Miracle-dz/UniFAR-FTUs huggingface dataset:
Solving the electrical impedance tomography inverse problem for logarithmic conductivity: Numerical sensitivity

Purpose In the context of electrical impedance tomography (EIT), this paper aims to evaluate limitations of estimating conductivity or resistivity, as well as the improvements achieved with the use of an alternate description of the solution space, the logarithmic conductivity. Design/methodology/approach A quantitative analysis is performed, solving the inverse EIT problem by using the Gauss–Newton and non-linear conjugate gradient methods for a numerical phantom of 15 elements. A property of symmetry is studied for the direct EIT problem for a phantom of 385,601 elements. Findings Solving the inverse EIT problem in logarithmic conductivity is more robust to the initial guess, as solutions are kept within physical bounds (conductivity positiveness). Also, convergence is faster and less dependent on the final values of the estimates. Research limitations/implications Logarithmic conductivity provides an advantageous description of the solution space for the EIT inverse problem. Similar estimation problems might be subject to analogous conclusions. Originality/value This study provides a novel analysis, quantitatively comparing the effect of different variables to solve the inverse EIT problem.
Target Speaker Voice Activity Detection with Transformers and Its Integration with End-To-End Neural Diarization

This paper describes a speaker diarization model based on target speaker voice activity detection (TS-VAD) using transformers. To overcome the original TS-VAD model’s drawback of being unable to handle an arbitrary number of speakers, we investigate model architectures that use input tensors with variable-length time and speaker dimensions. Transformer layers are applied to the speaker axis to make the model output insensitive to the order of the speaker profiles provided to the TS-VAD model. Time-wise sequential layers are interspersed between these speaker-wise transformer layers to allow the temporal and cross-speaker correlations of the input speech signal to be captured. We also extend a diarization model based on end-to-end neural diarization with encoder-decoder based attractors (EEND-EDA) by replacing its dot-product-based speaker detection layer with the transformer-based TS-VAD. Experimental results on VoxConverse show that using the transformers for the cross-speaker modeling reduces the diarization error rate (DER) of TS-VAD by 11.3%, achieving a new state-of-the-art (SOTA) DER of 4.57%. Also, our extended EEND-EDA reduces DER by 6.9% on the CALLHOME dataset relative to the original EEND-EDA with a similar model size, achieving a new SOTA DER of 11.18% under a widely used training data setting.
Power Delivery for Ultra-Large-Scale Applications on Si-IF

In recent years, with the rise of artificial intelligence and big data, there is an even greater demand for scaling out computing and memory capacity. Silicon interconnect fabric (Si-IF), a wafer-scale integration platform, promotes a paradigm shift in packaging features and enables ultra-large-scale systems, while significantly improving communication bandwidth and latency. Such systems are expected to dissipate tens of kilowatts of power. Designing an efficient and robust power delivery methodology for these high power applications is a key challenge in the enablement of the Si-IF platform. Based on several figure-of-merit parameters, an efficient power delivery methodology is matched with each of three candidate applications on the Si-IF, namely, artificial intelligence accelerators, high-performance computing, and neuromorphic computing. The proposed power delivery approaches were simulated and exhibit compatibility with the relevant ultra-large-scale application on Si-IF. The simulation results confirm that the dedicated power delivery topologies can support ultra-large-scale applications on the SI-IF.
Overleaf Example

Mixed integer convex and nonlinear programs, MICP and MINLP, are expressive but require long solving times. Recent work that combines learning methods on solver heuristics has shown potential to overcome this issue allowing for applications on larger scale practical problems. Gathering sufficient training data to employ these methods still present a challenge since getting data from traditional solvers are slow and newer learning approaches still require large amounts of data. In order to scale up and make these hybrid learning approaches more manageable we propose ReDUCE, a method that exploits structure within small to medium size datasets. We also introduce the bookshelf organization problem as an MINLP as a way to measure performance of solvers with ReDUCE. Results show that existing algorithms with ReDUCE can solve this problem within a few seconds, a significant improvement over the original formulation. ReDUCE is demonstrated as a high level planner for a robotic arm for the bookshelf problem.
On Evaluating Self-Adaptive and Self-Healing Systems using Chaos Engineering

