Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on vibration induced by the coupled heat and force due to rotor-to-stator rub

The paper sets up a set of coupled nonlinear differential equations with time variable coefficients to research the vibration of rotating machinery under rotor to stator rub condition. The seal stiffness, which is used to calculate rotor-stator contact force and rotor surface temperature distribution, is calculated using the finite element method firstly. Then, the transient heat conduction equations and rotor dynamic equations are combined together considering the thermal bending as unbalance fore acting on the rotor. The feedback from rub to vibration, asymmetric heat and shaft bending is analyzed by transient integration. The paper gives a detailed temperature distribution, thermal bending and vibration characteristics under different rotating speeds and rub conditions. It is found that rub-induced vibration tends to grow with time when the rotor is running below its critical speed. Rub section temperature difference grows quickly. Partial rub finally turns to full annular rub. Rub-induced vibration will be spiral and oscillation versus time if the rotor runs above the critical speed in the case of light rub. At severe rub, the vibration will be aggravated despite the rotor running above or under the critical speed. Two rub-induced vibration examples showing the coupled heat and force impact symptoms occurring in real turbines are given. Abnormal vibration phenomenon is analyzed and explained using the model.
Weighted Notions of Fairness with Binary Supermodular Chores

We study the problem of allocating indivisible chores among agents with binary supermodular cost functions. In other words, each chore has a marginal cost of $0$ or $1$ and chores exhibit increasing marginal costs (or decreasing marginal utilities). In this note, we combine the techniques of Viswanathan and Zick (2022) and Barman et al. (2023) to present a general framework for fair allocation with this class of valuation functions. Our framework allows us to generalize the results of Barman et al. (2023) and efficiently compute allocations which satisfy weighted notions of fairness like weighted leximin or min weighted $p$-mean malfare for any $p \ge 1$.
Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Review

Cancer has relational information residing at varying scales, modalities, and resolutions of the acquired data, such as radiology, pathology, genomics, proteomics, and clinical records. Integrating diverse data types can improve the accuracy and reliability of cancer diagnosis and treatment. There can be disease-related information that is too subtle for humans or existing technological tools to discern visually. Traditional methods typically focus on partial or unimodal information about biological systems at individual scales and fail to encapsulate the complete spectrum of the heterogeneous nature of data. Deep neural networks have facilitated the development of sophisticated multimodal data fusion approaches that can extract and integrate relevant information from multiple sources. Recent deep learning frameworks such as Graph Neural Networks (GNNs) and Transformers have shown remarkable success in multimodal learning. This review article provides an in-depth analysis of the state-of-the-art in GNNs and Transformers for multimodal data fusion in oncology settings, highlighting notable research studies and their findings. We also discuss the foundations of multimodal learning, inherent challenges, and opportunities for integrative learning in oncology. By examining the current state and potential future developments of multimodal data integration in oncology, we aim to demonstrate the promising role that multimodal neural networks can play in cancer prevention, early detection, and treatment through informed oncology practices in personalized settings.
Rogue waves generation in a left-handed nonlinear transmission line with series varactor diodes

We investigate the electromagnetic wave behavior and its characterization using collective variables technique. Second-order dispersion, first- and second-order nonlinearities, which strongly act in a left-handed nonlinear transmission line with series varactor diodes, are taken into account. Four frequency ranges have been found. The first one gives the so-called energetic soliton due to a perfect combination of second-order dispersion and first-order nonlinearity. The second frequency range presents a dispersive soliton leading to the collapse of the electromagnetic wave at the third frequency range. But the fourth one shows physical conditions which are able to provoke the appearance of wave trains generation with some particular waves, the rogue waves. Moreover, we demonstrate that the number of rogue waves increases with frequency. The soliton, thereafter, gains a relative stability when second-order nonlinearity comes into play with some specific values in the fourth frequency range. Furthermore, the stability conditions of the electromagnetic wave at high frequencies have been also discussed.
Experimental Study of Aerodynamic Noise vs Drag Relationships for Circular Cylinders

An experimental program has been conducted to determine the quantitative relationship between far-field noise and drag coefficient for circular cylinders. The test program included smooth and roughened cylinders with Reynolds numbers between 45,000 and 450,000 and Mach numbers between 0.1 and 0.5. The resulting drag coefficients varied between 0.75 and 1.2. The results show a strong dependence of SPL on drag coefficient varying as 50 to 90 times log (CD) depending on directivity angle and bandwidth. This represents the first controlled laboratory data available to directly verify the quantitative relationship between noise and drag coefficient at constant Mach numbers. aF b
Learning Grounded Vision-Language Representation for Versatile Understanding in Untrimmed Videos

