Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance assessment of switched control systems based on tensor space approach

Based on tensor space, a new performance assessment approach is proposed for switched control systems in this paper. Switched control system is widely existing in industrial processes, especially in safety‐important processes where the systems controlled may be switched by protection logic during accident. Clearly, bad performance of control and protection strategies will eventually drive the system to a dangerous condition. To treat this problem, one may set up a performance assessment procedure for the switched control systems using multiple model method, where performance is evaluated through assessment of every individual submodel of the switched system. Obviously, this approach ignores interaction nature between control algorithm and protection strategy. The proposed method can capture the interaction nature of control and protection systems. In the tensor space, the interacting relation of control and protection systems can be synthetically represented by adding the logical switching space. Specifically, the tensor space modeling representation for switched systems has been developed, which can actually model the high coupling interaction of control and protection systems. The data‐driven tensor space algorithm based on higher‐order singular value decomposition has also been developed to assess the performance of switched control systems. By using orthogonal projection in tensor space extended from the matrix space, prediction error approach has been employed to obtain the optimal prediction error variance being as the control performance benchmark for performance assessment. Finally, numerical simulation examples are presented to illustrate the rationality and effectiveness of tensor space approach by comparing with the multiple model approach. Copyright © 2015 John Wiley & Sons, Ltd.
Measurement-based quantum communication with resource states generated by entanglement purification

We investigate measurement-based quantum communication with noisy resource states that are generated by entanglement purification. We consider the transmission of encoded information via noisy quantum channels using a measurement-based implementation of encoding, error correction and decoding. We show that such an approach offers advantages over direct transmission, gate-based error correction and measurement-based schemes with direct generation of resource states. We analyze the noise structure of resource states generated by entanglement purification and show that a local error model, i.e. noise acting independently on all qubits of the resource state, is a good approximation in general, and provides an exact description for $GHZ$-states. The latter are resources for a measurement-based implementation of error correction codes for bit-flip or phase flip errors. This provides a first link between the recently found very high thresholds for fault-tolerant measurement-based quantum information processing based on local error models for resource states, to error thresholds for gate based computational models.
Motion Vector Decision for Low Complexity HEVC Encoding

In this paper, a simple motion vector decision method is addressed to reduce the complexity of the motion estimation in the high efficiency video coding (HEVC). Its basic idea is to inherit the motion vector of a parent for its remaining 2nd partition if the motion information of the 1st partition dividing its parent 2Nx2N PU is identical with that of its parent 2Nx2N PU. Furthermore, this concept is extended to coding unit (CU) depth decision. Experimental results show that the proposed method achieves on average 6.1% of total encoding time saving with only 0.5% BDBR loss under a random access condition, and 6.6% of total encoding time saving with 0.4% BDBR loss under a low delay condition.
The Spitzer Mass Stratiflcation Instability in Dense Star Clusters

Globular clusters containing a sub-population of sufficiently massive heavier stars will develop the so-called Spitzer mass stratification instability. In their approach to thermal equilibrium with the surrounding lighter stars, the heavier stars slow down and sink into the central region of the cluster. The sub-population heats as it falls into the core, losing energy to lighter stars through random two-body interactions. As the lighter stars absorb energy, they attain larger orbits. Over time the heavier sub-population will collect in the core where few lighter stars remain. Eventually, the heavier sub-population will have become so energetic and isolated that the lighter stars are not able to rapidly conduct heat away. Therefore, the dynamical evolution of stars in the heavier subpopulation decouples from that of the lighter stars. The heavier sub-population later undergoes a gravothermal collapse. It is possible to represent the dynamical evolution of globular clusters by a series of quasistationary equilibrium models whose velocities are periodically perturbed according to calculated rates representing the average effect of stellar encounters. Here we present an implementation of the so-called Henon Monte Carlo method for modeling the evolution of a stellar cluster containing a subpopulation of heavier stars. We observe the effects of Spitzer’s instability in numerous calculations, and confirm that the instability occurs under the conditions that Spitzer proposed. Thesis Supervisor: Frederic A. Rasio Title: Assistant Professor of Physics
ISCCP Cloud Algorithm Intercomparison.