With the growing adoption of self-adaptive systems in various domains, there is an increasing need for strategies to assess their correct behavior. In particular self-healing systems, which aim to provide resilience and fault-tolerance, often deal with unanticipated failures in critical and highly dynamic environments. Their reactive and complex behavior makes it challenging to assess if these systems execute according to the desired goals. Recently, several studies have expressed concern about the lack of systematic evaluation methods for self-healing behavior.In this paper, we propose CHESS, an approach for the systematic evaluation of self-adaptive and self-healing systems that builds on chaos engineering. Chaos engineering is a methodology for subjecting a system to unexpected conditions and scenarios. It has shown great promise in helping developers build resilient microservice architectures and cyber-physical systems. CHESS turns this idea around by using chaos engineering to evaluate how well a self-healing system can withstand such perturbations. We investigate the viability of this approach through an exploratory study on a self-healing smart office environment. The study helps us explore the promises and limitations of the approach, as well as identify directions where additional work is needed. We conclude with a summary of lessons learned.
Uniqueness of entire solutions to quasilinear equations of $ p $-Laplace type

<abstract><p>We prove the uniqueness property for a class of entire solutions to the equation</p> <p><disp-formula> <label/> <tex-math id="FE1"> \begin{document}$ \begin{equation*} \left\{ \begin{array}{ll} -{\rm div}\, \mathcal{A}(x,\nabla u) = \sigma, \quad u\geq 0 \quad {\text{in }} \mathbb{R}^n, \\ {\liminf\limits_{|x|\rightarrow \infty}}\, u = 0, \end{array} \right. \end{equation*} $\end{document} </tex-math></disp-formula></p> <p>where $ \sigma $ is a nonnegative locally finite measure in $ \mathbb{R}^n $, absolutely continuous with respect to the $ p $-capacity, and $ {\rm div}\, \mathcal{A}(x, \nabla u) $ is the $ \mathcal{A} $-Laplace operator, under standard growth and monotonicity assumptions of order $ p $ ($ 1 < p < \infty $) on $ \mathcal{A}(x, \xi) $ ($ x, \xi \in \mathbb{R}^n $); the model case $ \mathcal{A}(x, \xi) = \xi | \xi |^{p-2} $ corresponds to the $ p $-Laplace operator $ \Delta_p $ on $ \mathbb{R}^n $. Our main results establish uniqueness of solutions to a similar problem,</p> <p><disp-formula> <label/> <tex-math id="FE2"> \begin{document}$ \begin{equation*} \left\{ \begin{array}{ll} -{\rm div}\, \mathcal{A}(x,\nabla u) = \sigma u^q +\mu, \quad u\geq 0 \quad {\text{in }} \mathbb{R}^n, \\ {\liminf\limits_{|x|\rightarrow \infty}}\, u = 0, \end{array} \right. \end{equation*} $\end{document} </tex-math></disp-formula></p> <p>in the sub-natural growth case $ 0 < q < p-1 $, where $ \mu, \sigma $ are nonnegative locally finite measures in $ \mathbb{R}^n $, absolutely continuous with respect to the $ p $-capacity, and $ \mathcal{A}(x, \xi) $ satisfies an additional homogeneity condition, which holds in particular for the $ p $-Laplace operator. </p></abstract>
Incremental Semantic Localization using Hierarchical Clustering of Object Association Sets