Joint video-language learning has received increasing attention in recent years. However, existing works mainly focus on single or multiple trimmed video clips (events), which makes human-annotated event boundaries necessary during inference. To break away from the ties, we propose a grounded vision-language learning framework for untrimmed videos, which automatically detects informative events and effectively excavates the alignments between multi-sentence descriptions and corresponding event segments. Instead of coarse-level video-language alignments, we present two dual pretext tasks to encourage fine-grained segment-level alignments, i.e., text-to-event grounding (TEG) and event-to-text generation (ETG). TEG learns to adaptively ground the possible event proposals given a set of sentences by estimating the cross-modal distance in a joint semantic space. Meanwhile, ETG aims to reconstruct (generate) the matched texts given event proposals, encouraging the event representation to retain meaningful semantic information. To encourage accurate label assignment between the event set and the text set, we propose a novel semantic-aware cost to mitigate the sub-optimal matching results caused by ambiguous boundary annotations. Our framework is easily extensible to tasks covering visually-grounded language understanding and generation. We achieve state-of-the-art dense video captioning performance on ActivityNet Captions, YouCook2 and YouMakeup, and competitive performance on several other language generation and understanding tasks. Our method also achieved 1st place in both the MTVG and MDVC tasks of the PIC 4th Challenge. Our code is publicly available at https://github.com/zjr2000/GVL.
Scope and Arbitration in Machine Learning Clinical EEG Classification

A key task in clinical EEG interpretation is to classify a recording or session as normal or abnormal. In machine learning approaches to this task, recordings are typically divided into shorter windows for practical reasons, and these windows inherit the label of their parent recording. We hypothesised that window labels derived in this manner can be misleading – for example, windows without evident abnormalities can be labelled ‘abnormal’ – disrupting the learning process and degrading performance. We explored two separable approaches to mitigate this problem: increasing the window length and introducing a second-stage model to arbitrate between the window-specific predictions within a recording. Evaluating these methods on the Temple University Hospital Abnormal EEG Corpus, we significantly improved state-of-the-art average accuracy from 89.8 percent to 93.3 percent. This result defies previous estimates of the upper limit for performance on this dataset and represents a major step towards clinical translation of machine learning approaches to this problem. Our study includes electroencephalography (EEG) datasets collected from https://isip.piconepress. com/projects/tuh\_eeg/. Our code is shared on https://github.con zhuyixuanl997/ EEGScopeAndArbitration.
Lower negative bounds on the static electric susceptibility of nonequilibrium cubic crystals

We use a classical, microscopic model of point-like dipolarizable entities (a model that is standard in the case of positive polarizability) and investigate its behavior for simple cubic (sc), body-centered cubic (bcc), and face-centered cubic (fcc) crystals with one entity per primitive cell when the static polarizability of the entities is negative and the mutual interaction between the entities is taken into account. We find that the static electric susceptibility is bounded below due to an instability towards self-polarization but the lower permissible bound is negative definite in each case, i.e., the concept of negative static electric susceptibility remains robust, according to the model, when mutual interactions are taken into account. The usual Clausius-Mossotti relation between the static polarizability and the static electric susceptibility remains valid in the case of negative parameters, but only down to the lower permissible bound; the value of the bound depends on the crystal structure and is always unrelated to the asymptote of the Clausius-Mossotti curve. The lower permissible bounds of the static electric susceptibility are found to be -0.906 for sc, and -1.00 for bcc and fcc. These results confirm that, although the magnitude of the static electric susceptibility does not diverge in the negative case (as it can in the positive case), the magnitudes attainable in the negative case for condensed media may, nevertheless, be many orders of magnitude greater than those predicted previously for inverted vapors and gases. This is a promising result in relation to the development of potential new technologies that exploit the phenomenon.
Semiclassical theory of frequency combs generated by parametric modulation of optical microresonators

An optical microresonator, which parameters are periodically modulated in time, can generate optical frequency comb (OFC) spectral resonances equally spaced by the modulation frequency. Significant recent progress in realization of OFC generators based on the modulation of microresonator parameters boosted interest to their further experimental development and theoretical understanding of underlying phenomena. However, most of theoretical approaches developed to date were based on the lumped parameter models which unable to evaluate, analyse, and optimize the effect of spatial distribution of modulation inside microresonators. Here we develop the multi-quantum semiclassical theory of parametrically excited OFCs which solves these problems. As an application, we compare OFCs which are resonantly or adiabatically excited in a racetrack microresonator (RTM) and a Surface Nanoscale Axial Photonics (SNAP) bottle microresonator (SBM). The principal difference between these two types of microresonators consists in much slower propagation speed of whispering gallery modes along the SBM axis compared to the speed of modes propagating along the RTM waveguide axis. We show that, due to this difference, similar OFCs can be generated by an SBM with a much smaller size compared to that of the RTM. Based on the developed theory, we analytically express the OFC spectrum of microresonators through the spatial distribution of modulated parameters and optimize this distribution to arrive at the strongest OFCs generated with minimum power consumption.
Inertial Migration in Micro-Centrifuge Devices