Abstract The International Satellite Cloud Climatology Project (ISCCP) will provide a uniform global climatology of satellite-measured radiances and derive an experimental climatology of cloud radiative properties from these radiances. A pilot study to intercompare cloud analysis algorithms was initiated in 1981 to define a state-of-the-art algorithm for ISCCP. This study compared the results of applying six different algorithms to the same satellite radiance data. The results show that the performance of all current algorithms depends on how accurately the clear sky radiances are specified; much improvement in results is possible with better methods for obtaining these clear-sky radiances. A major difference between the algorithms is caused by their sensitivity to changes in the cloud size distribution and optical properties: all methods, which work well for some cloud types or climate regions, do poorly for other situations. Therefore, the ISCCP algorithm is composed of a series of steps, each of which .
Introduction: Exploring the Future of Interior Design in a Virtual–Physical Continuum

, are at the core of interior design practice. In a similar direction, Vahdat pointed out that the very nature of interior design allows professionals to provide unique user experiences. Highlighting the significance of virtual space in the future market of design, Vahdat believes that instead of imitating photorealistic renders, it is essential to introduce alienation/defamiliarization strategies—through which designers can invoke awareness about the mediation involved in the virtual sphere. The changing role of an interior designer is more uncertain within the context of virtual technology that has the ability to readily conjure quick solutions through publicly accessible artificial intelligence programs such as MidJourney and DALL-E. These programs are harnessing public powers of imagination to produce numerous interior design solutions through verbal and visual prompts. Perhaps, more positively, it is an opportunity for the profession to shape public knowledge with a unique “gaze” that designers are trained for and to differentiate themselves through deep investigations of materials, space
Dipeptidyl Amino-Peptidase 3 (DPP3) as an Early Marker of Severity in a Patient Population with Cardiogenic Shock

Dipeptidyl amino-peptidase 3 (DPP3) is an aminopeptidase that is released into circulation upon cell death. DPP3 is involved in the degradation of angiotensins, enkephalines, and endomorphines. It has been shown that circulating DPP3 (cDPP3) plasma concentration increases in cardiogenic shock (CS) patients and correlates with high mortality risk. Cardiogenic shock is a life-threatening syndrome associated with organ hypoperfusion. One of the common causes of CS is acute myocardial infarction (AMI). This study aimed to investigate if cDPP3 levels are associated with CS severity and the need for ventilation in patients suffering from CS. Fifteen patients with CS were included in this study. Six patients were invasively ventilated. The values of cDPP3 were higher in ventilated patients than in non-ventilated patients at admission, 3 h, and 24 h after admission in the intensive care unit. Patients with pulmonary hypertension at admission also showed high cDPP3 values at all time points. Furthermore, high cDPP3 levels were associated with reduced stroke volume. Our results suggest that cDPP3 could predict CS progression and guide therapy escalation.
NUMERICAL SOLUTION OF PARABOLIC EQUATIONS IN HIGH DIMENSIONS

We consider the numerical solution of diffusion problems in (0,T ) x Ω for and for T > 0 in dimension d d ≥ 1. We use a wavelet based sparse grid space discretization with mesh-width h and order p d ≥ 1, and hp discontinuous Galerkin time-discretization of order on a geometric sequence of many time steps. The linear systems in each time step are solved iteratively by GMRES iterations with a wavelet preconditioner. We prove that this algorithm gives an L 2 (Ω)-error of O(N-p ) for u(x,T) where N is the total number of operations, provided that the initial data satisfies with e > 0 and that u(x,t) is smooth in x for t>0 . Numerical experiments in dimension d up to 25 confirm the theory.
Modeling and design optimization of textile-like materials via homogenization and one-dimensional models of elasticity

