Miracle-dz/UniFAR-FTUs huggingface dataset:
Modal-based Kinematics and Contact Detection of Soft Robots

Soft robots offer an alternative approach to manipulate within a constrained space while maintaining a safe interaction with the external environment. Owing to its adaptable compliance characteristic, external contact force can easily deform the robot shapes and lead to undesired robot kinematic and dynamic properties. Accurate contact detection and contact location estimation are of critical importance for soft robot modeling, control, trajectory planning, and eventually affect the success of task completion. In this article, we focus on the investigation of a one degree of freedom (1-DoF) soft pneumatic bending robot, which is regarded as one of the fundamental components to construct complex, multi-DoFs soft robots. This 1-DoF soft robot is modeled through the integral representation of the spatial curve, where direct and instantaneous kinematics are calculated explicitly through a modal method. The fixed centrode deviation method is used to detect the external contact and estimate the contact location. Simulation results and experimental studies indicate that the contact location can be accurately estimated by solving a nonlinear least-square optimization problem. Experimental validation shows that the proposed algorithm is able to successfully estimate the contact location with the estimation error of 1.46 mm.
Automatic Detection of Myocontrol Failures Based upon Situational Context Information

Myoelectric control systems for assistive devices are still unreliable. The user’s input signals can become unstable over time due to e.g. fatigue, electrode displacement, or sweat. Hence, such controllers need to be constantly updated and heavily rely on user feedback. In this paper, we present an automatic failure detection method which learns when plausible predictions become unreliable and model updates are necessary. Our key insight is to enhance the control system with a set of generative models that learn sensible behaviour for a desired task from human demonstration. We illustrate our approach on a grasping scenario in Virtual Reality, in which the user is asked to grasp a bottle on a table. From demonstration our model learns the reach-to-grasp motion from a resting position to two grasps (power grasp and tridigital grasp) and how to predict the most adequate grasp from local context, e.g. tridigital grasp on the bottle cap or around the bottleneck. By measuring the error between new grasp attempts and the model prediction, the system can effectively detect which input commands do not reflect the user’s intention. We evaluated our model in two cases: i) with both position and rotation information of the wrist pose, and ii) with only rotational information. Our results show that our approach detects statistically highly significant differences in error distributions with p<0.001 between successful and failed grasp attempts in both cases.
H∞-Control of Grid-Connected Converters: Design, Objectives and Decentralized Stability Certificates

The modern power system features high penetration of power converters due to the development of renewables, HVDC, etc. Currently, the controller design and parameter tuning of power converters heavily rely on rich engineering experience and extrapolation from a single converter system, which may lead to inferior performance or even instabilities under variable grid conditions. In this paper, we propose an $\mathcal {H}_{\infty }$ -control design framework to provide a systematic way for the robust and optimal control design of power converters. We discuss how to choose weighting functions to achieve anticipated and robust performance with regards to multiple control objectives. Further, we show that by a proper choice of the weighting functions, the converter can be conveniently specified as grid-forming or grid-following in terms of small-signal dynamics. Moreover, this paper first proposes a decentralized stability criterion based on the small gain theorem, which enables us to guarantee the global small-signal stability of a multi-converter system through local control design of the power converters. We provide high-fidelity nonlinear simulations and hardware-in-the-loop (HIL) real-time simulations to illustrate the effectiveness of our method.
Hyp-RL : Hyperparameter Optimization by Reinforcement Learning

Hyperparameter tuning is an omnipresent problem in machine learning as it is an integral aspect of obtaining the state-of-the-art performance for any model. Most often, hyperparameters are optimized just by training a model on a grid of possible hyperparameter values and taking the one that performs best on a validation sample (grid search). More recently, methods have been introduced that build a so-called surrogate model that predicts the validation loss for a specific hyperparameter setting, model and dataset and then sequentially select the next hyperparameter to test, based on a heuristic function of the expected value and the uncertainty of the surrogate model called acquisition function (sequential model-based Bayesian optimization, SMBO). In this paper we model the hyperparameter optimization problem as a sequential decision problem, which hyperparameter to test next, and address it with reinforcement learning. This way our model does not have to rely on a heuristic acquisition function like SMBO, but can learn which hyperparameters to test next based on the subsequent reduction in validation loss they will eventually lead to, either because they yield good models themselves or because they allow the hyperparameter selection policy to build a better surrogate model that is able to choose better hyperparameters later on. Experiments on a large battery of 50 data sets demonstrate that our method outperforms the state-of-the-art approaches for hyperparameter learning.
More chemical detection through less sampling: amplifying chemical signals in hyperspectral data cubes through compressive sensing

