Miracle-dz/UniFAR-FTUs huggingface dataset:
P3DC-Shot: Prior-Driven Discrete Data Calibration for Nearest-Neighbor Few-Shot Classification

Nearest-Neighbor (NN) classification has been proven as a simple and effective approach for few-shot learning. The query data can be classified efficiently by finding the nearest support class based on features extracted by pretrained deep models. However, NN-based methods are sensitive to the data distribution and may produce false prediction if the samples in the support set happen to lie around the distribution boundary of different classes. To solve this issue, we present P3DC-Shot, an improved nearest-neighbor based few-shot classification method empowered by prior-driven data calibration. Inspired by the distribution calibration technique which utilizes the distribution or statistics of the base classes to calibrate the data for few-shot tasks, we propose a novel discrete data calibration operation which is more suitable for NN-based few-shot classification. Specifically, we treat the prototypes representing each base class as priors and calibrate each support data based on its similarity to different base prototypes. Then, we perform NN classification using these discretely calibrated support data. Results from extensive experiments on various datasets show our efficient non-learning based method can outperform or at least comparable to SOTA methods which need additional learning steps.
WiFi Physical Layer Stays Awake and Responds When it Should Not

WiFi communication should be possible only between devices inside the same network. However, we find that all existing WiFi devices send back acknowledgments (ACKs) to even fake packets received from unauthorized WiFi devices outside of their network. Moreover, we find that an unauthorized device can manipulate the power-saving mechanism of WiFi radios and keep them continuously awake by sending specific fake beacon frames to them. Our evaluation of over 5000 devices from 186 vendors confirms that these are widespread issues. We believe these loopholes cannot be prevented, and hence they create privacy and security concerns. Finally, to show the importance of these issues and their consequences, we implement and demonstrate two attacks where an adversary performs battery drain and WiFi sensing attacks just using a tiny WiFi module which costs less than ten dollars.
On the Arrow–Hurwicz differential system for linearly constrained convex minimization

In a real Hilbert space setting, we reconsider the classical Arrow-Hurwicz differential system in view of solving linearly constrained convex minimization problems. We investigate the asymptotic properties of the differential system and provide conditions for which its solutions converge towards a saddle point of the Lagrangian associated with the convex minimization problem. Our convergence analysis mainly relies on a `Lagrangian identity' which naturally extends on the well-known descent property of the classical continuous steepest descent method. In addition, we present asymptotic estimates on the decay of the solutions and the primal-dual gap function measured in terms of the Lagrangian. These estimates are further refined to the ones of the classical damped harmonic oscillator provided that second-order information on the objective function of the convex minimization problem is available. Finally, we show that our results directly translate to the case of solving structured convex minimization problems. Numerical experiments further illustrate our theoretical findings.
Simulated annealing, optimization, searching for ground states

The chapter starts with a historical summary of first attempts to optimize the spin glass Hamiltonian, comparing it to recent results on searching largest cliques in random graphs. Exact algorithms to find ground states in generic spin glass models are then explored in Section 1.2, while Section 1.3 is dedicated to the bidimensional case where polynomial algorithms exist and allow for the study of much larger systems. Finally Section 1.4 presents a summary of results for the assignment problem where the finite size corrections for the ground state can be studied in great detail.
Source-Free Unsupervised Domain Adaptation: A Survey

Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention for tackling domain-shift problems caused by distribution discrepancy across different domains. Existing UDA approaches highly depend on the accessibility of source domain data, which is usually limited in practical scenarios due to privacy protection, data storage and transmission cost, and computation burden. To tackle this issue, many source-free unsupervised domain adaptation (SFUDA) methods have been proposed recently, which perform knowledge transfer from a pre-trained source model to the unlabeled target domain with source data inaccessible. A comprehensive review of these works on SFUDA is of great significance. In this paper, we provide a timely and systematic literature review of existing SFUDA approaches from a technical perspective. Specifically, we categorize current SFUDA studies into two groups, i.e., white-box SFUDA and black-box SFUDA, and further divide them into finer subcategories based on different learning strategies they use. We also investigate the challenges of methods in each subcategory, discuss the advantages/disadvantages of white-box and black-box SFUDA methods, conclude the commonly used benchmark datasets, and summarize the popular techniques for improved generalizability of models learned without using source data. We finally discuss several promising future directions in this field.
Credible Remote Sensing Scene Classification Using Evidential Fusion on Aerial-Ground Dual-view Images

