Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalable Attribution of Adversarial Attacks via Multi-Task Learning

Deep neural networks (DNNs) can be easily fooled by adversarial attacks during inference phase when attackers add imperceptible perturbations to original examples, i.e., adversarial examples. Many works focus on adversarial detection and adversarial training to defend against adversarial attacks. However, few works explore the tool-chains behind adversarial examples, which can help defenders to seize the clues about the originator of the attack, their goals, and provide insight into the most effective defense algorithm against corresponding attacks. With such a gap, it is necessary to develop techniques that can recognize tool-chains that are leveraged to generate the adversarial examples, which is called Adversarial Attribution Problem (AAP). In this paper, AAP is defined as the recognition of three signatures, i.e., {\em attack algorithm}, {\em victim model} and {\em hyperparameter}. Current works transfer AAP into single label classification task and ignore the relationship between these signatures. The former will meet combination explosion problem as the number of signatures is increasing. The latter dictates that we cannot treat AAP simply as a single task problem. We first conduct some experiments to validate the attributability of adversarial examples. Furthermore, we propose a multi-task learning framework named Multi-Task Adversarial Attribution (MTAA) to recognize the three signatures simultaneously. MTAA contains perturbation extraction module, adversarial-only extraction module and classification and regression module. It takes the relationship between attack algorithm and corresponding hyperparameter into account and uses the uncertainty weighted loss to adjust the weights of three recognition tasks. The experimental results on MNIST and ImageNet show the feasibility and scalability of the proposed framework as well as its effectiveness in dealing with false alarms.
Stock Broad-Index Trend Patterns Learning via Domain Knowledge Informed Generative Network

Predicting the Stock movement attracts much attention from both industry and academia. Despite such significant efforts, the results remain unsatisfactory due to the inherently complicated nature of the stock market driven by factors including supply and demand, the state of the economy, the political climate, and even irrational human behavior. Recently, Generative Adversarial Networks (GAN) have been extended for time series data; however, robust methods are primarily for synthetic series generation, which fall short for appropriate stock prediction. This is because existing GANs for stock applications suffer from mode collapse and only consider one-step prediction, thus underutilizing the potential of GAN. Furthermore, merging news and market volatility are neglected in current GANs. To address these issues, we exploit expert domain knowledge in finance and, for the first time, attempt to formulate stock movement prediction into a Wasserstein GAN framework for multi-step prediction. We propose Index GAN, which includes deliberate designs for the inherent characteristics of the stock market, leverages news context learning to thoroughly investigate textual information and develop an attentive seq2seq learning network that captures the temporal dependency among stock prices, news, and market sentiment. We also utilize the critic to approximate the Wasserstein distance between actual and predicted sequences and develop a rolling strategy for deployment that mitigates noise from the financial market. Extensive experiments are conducted on real-world broad-based indices, demonstrating the superior performance of our architecture over other state-of-the-art baselines, also validating all its contributing components.
Towards decoupling the effects of permeability and roughness on turbulent boundary layers

Abstract Boundary-layer flow over a realistic porous wall might contain both the effects of wall-permeability and wall-roughness. These two effects are typically examined in the context of a rough-wall flow, i.e. by defining a ‘roughness’ length or equivalent to capture the effect of the surface on momentum deficit/drag. In this work, we examine the hypothesis of Esteban et al. (Phys. Rev. Fluids, vol. 7, no. 9, 2022, 094603), that a turbulent boundary layer over a porous wall could be modelled as a superposition of the roughness effects on the permeability effects by using independently obtained information on permeability and roughness. We carry out wind tunnel experiments at high Reynolds number ($14\ 400 \leq Re_{\tau } \leq 33\ 100$) on various combinations of porous walls where different roughnesses are overlaid over a given permeable wall. Measurements are also conducted on the permeable wall as well as the rough walls independently to obtain the corresponding length scales. Analysis of mean flow data across all these measurements suggests that an empirical formulation can be obtained where the momentum deficit ($\Delta U^+$) is modelled as a combination of independently obtained roughness and permeability length scales. This formulation assumes the presence of outer-layer similarity across these different surfaces, which is shown to be valid at high Reynolds numbers. Finally, this decoupling approach is equivalent to the area-weighted power-mean of the respective permeability and roughness length scales, consistent with the approach recently suggested by Hutchins et al. (Ocean Engng, vol. 271, 2023, 113454) to capture the effects of heterogeneous rough surfaces.
Neural Operators for Bypassing Gain and Control Computations in PDE Backstepping