The work consists of two parts. In the first part an optimization problem of structures of linear elastic material with contact modeled by Robin-type boundary conditions is considered. The structures model textile-like materials and possess certain quasiperiodicity properties. The homogenization method is used to represent the structures by homogeneous elastic bodies and is essential for formulations of the effective stress and Poisson's ratio optimization problems. At the micro-level, the classical one-dimensional Euler-Bernoulli beam model extended with jump conditions at contact interfaces is used. The stress optimization problem is of a PDE-constrained optimization type, and the adjoint approach is exploited. Several numerical results are provided. In the second part a non-linear model for simulation of textiles is proposed. The yarns are modeled by hyperelastic law and have no bending stiffness. The friction is modeled by the Capstan equation. The model is formulated as a problem with the rate-independent dissipation, and the basic continuity and convexity properties are investigated. The part ends with numerical experiments and a comparison of the results to a real measurement.
The Relevance of Serum Macrophage Migration Inhibitory Factor Level and Executive Function in Patients with White Matter Hyperintensity in Cerebral Small Vessel Disease

(1) Objective: To investigate the relationship between serum macrophage migration inhibitory factor (MIF) level and white matter hyperintensity (WMH) and executive function (EF) in cerebral small vascular disease (CSVD), and assess the impact and predictive value of MIF level and Fazekas scores in CSVD-related cognitive impairment (CI) (CSVD-CI); (2) Methods: A total of 117 patients with WMH admitted to the First Affiliated Hospital of Xinxiang Medical College from January 2022 to August 2022 were enrolled. According to the Montreal cognitive assessment (MoCA) scale, subjects were divided into a normal cognitive group and an impaired group. All subjects required serum MIF level, 3.0 T MRI, and neuropsychological evaluation to investigate the risk factors for CDVD-CI, analyze the correlation between MIF level, WMH, and EF, and to analyze the diagnostic value of MIF and WMH degree in predicting CSVD-CI; (3) Results: 1. Fazekas score and MIF level were the risk factors of CSVD-CI. 2. The Fazekas score was negatively correlated with MoCA score, positively correlated with Stroop C-Time, Stroop C-Mistake, Stroop interference effects (SIE)-Time, SIE-Mistake, and color trails test (CTT) interference effects (CIE) (B-A). 3. The MIF level was positively correlated with Fazekas score, Stroop C-Time, SIE-Time, CTT B-Time, and CIE (B-A), and negatively correlated with MoCA score. 4. Fazekas score and MIF level were significant factors for diagnosing CSVD-CI; (4) Conclusion: The Fazekas score and MIF level may be the risk factors of CSVD-CI, and they are closely correlated to CI, especially the EF, and they have diagnostic value for CSVD-CI.
Adaptive Symmetry Reference Trajectory Generation in Shared Autonomy for Active Knee Orthosis

Gait symmetry training plays an essential role in the rehabilitation of hemiplegic patients. Robotics-based gait training has been widely accepted by patients and clinicians. Reference trajectory generation for the affected side using the motion data of the unaffected side is an important way to achieve this. However, online generation of gait reference trajectory requires the algorithm to provide correct gait phase delay and could reduce the impact of measurement noise from sensors and input uncertainty from users. Based on an active knee orthosis (AKO) prototype, this work presents an adaptive symmetric gait trajectory generation framework for the gait rehabilitation of hemiplegic patients. Using the adaptive nonlinear frequency oscillators (ANFO) and movement primitives, we implement online gait pattern encoding and adaptive phase delay according to the real-time user input. A shared autonomy (SA) module with online input validation and arbitration has been designed to prevent undesired movements from being transmitted to the actuator on the affected side. The experimental results demonstrate the feasibility of the framework. Overall, this work suggests that the proposed method has the potential to perform gait symmetry rehabilitation in an unstructured environment and provide a kinematic reference for torque-assistance AKO.
Middleware for Data Intensive Analytics on HPC