Due to their ability to offer more comprehensive information than data from a single view, multi-view (e.g., multi-source, multi-modal, multi-perspective) data are being used more frequently in remote sensing tasks. However, as the number of views grows, the issue of data quality is becoming more apparent, limiting the potential benefits of multi-view data. Although recent deep neural network (DNN)-based models can learn the weight of data adaptively, a lack of research on explicitly quantifying the data quality of each view when fusing them renders these models inexplicable, performing unsatisfactorily and inflexibly in downstream remote sensing tasks. To fill this gap, in this paper, evidential deep learning is introduced to the task of aerial-ground dual-view remote sensing scene classification to model the credibility of each view. Specifically, the theory of evidence is used to calculate an uncertainty value which describes the decision-making risk of each view. Based on this uncertainty, a novel decision-level fusion strategy is proposed to ensure that the view with lower risk obtains more weight, making the classification more credible. On two well-known, publicly available datasets of aerial-ground dual-view remote sensing images, the proposed approach achieves state-of-the-art results, demonstrating its effectiveness.
Online Linearized LASSO

Sparse regression has been a popular approach to perform variable selection and enhance the prediction accuracy and interpretability of the resulting statistical model. Existing approaches focus on offline regularized regression, while the online scenario has rarely been studied. In this paper, we propose a novel online sparse linear regression framework for analyzing streaming data when data points arrive sequentially. Our proposed method is memory efficient and requires less stringent restricted strong convexity assumptions. Theoretically, we show that with a properly chosen regularization parameter, the $\ell_2$-norm statistical error of our estimator diminishes to zero in the optimal order of $\tilde{O}({\sqrt{s/t}})$, where $s$ is the sparsity level, $t$ is the streaming sample size, and $\tilde{O}(\cdot)$ hides logarithmic terms. Numerical experiments demonstrate the practical efficiency of our algorithm.
On the Generalized Hamming Weights of Hyperbolic Codes

A hyperbolic code is an evaluation code that improves a Reed-Muller because the dimension increases while the minimum distance is not penalized. We give the necessary and sufficient conditions, based on the basic parameters of the Reed-Muller, to determine whether a Reed-Muller coincides with a hyperbolic code. Given a hyperbolic code, we find the largest Reed-Muller containing the hyperbolic code and the smallest Reed-Muller in the hyperbolic code. We then prove that similarly to Reed-Muller and Cartesian codes, the $r$-th generalized Hamming weight and the $r$-th footprint of the hyperbolic code coincide. Unlike Reed-Muller and Cartesian, determining the $r$-th footprint of a hyperbolic code is still an open problem. We give upper and lower bounds for the $r$-th footprint of a hyperbolic code that, sometimes, are sharp.
Mechanics of rollable and foldable film-on-foil electronics

The mechanics of film-on-foil devices is presented in the context of thin-film transistors on steel and plastic foils. Provided the substrates are thin, such transistors function well after the foils are rolled to small radii of curvature. When a substrate with a lower elastic modulus is used, smaller radii of curvature can be achieved. Furthermore, when the transistors are placed in the neutral surface by sandwiching between a substrate and an encapsulation layer, even smaller radii of curvature can be attained. Transistor failure clearly shows when externally forced and thermally induced strains add to, or subtract from, each other.
Large‐amplitude transmitter‐associated and lightning‐associated whistler waves in the Earth's inner plasmasphere at L < 2