We introduce a framework for eliminating the computation of controller gain functions in PDE control. We learn the nonlinear operator from the plant parameters to the control gains with a (deep) neural network. We provide closed-loop stability guarantees (global exponential) under an NN-approximation of the feedback gains. While, in the existing PDE backstepping, finding the gain kernel requires (one offline) solution to an integral equation, the neural operator (NO) approach we propose learns the mapping from the functional coefficients of the plant PDE to the kernel function by employing a sufficiently high number of offline numerical solutions to the kernel integral equation, for a large enough number of the PDE model's different functional coefficients. We prove the existence of a DeepONet approximation, with arbitrarily high accuracy, of the exact nonlinear continuous operator mapping PDE coefficient functions into gain functions. Once proven to exist, learning of the NO is standard, completed"once and for all"(never online) and the kernel integral equation doesn't need to be solved ever again, for any new functional coefficient not exceeding the magnitude of the functional coefficients used for training. We also present an extension from approximating the gain kernel operator to approximating the full feedback law mapping, from plant parameter functions and state measurement functions to the control input, with semiglobal practical stability guarantees. Simulation illustrations are provided and code is available on github. This framework, eliminating real-time recomputation of gains, has the potential to be game changing for adaptive control of PDEs and gain scheduling control of nonlinear PDEs. The paper requires no prior background in machine learning or neural networks.
A generalization of the Riccati recursion for equality‐constrained linear quadratic optimal control

This paper introduces a generalization of the well‐known Riccati recursion for solving the discrete‐time equality‐constrained linear quadratic optimal control problem. The recursion can be used to compute problem solutions as well as optimal feedback control policies. Unlike other tailored approaches for this problem class, the proposed method does not require restrictive regularity conditions on the problem. This allows its use in nonlinear optimal control problem solvers that use exact Lagrangian Hessian information. We demonstrate that our approach can be implemented in a highly efficient algorithm that scales linearly with the horizon length. Numerical tests show a significant speed‐up of about one order of magnitude with respect to state‐of‐the‐art general‐purpose sparse linear solvers. Based on the proposed approach, faster nonlinear optimal control problem solvers can be developed that are suitable for more complex applications or for implementations on low‐cost or low‐power computational platforms. The implementation of the proposed algorithm is made available as open‐source software.
Differential Pitch Sensitivity of the Ear

Differential pitch sensitivity is defined as the minimum percentage change in frequency which can be detected by the ear. The value of this ratio was measured over a frequency range from 32 cycles to 11,600 cycles and from the thershold of audibility to a sensation level of 90 db. in the case of ten normal ears. A number of subsequent observations were made using binaural and bone conductions timuli. In a region above 1,000 cycles and above a 20 db. sensation level the minimum detectable variation Δ/f is approximately constant at a value of . 003. Below 1,000 cycles the function Δf is approximately constant for any one sensation level. Binaural and bone conduction stimuli tend to increase the sensitivity of the ear to pitch changes in the general region below 500 cycles but above this region there is very little effect. The errors encountered in this type of measurement are discussed, together with the design and technique necessary to minimize such effects. A series of observations taken with modified app.
Classification of 3D Point Cloud Using Numerical Surface Signatures on Interest Points