We present a novel approach for relocalization or place recognition, a fundamental problem to be solved in many robotics, automation, and AR applications. Rather than relying on often unstable appearance information, we consider a situation in which the reference map is given in the form of localized objects. Our localization framework relies on 3D semantic object detections, which are then associated to objects in the map. Possible pair-wise association sets are grown based on hierarchical clustering using a merge metric that evaluates spatial compatibility. The latter notably uses information about relative object configurations, which is invariant with respect to global transformations. Association sets are furthermore updated and expanded as the camera incrementally explores the environment and detects further objects. We test our algorithm in several challenging situations including dynamic scenes, large view-point changes, and scenes with repeated instances. Our experiments demonstrate that our approach outperforms prior art in terms of both robustness and accuracy.
The Aerostatic Seal: Analysis and Development of a New Dynamic Seal Concept for Steam Turbine Application

This thesis describes the development of a new seal concept for steam turbines called the Aerostatic Seal. The Aerostatic Seal is a dynamic seal, and so can respond to rotor radial movement to maintain a low clearance between the seal and the rotor. As the seal is dynamic, smaller clearances can be achieved without rotor contact compared to conventional static seals such as the labyrinth seal, hence increasing the efficiency of the turbine through reduced leakage. Furthermore, as the seal is dynamic it can tolerate larger radial transients typically found during start up and shut down of the steam turbine, and so also contributes to increasing the flexibility of the turbine plant. In this thesis an analytical design and analysis methodology was developed for the Aerostatic Seal. The methodology was used to generate a number of seal designs which were experimentally tested in a non-rotating test facility using room temperature air. The results confirmed that the seal would operate dynamically, and the experimental campaign provided valuable data on the operation of the seal. The non-rotating rig was also used to test a second generation seal design. The seal was then tested in a rotating test facility, which modelled high speed rotor radial transients with an adjustable eccentric rotor. The Aerostatic Seal demonstrated the ability to respond to high speed transients. A final test campaign was conducted in the high temperature steam rig at TU Braunschweig, Germany, enabling experimental demonstration of the Aerostatic Seal using realistic materials and represented realistic steam turbine conditions. Finally, based on the experimental and analytical work carried out within this thesis, a proposed Aerostatic Seal design for steam turbine implementation is presented.
On Time and Space: An Experimental Study on Graph Structural and Temporal Encodings

Dynamic networks reflect temporal changes occurring to the graph's structure and are used to model a wide variety of problems in many application fields. We investigate the design space of dynamic graph visualization along two major dimensions: the network structural and temporal representation. Significant research has been conducted evaluating the benefits and drawbacks of different structural representations for static graphs, however, few extend this comparison to a dynamic network setting. We conduct a study where we assess the participants' response times, accuracy, and preferences for different combinations of the graph's structural and temporal representations on typical dynamic network exploration tasks, with and without support of common interaction methods. Our results suggest that matrices provide better support for tasks on lower-level entities and basic interactions require longer response times while increasing accuracy. Node-link with auto animation proved to be the quickest and most accurate combination overall, while animation with playback control the most preferred temporal encoding.
Riesz-Quincunx-UNet Variational Autoencoder for Unsupervised Satellite Image Denoising

Multiresolution deep learning approaches, such as the UNet architecture, have achieved high performance in classifying and segmenting images. Most traditional convolutional neural network (CNN) architectures commonly use pooling to enlarge the receptive field, which usually results in irreversible information loss. The UNet architecture avoids this information loss by introducing skip connections that allow the reconstruction of lost information. Leveraging this property of the UNet, this study proposes to include a Riesz-Quincunx (RQ) wavelet transform, which combines: 1) higher order Riesz wavelet transform and 2) orthogonal Quincunx wavelets (commonly used to reduce blur in medical images) inside the UNet to reduce noise in satellite images and their time-series. Combining both approaches, we introduce a hybrid RQ-UNet variational autoencoder (RQUNet-VAE) scheme for image and time series decomposition used to reduce noise in satellite imagery. By including denoising capabilities directly inside the UNet architecture, we hypothesize that our RQUNet-VAE may improve downstream image processing tasks that use the traditional UNet architecture. We present qualitative and quantitative experimental results that demonstrate that our proposed RQUNet-VAE is effective at reducing noise in satellite imagery yielding results similar to other state-of-the-art noise reduction methods. We further show that our RQUNet-VAE outperforms the UNet architecture specifically in cases where images exhibit high levels of noise. We show this result in two down-stream applications for multiband satellite images, including image time-series decomposition and image segmentation.
Towards In-distribution Compatibility in Out-of-distribution Detection