1 EXTENDED ABSTRACT My research has focused on the design and development of MIddleware for Data-intensive Analytics and Science (MIDAS). MIDAS provides the necessary resource management capabilities which enable the use of Big Data frameworks on HPC resources. MIDAS has been used for two distinct application and analysis: First, it supports data-intensive analysis applications in conjunction with traditional HPC applications. It has enabled the analysis of biophysical simulations systems that were not feasible before. Second, MIDAS is being used to analyze high-resolution satellite imagery and derived products (such as digital elevation models). In addition to large volumes of data involved, the analysis of images is computationally intensive and requires high-performance and distributed resources. My research focuses on providing a common workload management middleware abstraction, on distributed resources, that will capture the commonalities of the data analysis between different domain sciences, and a resource management abstraction that will allow Big Data style analytics on HPCs in conjunction with HPC style simulations. One important component of these abstractions is the Pilot abstraction. The Pilot abstraction operates as a resource placeholder which can be used to execute a set of heterogeneous tasks within the placeholder on the same resource. I am using the Pilot-Abstraction and its implementation RADICAL-Pilot to support different types of data analysis as well as use it as a building block for the middleware abstraction defined before. Currently, I am a fourth year PhD candidate in the Electrical and Computer Engineering (ECE) department at Rutgers, the State University of New Jersey and a member of the RADICAL group, lead by Prof. Shantenu Jha. Prior to my PhD, I obtained my master degree in Integrated Hardware and Software Systems and a 5-year diploma (bachelor & masters of engineering) degree in Computer Engineering and Informatics from the University of Patras, Greece.
Examining Virginia’s African American History Course through the Lens of Racial Pedagogical Content Knowledge

Abstract During the 2020–2021 academic year, Virginia piloted a state-designed secondary African American history elective in 16 school divisions. Using the framework of Racial Pedagogical Content Knowledge (RPCK), this study examined the treatment of race in the new course by analyzing the state-created curriculum materials and interviewing three teachers that were part of the pilot program. Findings suggest that the curriculum challenged problematic traditional historic narratives, addressed issues of identity and structural racism, and applied racial knowledge through civic action projects. Teachers felt prepared to teach the course due to sustained racially conscious professional development facilitated by the Virginia Department of Education. The curriculum of the state-designed course and its implementation by teachers align with the core tenets of RPCK, such as its interrogation of power structures and inequalities, examination of intersectionality, and empowerment of students to resist racism and injustice through informed social action. Our analysis found the course does not use “inherently divisive concepts” as they are portrayed in Executive Order No. 1.
FINE TUNING OF DWELLING TIME IN FRICTION STIR WELDING OF AL 2024- T351 PLATES FOR PREVENTING BASE MATERIAL OVERHEATING, WELD TENSILE STRENGTH INCREASE AND WELD NUGGET SIZE DECREASE

After successful welding, destructive testing into test samples from AL 2024-T351 friction stir butt welds showed that tensile strength of the weld improve along the joint line, while dimensions of the weld nugget decrease. For those welds, both the base material and the welding tool constantly cool down during the welding phase. Obviously, the base material became overheated during the long dwelling phase what made conditions for creation of joints with the reduced mechanical properties. Preserving all process parameters but varying the dwelling time from 5 s to 27 s a new set of welding is done to reach maximal achievable tensile strength. An analytical-numerical-experimental model is used for optimising the duration of the dwelling time while searching for the maximal tensile strength of the welds.
Surge Current Interruption Capability of Discrete IGBT Devices in DC Hybrid Circuit Breakers