Compressive sensing (CS) is a method of sampling which permits some classes of signals to be reconstructed with high accuracy even when they have been undersampled. In this paper we explore a phenomenon in which bandwise CS sampling of a hyperspectral data cube followed by reconstruction can actually result in amplification of chemical signals contained in the cube. Perhaps most surprisingly, chemical signal amplification generally seems to increase as the level of sampling decreases. In some examples, the chemical signal is significantly stronger in a data cube reconstructed from 10% CS sampling than it is in the raw, 100% sampled data cube. We explore this phenomenon in two real-world datasets including the Physical Sciences Inc. Fabry-Pérot interferometer sensor multispectral dataset and the Johns Hopkins Applied Physics Lab FTIR-based longwave infrared sensor hyperspectral dataset. Each of these datasets contains the release of a chemical simulant, such as glacial acetic acid, triethyl phospate, and sulfur hexafluoride, and in all cases we use the adaptive coherence estimator (ACE) to detect a target signal in the hyperspectral data cube. We end the paper by suggesting some theoretical justifications for why chemical signals would be amplified in CS sampled and reconstructed hyperspectral data cubes and discuss some practical implications.
Gradient Projection and Conditional Gradient Methods for Constrained Nonconvex Minimization

Abstract Minimization of a smooth function on a sphere or, more generally, on a smooth manifold, is the simplest non-convex optimization problem. It has a lot of applications. Our goal is to propose a version of the gradient projection algorithm for its solution and to obtain results that guarantee convergence of the algorithm under some minimal natural assumptions. We use the Ležanski-Polyak-Lojasiewicz condition on a manifold to prove the global linear convergence of the algorithm. Another method well fitted for the problem is the conditional gradient (Frank-Wolfe) algorithm. We examine some conditions which guarantee global convergence of full-step version of the method with linear rate.
Dark sectors with dynamical coupling

Coupled dark matter-dark energy scenarios are modeled via a dimensionless parameter $\xi$, which controls the strength of their interaction. While this coupling is commonly assumed to be constant, there is no underlying physical law or symmetry that forbids a time-dependent $\xi$ parameter. The most general and complete interacting scenarios between the two dark sectors should therefore allow for such a possibility, and it is the main purpose of this study to constrain two possible and well-motivated coupled cosmologies by means of the most recent and accurate early and late-time universe observations. We find that CMB data alone prefers $\xi(z) >0$ and therefore a smaller amount of dark matter, alleviating some crucial and well-known cosmological data tensions. An objective assessment of the Bayesian evidence for the coupled models explored here shows no particular preference for the presence of a dynamical dark sector coupling.
A Survey and Experimental Analysis of Distributed Subgraph Matching

Recently there emerge many distributed algorithms that aim at solving subgraph matching at scale. Existing algorithm-level comparisons failed to provide a systematic view to the pros and cons of each algorithm mainly due to the intertwining of strategy and optimization. In this paper, we identify four strategies and three general-purpose optimizations from representative state-of-the-art works. We implement the four strategies with the optimizations based on the common Timely dataflow system for systematic strategy-level comparison. Our implementation covers all representation algorithms. We conduct extensive experiments for both unlabelled matching and labelled matching to analyze the performance of distributed subgraph matching under various settings, which is finally summarized as a practical guide.
Bilinear Compressed Sensing Under Known Signs via Convex Programming

We consider the bilinear inverse problem of recovering two vectors, <inline-formula><tex-math notation="LaTeX">$\boldsymbol{x}\in \mathbb {R}^L$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\boldsymbol{w}\in \mathbb {R}^L$</tex-math></inline-formula>, from their entrywise product. We consider the case where <inline-formula><tex-math notation="LaTeX">$\boldsymbol{x}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\boldsymbol{w}$</tex-math></inline-formula> have known signs and are sparse with respect to known dictionaries of size <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula>, respectively. Here, <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> may be larger than, smaller than, or equal to <inline-formula><tex-math notation="LaTeX">$L$</tex-math></inline-formula>. We introduce <inline-formula><tex-math notation="LaTeX">$\ell _1$</tex-math></inline-formula>-BranchHull, which is a convex program posed in the natural parameter space and does not require an approximate solution or initialization in order to be stated or solved. Under the assumptions that <inline-formula><tex-math notation="LaTeX">$\boldsymbol{x}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\boldsymbol{w}$</tex-math></inline-formula> satisfy a comparable-effective-sparsity condition and are <inline-formula><tex-math notation="LaTeX">$S_1$</tex-math></inline-formula>- and <inline-formula><tex-math notation="LaTeX">$S_2$</tex-math></inline-formula>-sparse with respect to a random dictionary, we present a recovery guarantee in a noisy case. We show that <inline-formula><tex-math notation="LaTeX">$\ell _1$</tex-math></inline-formula>-BranchHull is robust to small dense noise with high probability if the number of measurements satisfy <inline-formula><tex-math notation="LaTeX">$L\geq \Omega ((S_1+S_2)\log ^{2}(K+N))$</tex-math></inline-formula>. Numerical experiments show that the scaling constant in the theorem is not too large. We also introduce variants of <inline-formula><tex-math notation="LaTeX">$\ell _1$</tex-math></inline-formula>-BranchHull for the purposes of tolerating noise and outliers, and for the purpose of recovering piecewise constant signals. We provide an ADMM implementation of these variants and show they can extract piecewise constant behavior from real images.
Capacity and Coverage Enhancement Using Long-Endurance Tethered Airborne Base Stations