Within microcentrifuge devices, a microfluidic vortex separates larger particles from a heterogeneous suspension using inertial migration, a phenomenon that causes particles to migrate across streamlines. The ability to selectively capture particles based on size differences of a few microns makes microcentrifuges useful diagnostic tools for trapping rare cells within blood samples. However, rational design of microcentrifuges has been held back from its full potential by a lack of quantitative modeling of particle capture mechanics. Here we use an asymptotic method, in which particles are accurately modeled as singularities in a linearized flow field, to rapidly calculate particle trajectories within microcentrifuges. Our predictions for trapping thresholds and trajectories agree well with published experimental data. Our results clarify how capture reflects a balance between advection of particles within a background flow and their inertial focusing and shows why the close proximity of trapped and untrapped incoming streamlines makes it challenging to design microcentrifuges with sharp trapping thresholds.
Recipro-CAM: Fast gradient-free visual explanations for convolutional neural networks

The Convolutional Neural Network (CNN) is a widely used deep learning architecture for computer vision. However, its black box nature makes it difficult to interpret the behavior of the model. To mitigate this issue, AI practitioners have explored explainable AI methods like Class Activation Map (CAM) and Grad-CAM. Although these methods have shown promise, they are limited by architectural constraints or the burden of gradient computing. To overcome this issue, Score-CAM and Ablation-CAM have been proposed as gradient-free methods, but they have longer execution times compared to CAM or Grad-CAM based methods, making them unsuitable for real-world solution though they resolved gradient related issues and enabled inference mode XAI. To address this challenge, we propose a fast gradient-free Reciprocal CAM (Recipro-CAM) method. Our approach involves spatially masking the extracted feature maps to exploit the correlation between activation maps and network predictions for target classes. Our proposed method has yielded promising results, outperforming current state-of-the-art method in the Average Drop-Coherence-Complexity (ADCC) metric by $1.78 \%$ to $3.72 \%$, excluding VGG-16 backbone. Moreover, Recipro-CAM generates saliency maps at a similar rate to Grad-CAM and is approximately $148$ times faster than Score-CAM. The source code for Recipro-CAM is available in our data analysis framework.
Closed-form analytical solution for the transfer matrix based on Pendry-MacKinnon discrete Maxwell's equations

Pendry and MacKinnon meaningful discretization of Maxwell's equations was put forward specifically as part of a finite-element numerical algorithm. By contrast with a numerical approach, in the same spirit evoked by the relationships between the difference and the differential equations, we provide an analytical solution for the transfer matrix elements generated by this discretization of Maxwell's equations. The method, valid for all modes and any non-magnetic pattern with frequency-dependent permittivities, is exemplified for a bilaminar structure. The results from hundreds of path-operators simplify to a small number of propagation channels, which transfer the electromagnetic field between input and output. A specific bilaminar device is design-optimized and used to generate a topological map from permittivities to the number of modes and very high Q-factor resonances.
Non‐Fermi‐Liquid Behavior of Superconducting SnH4

The chemical interaction of Sn with H2 by X‐ray diffraction methods at pressures of 180–210 GPa is studied. A previously unknown tetrahydride SnH4 with a cubic structure (fcc) exhibiting superconducting properties below TC = 72 K is obtained; the formation of a high molecular C2/m‐SnH14 superhydride and several lower hydrides, fcc SnH2, and C2‐Sn12H18, is also detected. The temperature dependence of critical current density JC(T) in SnH4 yields the superconducting gap 2Δ(0) = 21.6 meV at 180 GPa. SnH4 has unusual behavior in strong magnetic fields: B,T‐linear dependences of magnetoresistance and the upper critical magnetic field BC2(T) ∝ (TC – T). The latter contradicts the Wertheimer–Helfand–Hohenberg model developed for conventional superconductors. Along with this, the temperature dependence of electrical resistance of fcc SnH4 in non‐superconducting state exhibits a deviation from what is expected for phonon‐mediated scattering described by the Bloch‐Grüneisen model and is beyond the framework of the Fermi liquid theory. Such anomalies occur for many superhydrides, making them much closer to cuprates than previously believed.
Evidence for the Self-organized Criticality Phenomenon in the Prompt Phase of Short Gamma-Ray Bursts