The power electronic interrupter (PEI) determines the current interruption rating of the dc hybrid circuit breaker (HCB). This article deals with discrete insulated gate bipolar transistor (IGBT)-based PEI modules. The influence of the voltage clamping circuit (VCC) on the surge current interruption capability (SCC) of the discrete IGBT is unveiled for the first time. Two commonly used VCC configurations are considered: a purely MOV-based voltage clamp and a MOV-resistor-capacitor(RC) combination clamp designated as types I and II PEI modules, respectively. Comprehensive measurements are used to analyze the device turnoff behavior under each PEI module type to determine their limitations, as well as their failure modes. The type I PEI is limited by the turnoff thermal stresses arising from the hard switching dynamics. The type II PEI, on the contrary, has the potential to achieve lower turnoff energy, among other benefits, but exhibits a unique failure mode during the tail current stage. Therefore, static and mixed-mode technology computer-aided design (TCAD) device simulations are introduced to provide further insights into the internal processes that alter the type II turnoff, and in turn, explain the failure mechanism. Finally, the influence of the various circuit parameters on the turnoff process is evaluated, and methods to enhance the SCC of the IGBT-based PEI are presented.
Lunar dust simulant charging and transport under UV irradiation in vacuum: Experiments and numerical modeling

Recent high‐altitude observations, made by the Lunar Dust Experiment (LDEX) experiment on board LADEE orbiting the Moon, indicate that high‐altitude (>10 km) dust particle densities are well correlated with interplanetary dust impacts. They show no evidence of high dust density suggested by Apollo 15 and 17 observations and possibly explained by electrostatic forces imposed by the plasma environment and photon irradiation. This paper deals with near‐surface conditions below the domain of observation of LDEX where electrostatic forces could clearly be at play. The upper and lower limits of the cohesive force between dusts are obtained by comparing experiments and numerical simulations of dust charging under ultraviolet irradiation in the presence of an electric field and mechanical vibrations. It is suggested that dust ejection by electrostatic forces is made possible by microscopic‐scale amplifications due to soil irregularities. At low altitude, this process may be complementary to interplanetary dust impacts.
Can We Predict Histological Images Using Mammograms ?

Along with the improvement in medical image analysis, exploring deep learning based approaches in the context of mammography and histology image processing has also become more realistic. We propose a computer based breast cancer modelling approach: “the Mammography–Histology–Linking–Model”, which develops a mapping of features between mammographic abnormalities and their histopathological representation. Potential clinical contribution of this model is that it avoids the needs for further biopsy if the abnormality is deemed benign by estimating and generating histological images.
Mathematical Biology is Good for Mathematics

A bout ten years ago I wrote an article, “Why is Mathematical Biology so Hard? ” for these Notices intending to explain why the applications of mathematics to biology would be very different than the traditional applications to physics and engineering [42]. A lot has happened since then. Mathematical biology has grown from a small field, containing relatively few mathematicians, to a major branch of applied mathematics. The reasons for this growth, which are implicit in the discussion below, are not the point of this article, nor do I want to encourage mathematicians to switch to mathematical biology. Rather, I want to make the case that mathematical biology benefits all mathematicians; it is good for the health of mathematics as a whole.
Shape analysis of amygdala atrophy using SPHARM-OT

We propose SPHARM-OT, an enhanced spherical harmonic (SPHARM) surface modeling method using optimal transport (OT) for spherical parameterization. To demonstrate its effectiveness, we apply it to shape analysis of amygdala atrophy in Alzheimer’s disease (AD). Of note, identifying morphological abnormalities of medial temporal structures such as hippocampus and amygdala is an important research topic for early diagnosis of AD. Our empirical study includes two steps: (1) the newly proposed SPHARM-OT method is used to model amygdala shapes of 101 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI); (2) using the random field theory to perform surface-based statistical analysis for detecting shape changes between cognitively normal (CN) and AD participants. We demonstrate that (1) the new SPHARM-OT method could effectively reduce surface mapping distortion and lead to a more accurate shape reconstruction result; and (2) significant shape changes between CN and AD participants are identified on certain amygdalar surface regions.