Deep neural network, despite its remarkable capability of discriminating targeted in-distribution samples, shows poor performance on detecting anomalous out-of-distribution data. To address this defect, state-of-the-art solutions choose to train deep networks on an auxiliary dataset of outliers. Various training criteria for these auxiliary outliers are proposed based on heuristic intuitions. However, we find that these intuitively designed outlier training criteria can hurt in-distribution learning and eventually lead to inferior performance. To this end, we identify three causes of the in-distribution incompatibility: contradictory gradient, false likelihood, and distribution shift. Based on our new understandings, we propose a new out-of-distribution detection method by adapting both the top-design of deep models and the loss function. Our method achieves in-distribution compatibility by pursuing less interference with the probabilistic characteristic of in-distribution features. On several benchmarks, our method not only achieves the state-of-the-art out-of-distribution detection performance but also improves the in-distribution accuracy.
CH-MARL: A Multimodal Benchmark for Cooperative, Heterogeneous Multi-Agent Reinforcement Learning

We propose a multimodal (vision-and-language) benchmark for cooperative and heterogeneous multi-agent learning. We introduce a benchmark multimodal dataset with tasks involving collaboration between multiple simulated heterogeneous robots in a rich multi-room home environment. We provide an integrated learning framework, multimodal implementations of state-of-the-art multi-agent reinforcement learning techniques, and a consistent evaluation protocol. Our experiments investigate the impact of different modalities on multi-agent learning performance. We also introduce a simple message passing method between agents. The results suggest that multimodality introduces unique challenges for cooperative multi-agent learning and there is significant room for advancing multi-agent reinforcement learning methods in such settings.
Adapting the LodView RDF Browser for Navigation over the Multilingual Linguistic Linked Open Data Cloud

This paper is dedicated to using of LodView RDF browser for navigation over the multilingual Linguistic Linked Open Data cloud. We reveal several limitations of LodView that impede its use for this purpose, and propose improvements to be made for fixing these limitations. These improvements are: 1) resolution of Cyrillic URIs; 2) decoding Cyrillic URIs in Turtle representations of resources; 3) support of Cyrillic literals; 4) user-friendly URLs for RDF representations of resources; 5) support of hash URIs; 6) expanding nested resources; 7) support of RDF collections; 8) support of LATEX math notation; and 9) pagination of resource property values. We implement several of the proposed improvements.
COPD_A_246850 1061..1069

Shohei Kawachi 1 Keisaku Fujimoto 2 1Department of Biomedical Laboratory Science, Graduate School of Medicine, Shinshu University, Nagano 390-8621, Japan; 2Department of Clinical Laboratory Sciences, Shinshu University School of Health Sciences, Nagano 3908621, Japan Background: Dynamic lung hyperinflation (DLH) has been evaluated based on decreased inspiratory capacity (IC) during exercise load. However, this is not routinely done in clinical practice. We have developed a convenient method of metronome-paced incremental hyperventilation (MPIH) and reported its usefulness. In the present study, we compared these two methods for evaluating DLH and examined whether our MPIH method can be used to predict DLH during exercise. Methods: DLH was measured by MPIH and constant load exercise (CLE) in 35 patients with stable COPD. DLH was defined as the most decreased IC (IClowest) and the most decreases in IC from IC at rest (−IClowest), and we compared between these two methods. Results: The IClowest in CLE and the −IClowest in MPIH were significantly lower in emphysemadominant COPD than in emphysema-nondominant COPD. Both IClowest and −IClowest showed significant correlations between the two methods (r = 0.67, p < 0.01 and r = 0.44, p < 0.01, respectively). The endurance time of CLE was significantly correlated with IClowest following MPIH (r = 0.62, p<0.01) but notwith that obtained by theCLEmethod. Furthermore, the IClowest of MPIH was more significantly correlated with endurance time in emphysema-dominant COPD. Weak but significant correlations between the −IClowest obtained by each method and maximum modified Borg scale were observed (MPIH: r = 0.38, p = 0.02; CLE: r = 0.37, p = 0.03). Conclusion: The MPIH method may be a convenient method to predict exercise tolerance and dyspnea as a clinically useful synergic screening surrogate for DLH during exercise.
Surgery duration: Optimized prediction and causality analysis