This paper describes a 3D object classification method by 3D-3D comparison using the numerical surface point signatures on interest points of 3D objects point cloud. Interest or salient points of 3D point cloud were found by Heat Kernel Signature method. The numerical point signatures used for classification were composed only on these points. To investigate the objects classification resistance to the data measurement noise, additionally to original 3D data was added 1.5 % of continuity distributed noise. Object classification was carried out using forty three 3D objects point cloud database. Study of 3D object interest points recognition has shown that the standard Surface Point Signatures methodology is sensitive to the normal vector used for signature composition as well as the object’s surface normal is very sensitive to objects mesh error. In order to reduce the sensitivity to the object surface measurement error we have proposed to use one constant vector as average from all object mesh normal’s. Such approach on average improved interest point’s recognition rate by ~16 % and allowed to reach 95.9 % of classification accuracy on used 43 objects database. DOI: http://dx.doi. org/10.5755/j01.eee. 20.6. 7259
Dispersive perturbations of solitons of the nonlinear Schrödinger equation

A useful analysis of dispersive (radiative) perturbations of solitons of the nonlinear Schrodinger equation is developed. With reference to the propagation of optical solitons in glass fibers, the analysis is used to treat the collision of a low-intensity wave packet with a soliton, the radiation field created by the local perturbation of a soliton, and finally that created by a spatially periodic perturbation of the parameters of the fiber, or equivalently by a periodic variation in gain and loss that averages to zero. Perturbations whose wavelength is short compared with the soliton period produce exponentially small radiation fields as a result of the need for phase matching.
Self-interaction correction and contact hyperfine field

The hyperfine field is a precise and essential probe of the magnetic state of a solid, and of the quality of theoretical core wave functions, but it's accurate evaluation has proven challenging from first principles. In this work, the self-interaction free potential, suggested recently by Lundin and Eriksson, is applied to the core states in the calculation of the hyperfine field for 3d transition metal ferromagnets Fe, Co, and Ni, and for three Fe compounds. Compared to the local spin density approximation and to its conventional self-interaction corrected form, the new potential functional is found to increase substantially the core contribution to the Fermi contact term, leading to good agreement with measurements for Fe and Co, and significantly better results for iron compounds. Our results strongly suggest that the new functional is more suitable for generating realistic core wave functions to high accuracy for a wide range of materials. The subtle effects resulting from the change of potential functional are also addressed.
Pointwise convergence of Cesàro and Riesz means on certain function spaces

We consider a function space $$\mathscr{QA}$$ on the unit sphere of ℝ^3, which contains Llog Llog log log L, and prove the spherical harmonics expansions of functions in $$\mathscr{QA}$$ are summable a.e. with respect to the Cesàro means of the critical order 1/2. We also prove that a similar result holds for the Bochner- Riesz means of multiple Fourier series of periodic functions on ℝ^d, d ≥ 2.
Digital adaptive optics with interferometric homodyne encoding for mitigating atmospheric turbulence

Abstract. Digital adaptive optics (DAO) created using homodyne encoding can mitigate atmospheric turbulence in passive imaging systems. This work demonstrates a self-referencing homodyne interferometry technique that combines the passive imaging utility of multiframe algorithmic procedures with the single-frame correction capability of the Shack–Hartmann adaptive optics technique. This paper presents image reconstruction improvements through the addition of (1) phase diversity modulation techniques within the interferometry reconstruction algorithm and (2) temporal image processing techniques applied after the interferometry reconstruction algorithm. By imaging quick response codes through a turbulent air chamber in the laboratory, it was possible to quantify the machine-readable performance gain provided by DAO when compared with a standard imaging camera. Results from this research verify that DAO from homodyne encoding provides turbulence mitigation for single frames of data, paving the way for environmentally robust, high-speed, self-contained imaging systems.
Quantum‐classical molecular dynamics as an approximation to full quantum dynamics

