the root of the ear helix in the patient’s side-view photograph and a transverse line passing through the longest horizontal axis of the external meatus opening, was established. We then measured the distance between the RP and the soft tissue depression in a portrait and the bone depression on a computed tomography (CT) scan. The discrepancy between these distances was quantified. Results Among the patients with isolated zygomatic arch fractures, only those with a fully visible ear on a side-view photograph were included. Twenty-four patients met the inclusion criteria. There were four types of discrepancies in the location of the soft tissue depression compared to the bone depression: type I, forward and upward discrepancy (7.45 and 3.28 mm), type II, backward and upward (4.29 and 4.21 mm), type III, forward and downward (10.06 and 5.15 mm), and type IV, backward and downward (2.61 and 3.27 mm). Conclusion This study showed that discrepancy between the locations of the depressions on the soft tissue and bone exists in various directions. Therefore, applying the transverse and vertical distances measured from a bone image of the CT scan onto the patient’s face at the indicated RP will be helpful for predicting the reduction location.