[1] We report observations of very large amplitude whistler mode waves in the Earth's nightside inner radiation belt enabled by the STEREO Time Domain Sampler. Amplitudes range from 30–110 mV/m (zero-peak), 2 to 3 orders of magnitude larger than previously observed in this region. Measurements from the peak electric field detector (TDSMax) indicate that these large-amplitude waves are prevalent throughout the plasmasphere. A detailed examination of high time resolution electric field waveforms is undertaken on a subset of these whistlers at L 100 keV) electrons on a time scale of <1 s and thus may be an important previously unaccounted for source of energization or pitch-angle scattering in the inner radiation belt.
Effects of Multimodal Therapy, Blinding, and Multi-laboratory Protocol Conduct on the Robustness of the Rat Model of Adjuvant Induced Arthritis

Abstract Background/Aim: The aim of this study was to investigate the effects of multimodal therapy comprising buprenorphine (BUP) and indomethacin (IND) on key translational parameters in the rat adjuvant induced arthritis (AIA) model. Furthermore, we investigated the difference between visual assessment scores and histology scores generated by blinded and non-blinded assessors and the robustness and generalizability of results by conducting a multi-laboratory study. Materials and Methods: The experiment was terminated on day 26 after 11 days (days 15-25) of voluntarily ingested buprenorphine and 7 days of gavage delivered indomethacin treatment (days 19-25). The treatment effects were assessed on the last day of the study, relying on body weight assessment, serum concentrations of α1- acid glycoprotein, and assessment of affected hind paws swelling, in-life and post mortem. Results: Across two laboratories, the combined analgesic treatments had minimal effects on the measured model parameters indicating that multimodal treatment did not affect the translatability of the model. We found an improvement in clinical scores (a negative change in scores) in nearly all medicated animals when scored informed, whereas it was essentially 50:50 for the blinded scorings and no difference between the blinded and informed histological scoring. Conclusion: The present results support the use of more effective analgesic treatment regimens and the good practice recommendations advocating blinding as a mandatory practice in conduct of preclinical in vivo efficacy studies. In spite of minor differences between results obtained at the two sites, there was good agreement between them indicating robustness of the AIA model.
Helicity multiplexed wavefront shaping at broadband ultraviolet regime

Here in this paper, we proposed a metasurface, which consists of silicon nitride as dielectric material possessing a high transparent window in the ultraviolet regime. We designed a single-layer dual-band metasurface instead of stacking and interleaved technique to overcome noise and low resolution, which gives broadband response for UV wavelengths. The proposed spin multiplexed metasurface is capable of generating two independent holographic images for right circularly polarized (RCP) and left circularly polarized (LCP) light. We achieved maximum cross-polarization efficiency on the designed wavelength i.e. 350nm and negligible zero-order efficiency. The unit cell and the proposed metasurface are simulated using Finite Difference Time Domain-based FDTD Solution from Lumerical Inc. for the specific wavelength while it gives broadband response for the wavelength band almost covering 290nm to 390nm.
Double conditioning of reactive scalar transport equations in turbulent nonpremixed flames

Double conditioning of reactive scalar transport equations on mixture fraction and sensible enthalpy is presented as a promising extension to conventional conditional moment closure (CMC) methods. Simple first order, singly conditioned CMC cannot accurately predict combustion phenomena such as partially premixed flames and flames with local extinction and reignition. CMC is applied to turbulent flames with significant temperature fluctuations that bring to light the deficiencies of the conventional CMC approach. Fluctuations of temperature and species around their conditional means cannot be neglected when these quantities are conditioned on mixture fraction only. Comparison with direct numerical simulation (DNS) results demonstrates, however, the great potential for doubly conditioned CMC. Doubly conditioned CMC gives excellent agreement with DNS at all times and it captures phenomena like local extinction and the onset of reignition accurately.
High contrast, ultrafast optically addressed ultraviolet light modulator based upon optical anisotropy in ZnO films grown on R-plane sapphire