This paper presents a mathematical derivation of a model for quantum‐classical molecular dynamics (QCMD) as a partial classical limit of the full Schrodinger equation. This limit is achieved in two steps: separation of the full wave function and short wave asymptotics for its ‘‘classical’’ part. Both steps can be rigorously justified under the same smallness assumptions. This throws some light on the time‐dependent self‐consistent‐field method and on mixed quantum‐semiclassical models, which also depend on the separation step. On the other hand, the theory leads to a characterization of the critical situations in which the QCMD model is in danger of largely deviating from the solution of full Schrodinger equation. These critical situations are exemplified in an illustrative numerical simulation: the collinear collision of a classical particle with a harmonic quantum oscillator.
Tie-Dyeing Pattern Fast-Generation Method Based on Deep-Learning and Digital-Image-Processing Technology

Abstract Contingency and uniqueness are regarded as typical artistic characteristics. To accomplish the realistic effect of each tie-dyeing pattern artwork, we propose a digital tie-dyeing pattern fast-generation algorithm based on auxiliary-classifier deep-convolution generative adversarial network (AC-DCGAN) and image-processing technology. To apply this algorithm, the designer first draws the planar layout diagram of the tie-dyeing patterns. The diagram consists of a white background and polychrome circles, and the regional-connectivity algorithm is used to extract information on all the circle positions as well as the pattern categories in the diagram. Then the AC-DCGAN-generated background image is color-corrected to stitch and complete its construction. The AC-DCGAN-generated tie-dyeing pattern image is also color-corrected and is then segmented and copied to the circle area. Mean filtering creates the final digital tie-dyeing patterns. Results show no obvious color difference in generated patterns, splicing edges show uniform transition, and unique patterns exhibit tie-dye characteristics, achieving realistic artistic effects.
A high order Godunov scheme with constrained transport and adaptive mesh refinement for astrophysical and geophysical MHD

We propose to extend the well-known MUSCL-Hancock scheme for Euler equations to the induction equation modeling the magnetic field evolution in kinematic dynamo problems. The scheme is based on an integral form of the underlying conservation law which, in our formulation, results in a “finite-surface” scheme for the induction equation. This naturally leads to the well-known “constrained transport” method, with additional continuity requirement on the magnetic field representation. The second ingredient in the MUSCL scheme is the predictor step that ensures second order accuracy both in space and time. We explore specific constraints that the mathematical properties of the induction equations place on this predictor step, showing that three possible variants can be considered. We show that the most aggressive formulations reach the same level of accuracy than the other ones, at a lower computational cost. More interestingly, these schemes are compatible with the Adaptive Mesh Refinement (AMR) framework. It has been implemented in the AMR code RAMSES. It offers a novel and efficient implementation of a second order scheme for the induction equation. The scheme is then adaptated to solve for the full MHD equations using the same methodology. Through a series of test problems, we illustrate the performances of this new code using two different MHD Riemann solvers (Lax–Friedrich and Roe) and the need of the Adaptive Mesh Refinement capabilities in some cases. Finally, we show its versatility by applying it to the ABC dynamo problem and to the collapse of a magnetized cloud core.
Characterisation of medical conditions of children with sickle cell disease in the USA: findings from the 2007–2018 National Health Interview Survey (NHIS)