Airborne base stations (carried by drones) have a great potential to enhance coverage and capacity of cellular networks. Multiple scenarios and use cases will highly benefit from such technology such as (i) offloading terrestrial base stations (BSs) in dense and urban areas, and (ii) providing coverage for rural areas. However, one of the main challenges facing the deployment of airborne BSs is the limited available energy at the drone, which limits the flight time. In fact, most of the currently used unmanned aerial vehicles (UAVs) can only operate for one hour maximum. This limits the performance of the UAV-enabled cellular network due to the need to frequently visit the ground station to recharge, leaving the UAV's coverage area temporarily out of service. In this article, we propose a new UAV-enabled cellular network setup based on tethered UAVs (TUAVs). In the proposed setup, the TUAV is connected to a ground station (GS) through a tether, which provides the TUAV with both energy and data. This enables a flight that can stay for days. We describe in detail the components of the proposed system. Furthermore, we enlist the main advantages of a TUAV-enabled cellular network compared to typical untethered UAVs. Next, we discuss the potential applications and use cases for TUAVs. Finally, we discuss the challenges, design considerations, and future research directions to realize the proposed setup.
Efficient Butterfly Counting for Large Bipartite Networks

Bipartite networks are of great importance in many real-world applications. In bipartite networks, butterfly (i.e., a complete 2 x 2 biclique) is the smallest non-trivial cohesive structure and plays a key role. In this paper, we study the problem of efficient counting the number of butterflies in bipartite networks. The most advanced techniques are based on enumerating wedges which is the dominant cost of counting butterflies. Nevertheless, the existing algorithms cannot efficiently handle large-scale bipartite networks. This becomes a bottleneck in large-scale applications. In this paper, instead of the existing layer-priority-based techniques, we propose a vertex-priority-based paradigm BFC-VP to enumerate much fewer wedges; this leads to a significant improvement of the time complexity of the state-of-the-art algorithms. In addition, we present cache-aware strategies to further improve time efficiency while theoretically retaining the time complexity of BFC-VP. Moreover, we also show that our proposed techniques can work efficiently in external and parallel contexts. Our extensive empirical studies demonstrate that the proposed techniques can speed up the state-of-the-art techniques by up to two orders of magnitude for the real datasets.
Adaptive resource allocation scheme for 2-hop non-regenerative MIMO relaying system

Resource allocation schemes for traditional MIMO system have been widely studied. However, when the MIMO technique is applied to relaying systems, where several separated terminals form a virtual receive array (VAA) and relay the signal from the transmitter to the receiver, these issues, which have a close relationship with the system performance, should be solved before a MIMO relaying system is deployed on a large scale. The channel capacity for a 2-hop MIMO relaying system is derived from the perspective of information theory. An optimal resource allocation under an aggregate power constraint between relaying nodes is proposed in closed form for the case with two relaying nodes. Numerical results indicate that a MIMO relaying system can achieve a higher channel capacity than traditional MIMO does and that the positions of the relaying nodes affect the system performance directly.
Fault diagnosis for static CMOS circuits

This paper presents a new methodology for transistor leakage fault diagnosis using both I/sub DDQ/ and logic information. A method for handling intermediate faulty voltages in fault simulation is proposed. A scheme for generating diagnostic test vectors based on logic information in the presence of intermediate faulty voltages is also proposed. An example is used to demonstrate the new diagnosis methodology.
Genetic Algorithms for Optimizing Perspective Air Traffic Information Displays