An optically addressed ultraviolet light modulator has been demonstrated which exploits the optical anisotropy in a ZnO film epitaxially grown on (0112) sapphire. This device achieves both high contrast and high speed by exploiting the anisotropic bleaching of the anisotropic absorption and concomitant ultrafast polarization rotation near the lowest exciton resonances produced by femtosecond ultraviolet pulses. The resultant modulation is characterized by a contrast ratio of 70:1, corresponding to a dynamic polarization rotation of 12°, and it decays to a quasiequilibrium value within 100 ps.
Structural Characterization, Thermal Stability and Non-Isothermal Kinetic Analysis of Decomposition of Poly(methylmethacrylate) and Titanium Dioxide Nanocomposites

Abstract In this study poly(methylmethacrylate) (PMMA) and the corresponding nanocomposites loaded with titanium dioxide (TiO2) films were prepared by a solution casting method. All materials were characterized by Fourier transform infrared spectroscopy, UV-visible spectroscopy, X-ray diffraction and scanning electron microscopy. The thermal properties were investigated by differential scanning calorimetry and thermogravimetric analysis. The results showed that the incorporation of TiO2 nanoparticles in PMMA decreased the thermal stability of the polymer. Moreover, the kinetic study of the thermal degradation showed that the apparent activation energies of the nanocomposites PMMA/TiO2 were lower compared to the pure PMMA, confirming the catalytic effect of TiO2 nanoparticles on the PMMA thermal decomposition. Furthermore, g(α), which is the integral form of the reaction mechanism function, showed that the degradation mechanism of the PMMA nanocomposites was strongly dependent on the TiO2 amount introduced into their matrices. Finally, the degradation mechanism of the pure PMMA obeyed the R3 model corresponding to a phase boundary reaction (n = 3), whereas in the case of the PMMA/TiO2 nanocomposites, they obeyed the R2 model, corresponding to a phase boundary reaction (n = 2), for PMMA/TiO2 (3%) and an A1.5 model, corresponding to an Avrami-Erofeev correlation (m = 1.5), was obtained for the remaining nanocomposites PMMA/TiO2 (5, 7 and 10%).
Geometric theory of dynamical systems : an introduction

1 Differentiable Manifolds and Vector Fields. - 0 Calculus in ? n and Differentiable Manifolds. - 1 Vector Fields on Manifolds. - 2 The Topology of the Space of Cr Maps. - 3 Transversality. - 4 Structural Stability. - 2 Local Stability. - 1 The Tubular Flow Theorem. - 2 Linear Vector Fields. - 3 Singularities and Hyperbolic Fixed Points. - 4 Local Stability. - 5 Local Classification. - 6 Invariant Manifolds. - 7 The ? -lemma (Inclination Lemma). Geometrical Proof of Local Stability. - 3 The Kupka-Smale Theorem. - 1 The Poincare Map. - 2 Genericity of Vector Fields Whose Closed Orbits Are Hyperbolic. - 3 Transversality of the Invariant Manifolds. - 4 Genericity and Stability of Morse-Smale Vector Fields. - 1 Morse-Smale Vector Fields Structural Stability. - 2 Density of Morse-Smale Vector Fields on Orientable Surfaces. - 3 Generalizations. - 4 General Comments on Structural Stability. Other Topics. - Appendix: Rotation Number and Cherry Flows. - References.
Accuracy of functional gene community detection in Saccharomyces cerevisiae by maximizing Generalized Modularity Density