Objectives We used the National Health Interview Survey (NHIS) data set to examine the prevalence of comorbid medical conditions; explore barriers to accessing healthcare and special educational services; and assess the associations between sickle cell disease (SCD) status and demographics/socioeconomic status (SES), and social determinants of health (SDoH) on comorbidities among children in the USA. Design Cross-sectional. Setting NHIS Sample Child Core questionnaire 2007–2018 data set. Participants 133 481 children; presence of SCD was determined by an affirmative response from the adult or guardian of the child. Main outcome measures Multivariate logistic regression was used to compare the associations between SCD status, SES and SDoH for various medical conditions for all races and separately for black children at p<0.05. Results 133 481 children (mean age 8.5 years, SD: 0.02), 215 had SCD and ~82% (weighted) of the children with SCD are black. Children with SCD were more likely to suffer from comorbid conditions, that is, anaemia (adjusted OR: 27.1, p<0.001). Furthermore, children with SCD had at least two or more emergency room (ER) visits (p<0.001) and were more likely to have seen a doctor 1–15 times per year (p<0.05) compared with children without SCD. Household income (p<0.001) and maternal education were lower for children with SCD compared with children without SCD (52.4% vs 63.5% (p<0.05)). SCD children with a maternal parent who has < / > High School degree were less likely to have no ER visits or 4–5 ER visits, and more likely to have 2–3 ER visits within 12 months. Conclusion Children with SCD experienced significant comorbid conditions and have high healthcare usage, with black children being disproportionately affected. Moreover, maternal education status and poverty level illustrates how impactful SES can be on healthcare seeking behaviour for the SCD population. SDoH have significant implications for managing paediatric patients with SCD in clinical settings.
Fluoxetine Inhibits STAT3-mediated Survival and Invasion of Osteosarcoma Cells

Background/Aim: Osteosarcoma (OS) is a common primary malignancy of bone in adolescents. Its highly metastatic characteristics can lead to treatment failure and poor prognosis. Although standard treatments, including surgery, radiotherapy, and chemotherapy, have progressed in the past decade, treatment options to overcome metastatic progression remain sparse. Fluoxetine, an anti-depressant, has been widely used in patients with cancer for their mental issues and was reported to possess antitumor potential. However, the effect of fluoxetine on OS remains unclear. Materials and Methods: In this study, we used cell viability, invasion/migration transwell, wound-healing and aortic ring assays to identify the effects of fluoxetine on metastasis and progression in OS. Results: Fluoxetine induced cytotoxicity in OS cells by activating both extrinsic/intrinsic apoptosis signaling pathways. Proliferation and anti-apoptosis-related factors such as cyclin D1 and X-linked inhibitor of apoptosis were suppressed by fluoxetine. Additionally, fluoxetine suppressed the invasive/migratory abilities of OS and inhibited the development of angiogenesis by reducing the phosphorylation of signal transducer and activator of transcription 3 (STAT3). Metastasis-associated factors, vascular endothelial growth factors, matrix metallopeptidase 2 and -9, were all reduced in OS cells by fluoxetine treatment. Conclusion: Fluoxetine not only induces cytotoxicity and apoptosis of OS cells, but also suppresses metastasis and angiogenesis by targeting STAT3.
Lung Cancer – Standard Therapy and the Use of a Novel, Highly Effective, Well Tolerated, Treatment With Progesterone Receptor Modulators

The most recent successful advances in lung cancer therapy have directly and increasingly focused on personalized tumor genetic/epigenetic/immunologic profiling, and the identification and development of novel pharmacologic agents aimed at those mutations [e.g., epidermal growth factor receptor (EGFR), Kristen rat sarcoma viral oncogene homolog (KRAS), anaplastic lymphoma kinase (ALK) and immunotherapy against programmed cell death protein 1 (PD-1) and its ligands] which have extended life and provided palliation for lung cancer-patients positive for these mutations. The objective of this study is to provide a review of the large number of drugs and their efficacy as of 2022, for lung cancer, but also introduce a novel treatment that has the potential, based on one controlled murine lung cancer study and 5 anecdotal human cases, that showed marked palliative and longevity benefits in very advanced lung cancer with no other treatment options, i.e., progesterone receptor (PR) antagonists targeting the immunosuppressive protein, the progesterone induced blocking factor (PIBF). Credibility, however, will only be provided when the efficacy can be demonstrated in a large series of lung cancer cases ideally with certain controls. Thus, the ultimate objective of the review is to interest oncologists with a large population of lung cancer patients to perform a well powered study to corroborate or refute the limited experience to date with PR antagonist therapy.