The prompt phase of gamma-ray bursts (GRBs) contains essential information regarding their physical nature and central engine, which are as yet unknown. In this paper, we investigate the self-organized criticality phenomenon in GRB prompt phases as done in X-ray flares of GRBs. We obtain the differential and cumulative distributions of 243 short GRB pulses, such as peak flux, FWHM, rise time, decay time, and peak time in the fourth BATSE Time-Tagged Event Catalog with the Markov Chain Monte Carlo technique. It is found that these distributions can be well described by power-law models. In particular, comparisons are made with 182 short GRB pulses in the third Swift GRB Catalog from 2004 December to 2019 July. The results are essentially consistent with the BATSE ones. We notice that there is no obvious power-law index evolution across different energy bands for either BATSE or Swift short GRBs. The joint analysis suggests that the GRB prompt phase can be explained by a fractal-diffusive self-organized criticality system with the spatial dimension S = 3 and the classical diffusion β = 1. Our findings show that GRB prompt phases and X-ray flares possess the very same magnetically dominated stochastic process and mechanism.
Small Fermi Pockets Intertwined with Charge Stripes and Pair Density Wave Order in a Kagome Superconductor

The kagome superconductor family AV3Sb5 (A=Cs, K, Rb) emerged as an exciting platform to study exotic Fermi surface instabilities. Here we use spectroscopic-imaging scanning tunneling microscopy (SI-STM) and angle-resolved photoemission spectroscopy (ARPES) to reveal how the surprising cascade of higher and lower-dimensional density waves in CsV3Sb5 is intimately tied to a set of small reconstructed Fermi pockets. ARPES measurements visualize the formation of these pockets generated by a 3D charge density wave transition. The pockets are connected by dispersive q* wave vectors observed in Fourier transforms of STM differential conductance maps. As the additional 1D charge order emerges at a lower temperature, q* wave vectors become substantially renormalized, signaling further reconstruction of the Fermi pockets. Remarkably, in the superconducting state, the superconducting gap modulations give rise to an in-plane Cooper pair-density-wave at the same q* wave vectors. Our work demonstrates the intrinsic origin of the charge-stripes and the pair-density-wave in CsV3Sb5 and their relationship to the Fermi pockets. These experiments uncover a unique scenario of how Fermi pockets generated by a parent charge density wave state can provide a favorable platform for the emergence of additional density waves.
No-regret Algorithms for Fair Resource Allocation

We consider a fair resource allocation problem in the no-regret setting against an unrestricted adversary. The objective is to allocate resources equitably among several agents in an online fashion so that the difference of the aggregate $\alpha$-fair utilities of the agents between an optimal static clairvoyant allocation and that of the online policy grows sub-linearly with time. The problem is challenging due to the non-additive nature of the $\alpha$-fairness function. Previously, it was shown that no online policy can exist for this problem with a sublinear standard regret. In this paper, we propose an efficient online resource allocation policy, called Online Proportional Fair (OPF), that achieves $c_\alpha$-approximate sublinear regret with the approximation factor $c_\alpha=(1-\alpha)^{-(1-\alpha)}\leq 1.445,$ for $0\leq \alpha<1$. The upper bound to the $c_\alpha$-regret for this problem exhibits a surprising phase transition phenomenon. The regret bound changes from a power-law to a constant at the critical exponent $\alpha=\frac{1}{2}. $ As a corollary, our result also resolves an open problem raised by Even-Dar et al. [2009] on designing an efficient no-regret policy for the online job scheduling problem in certain parameter regimes. The proof of our results introduces new algorithmic and analytical techniques, including greedy estimation of the future gradients for non-additive global reward functions and bootstrapping adaptive regret bounds, which may be of independent interest.
Semi-Quantitative Analysis and Serological Evidence of Hepatitis A Virus IgG Antibody among children in Rumuewhor, Emuoha, Rivers State, Nigeria

Hepatitis A virus (HAV) infection has been greatly reduced in most developed countries through the use of vaccine and improved hygienic conditions. However, the magnitude of the problem is underestimated and there are no well-established Hepatitis A virus prevention and control strategies in Nigeria. The aim of this study was to determine the prevalence of Hepatitis A virus infection among children aged 2 to 9 years in Rumuewhor, Emuoha LGA, Rivers State, Nigeria. Blood samples were collected from the 89 children enrolled in this study, and analyzed for the presence of HAV IgG antibodies using ELISA techniques. Of the 89 participants, 22 (24.7%) tested positive for HAV IgG antibodies, while 67 (75.3%) were negative. The children within the ages of 4 to 6 years had the highest seropositivity rate (33.3%) while those less than 4 years had the least seropositivity rate (22.4%). The prevalence rate ratio of the males to females was 1:1.3. There was no significant difference (p between IgG seropositivity and age groups and gender. However, there was a statistical association of IgG seropositivity rates with respect to immunization. The seroprevalence rate recorded in this study was significant, indicating that the virus is endemic in this study area. Proper awareness, health education and vaccination are imperative to controlling and preventing HAV infection in Rumuewhor, Emuoha, Rivers State, Nigeria.