Identifying functionally-cohesive gene communities from large data sets of expression data for individual genes is a key approach to understanding the molecular components of biological processes. Here, we compare the accuracy of twelve different approaches to infer gene co-expression networks and then find gene communities within the networks. Among the approaches used are ones involving a recently developed clustering method that identifies communities by maximizing Generalized Modularity Density (Qg). RNA-Seq data from 691 samples of S. cerevisiae (yeast) are analyzed. These data have been obtained from organisms grown under diverse environmental and developmental conditions and encompass varied mutant lines. To assess the accuracy of different approaches, we introduce a statistical measure, the Average Adjusted Rand Index (AARI) score, which compares their results to Gene Ontology (GO) term associations. Inferring gene networks using the Context Likelihood of Relatedness (CLR) and subsequently clustering by maximizing Generalized Modularity Density is found to identify the most significant functional communities. Also, to quantify the extent to which the identified communities are biologically relevant, a GO term enrichment analysis is performed. The results indicate that many of the communities found by maximizing Generalized Modularity Density are enriched in genes with known biological functions. Furthermore, some of the communities contain genes of unknown function, enabling inference of potentially novel functional interactions involving these genes. Furthermore, some genes are species-specific orphan genes; assignment of these orphan genes to communities enriched in a particular biological process provides a method to infer the biological process in which they are involved. We focus on a few communities that are highly significantly enriched in a particular biological process, and develop experimentally-testable predictions about the orphan genes in these communities. Author summary Finding gene communities that are of biological relevance from expression profiles of individual genes is a critical approach to understanding biological processes and their molecular components. Various computational methods have been developed to infer underlying metabolic and regulatory networks and to identify functional communities of genes. Which network inference and clustering methods works best to achieve this goal has largely remained an open question. Here, using genome-wide transcriptomic data for S. cerevisiae, we systematically compare the effectiveness of several commonly used network inference and clustering methods. We rank these methods by comparing the clusters obtained by different methods to Gene Ontology (GO) terms. We find that inferring gene networks using a method known as the Context Likelihood of Relatedness (CLR) and subsequently clustering by maximizing Generalized Modularity Density identifies the most significant functional communities.
Quantifying coregistration errors in spectral imaging

It is well known that in multi- and hyperspectral imaging, the spatial coregistration of bands is important for the quality of image data. In practice, all spectral imaging sensors exhibit some degree of coregistration error, so that in any given pixel, different bands have a somewhat different "footprint" in the scene. Such coregistration errors may be in the form of differences in the position, size and shape of the spatial responsivity distribution, here termed pixel response function (PRF). There appears to be no standardized way to quantify the combined effect of these errors. This paper proposes a common metric for different types of coregistration error. Basically, the metric is the integrated difference between the PRFs of two bands in a given pixel. It is shown that under reasonable assumptions, this metric reflects the worst-case error in the signal resulting from coregistration errors between the two bands. To specify the coregistration of multiple bands, or in multiple pixels, an aggregate metric can be defined. The metric may be used for design optimization and should also be experimentally measurable. Extension to spectral and temporal coregistration is briefly discussed. The metric is proposed as a standardized way to report the coregistration performance of spectral image sensors.
Closing the Bio-Silicon Loop for Cellular Neural Prosthesis using FPGA-based Iono-Neuromorphic Models

Neural ptosthetic devices offer the ability to develop novel treatments for previously incurable diseases and ailments, such as deafness, blindness and tetraplepia. There is the potential to extend this concept to incorporate cognitive prosthetics, whereby damaged individual neuron cells or larger brain regiops are substituted by silicon neurons, in order to overcome conditions such as stroke or epilepsy. The development of such applications relies heavily upon efficient, scalable and powerful technological platforms, particularly systems capable of running large-scale neural models. The advancemente in fLeld-peogrammable gate array (FPGA) tnchnology provides an excellent foundation for development of these neural models with the same cost of software-based architectures, but with the performance of close to a dedicated hardware system. This paper illustrates the design of a programmable FPGA-based neural model, which is capable of simulating a large range of ion-channel dynamics and delivering biologically realistic network models. Through comparisons with alternative implementations the proposed model is determined to be more scalable and more computationally efficient. We implemented a hybrid bio-silicon syttem to demonstrate thp ability of silicon devices to provide cellular rehabilitation, restoring thn functionality of a damaged biological network.