Accurate estimation of duration of surgery (DOS) can lead to cost-effective utilization of surgical staff and operating rooms and decrease patients’ waiting time. In this study, we present a supervised DOS nonlinear regression prediction model whose accuracy outperforms earlier results. In addition, unlike previous studies, we identify the features that influence DOS prediction. Further, in difference from others, we study the causal relationship between the feature set and DOS. The feature sets used in prior studies included a subset of the features presented in this study. This study aimed to derive influential effectors of duration of surgery via optimized prediction and causality analysis. We implemented an array of machine learning algorithms and trained them on datasets comprising surgery-related data, to derive DOS prediction models. The datasets we acquired contain patient, surgical staff, and surgery features. The datasets comprised 23,293 surgery records of eight surgery types performed over a 10-year period in a public hospital. We have introduced new, unstudied features and combined them with features adopted from previous studies to generate a comprehensive feature set. We utilized feature importance methods to identify the influential features, and causal inference methods to identify the causal features. Our model demonstrates superior performance in comparison to DOS prediction models in the art. The performance of our DOS model in terms of the mean absolute error (MAE) was 14.9 minutes. The algorithm that derived the model with the best performance was the gradient boosted trees (GBT). We identified the 10 most influential features and the 10 most causal features. In addition, we showed that 40% of the influential features have a significant (p-value = 0.05) causal relationship with DOS. We developed a DOS prediction model whose accuracy is higher than that of prior models. This improvement is achieved via the introduction of a novel feature set on which the model was trained. Utilizing our prediction model, hospitals can improve the efficiency of surgery schedules, and by exploiting the identified causal relationship, can influence the DOS. Further, the feature importance methods we used can help explain the model’s predictions.
Concerns related to returning home to a “difficult-to-return zone” after a long-term evacuation due to Fukushima Nuclear Power Plant Accident: A qualitative study

This study aimed to identify concerns related to returning to the Nagadoro district of Iitate Village, Fukushima Prefecture, Japan, in 2023 among its residents as it is designated as a difficult-to-return zone after the Fukushima Daiichi nuclear power plant accident. The following four concerns were extracted from the interviews and qualitative analysis: “Difficulties in restarting/continuing farming,” which represent the difficulties in making a living from agriculture due to the absence of family members and neighbors, and the insufficient radiation decontamination; “Discriminatory treatment of products and residents from villagers,” which suggests the presence of discriminations that residents of Nagadoro district are eccentrics and its agricultural products should not be treated together with those of other districts in the Village due to the recognition that the district is severely polluted by radiation; “Shift of the responsibility of returning home from the country to residents by scapegoating,” which is characterized by the shift of responsibility from the government to the residents, including harsh social criticism of the residents of Nagadoro district for not returning even though the evacuation order has been lifted, when it should have been the government’s responsibility to recover the district to a habitable state; “Loss of options for continued evacuation,” which is the loss or weakening of the position of residents of the Nagadoro district who continue to evacuate, and of administrative compensation, resulting from the legal change that they are no longer “evacuees” after the evacuation order is lifted. The findings of this study will provide a foundation for the support of residents of the Nagadoro district after lifting the evacuation order scheduled for the spring of 2023. The findings may be transferable to the residents of other difficult-to-return zones expected to be lifted after the Nagadoro district and to also a radiation disaster-affected place in the future.