A perspective display of 3-D time-varying air traffic information is optimized such that selected geometrical features will be perceived by a human observer with the least errors. A previously developed spatial perception model is used to compose an objective function that reflects the quality at which observers are able to judge the 3-D relations of the airspace portrayed. A genetic algorithm is developed to optimize this multimodal objective function and exploit its time-evolving nature by simultaneously locating multiple local maxima and tracking them through time. Niching is used to tackle the dynamically changing function by anticipating future global maxima. A new technique is used to locate local maxima by utilizing a special distance function with a combination of sharing and crowding. This technique is able to cope with the asymmetrical dimensions of the local maxima and their changing shape, relative proportions and variable number. By having the GA continuously monitor the airspace, the algorithm is able to anticipate future maxima and achieve a quick and agile display response.
Adaptive Access Mechanism with Optimal Contention Window Based on Node Number Estimation Using Multiple Thresholds

How to improve the short-term fairness and increase the aggregated throughput simultaneously is a major problem in WLANs, yet has not been solved satisfactorily in previous work, especially in the situation that network loads vary significantly. To improve the performance in saturation state, we propose a novel access mechanism that sets an optimal Contention Window (CW) for all nodes in the network. By introducing a new parameter of CW Index (CWI), we present a linear CW adjustment rule based on the active node number and then develop an estimation algorithm of node number with three thresholds. Compared to previous algorithms, the new algorithm predicts the future node number based on the current network status, thus minimizes the fluctuating effect of idle slot intervals observed at each process. Moreover, it can identify the dense degree of idle slot intervals and track the changes of the accurate node number quickly. By selecting the optimal CWI adaptively, the maximum aggregated throughput keeps basically a constant despite the variation of the number of nodes. Meanwhile, by eliminating multiple transmission attempts of the same node, the short-term fairness is improved significantly. To evaluate the mechanism, we derive closed-form expressions for the network performance. The simulation results demonstrate the validity and good scalability of the proposed access mechanism.
A Comparative Study between Language-independent and Language-dependent Features for Document Classification

Each natural language has its own linguistic characters. In this paper, we investigated the optimum attributes between Korean and Chinese for document classification. To discover the best feature among language-independent n-grams, language-dependent morphemes and some other combined feature sets, the experiments on Interment news in each language were done. This paper used SVM as a machine learning algorithm. As a result, bi-gram was the best feature in Korean text categorization, and ‘uni-gram+noun+verb+idiom’ set showed the best performance in Chinese.
Automatic Diagnosis of Diabetic Retinopathy Micro aneurysm from Low Contrast Retinal Images using Mathematical Morphology Methods

Diabetic retinopathy is a disease of the retina, occurring in about a quarter of people with diabetes. The retina contains cells that convert the light into the electric signals, and these signals are then sent on to the brain. The symptoms can blur or distort the patient’s vision and are a main cause of blindness. Microaneurysm are one of the primary signs of retinopathy. Detection of exudates by ophthalmologists normally requires pupil dilation using a morphological method which takes time and affects patients. The leakage causes the retina to swell up a little and become waterlogged, a bit like a sponge. This swelling then damages the retinal cells themselves. This paper examines and proposes a set of optimally adjusted morphological operators to be used for Microaneurysm detection on diabetic retinopathy low-contrast images. Automatic tool for diagnosis of diabetic retinopathy must go through some well-defined steps. First, it has to detect the blood vessels and optic disc. Second, it has to identify abnormalities in the retina like exudates and microaneurysms that cause diabetic retinopathy. This method has resulted with sensitivity and specificity for our Microaneurysm detection is 80% and 99.5%, respectively.
A reference-set approach to information extraction from unstructured, ungrammatical data sources

This thesis investigates information extraction from unstructured, ungrammatical text on the Web such as classified ads, auction listings, and forum postings. Since the data is unstructured and ungrammatical, this information extraction precludes the use of rule-based methods that rely on consistent structures within the text or natural language processing techniques that rely on grammar. Instead, I describe extraction using a "reference set," which I define as a collection of known entities and their attributes. A reference set can be constructed from structured sources, such as databases, or scraped from semi-structured sources such as collections of Web pages. In some cases, as I shown in this thesis, a reference set can even be constructed automatically from the unstructured, ungrammatical text itself. This thesis presents methods to exploit reference sets for extraction using both automatic techniques and machine learning techniques. The automatic technique provides a scalable and accurate approach to extraction from unstructured, ungrammatical text. The machine learning approach provides even higher accuracy extractions and deals with ambiguous extractions, although at the cost of requiring human effort to label training data. The results demonstrate that reference-set based extraction outperforms the current state-of-the-art systems that rely on structural or grammatical clues, which is not appropriate for unstructured, ungrammatical text. Even the fully automatic case, which constructs its own reference set for automatic extraction, is competitive with the current state-of-the-art techniques that require labeled data. Reference-set based extraction from unstructured, ungrammatical text allows for a whole category of sources to be queried, allowing for their inclusion in data integration systems that were previously limited to structured and semi-structured sources.