Miracle-dz/UniFAR-FTUs huggingface dataset:
convoHER2: A Deep Neural Network for Multi-Stage Classification of HER2 Breast Cancer

Generally, human epidermal growth factor 2 (HER2) breast cancer is more aggressive than other kinds of breast cancer. Currently, HER2 breast cancer is detected using variety of medical test which are most expensive. Therefore, the aim of this study was to develop a computational model named convoHER2 for detecting HER2 breast cancer with image data using convolution neural network (CNN). Hematoxylin and eosin (H&E) and immunohistochemical (IHC) stained images were used as raw data from the Bayesian information criterion (BIC) benchmark dataset. This dataset consists of 4873 images of H&E and IHC. Among all images of the dataset, 3896 and 977 images were applied to train and test the convoHER2 model, respectively. All images of this dataset are resized due to high resolution of the image for forming better detection performance of convoHER2 model. Moreover, the dataset is classified into four different labels (0+, 1+, 2+, 3+) for identifying the grade of detected HER2 breast cancer. The convoHER2 model is able to detect HER2 cancer and its grade with accuracy 85% and 88% using H&E images and IHC images, respectively. The outcomes of this study determined that the HER2 cancer detecting rates of the convoHER2 model are much enough to provide better diagnosis to the patient for recovering their HER2 breast cancer in future.
Learning Nonlinear Couplings in Network of Agents from a Single Sample Trajectory

—We consider a class of stochastic dynami- cal networks whose governing dynamics can be modeled using a coupling function. It is shown that the dynamics of such networks can generate geometrically ergodic trajectories under some reasonable assumptions. We show that a general class of coupling functions can be learned using only one sample trajectory from the network. This is practically plausible as in numerous applications it is desired to run an experiment only once but for a longer period of time, rather than repeating the same experiment multiple times from different initial conditions. Building upon ideas from the concentration inequalities for geometrically ergodic Markov chains, we formulate several results about the convergence of the empirical estimator to the true coupling function. Our theoretical ﬁndings are supported by extensive simulation results.
SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal Control

Traffic signal control is safety-critical for our daily life. Roughly one-quarter of road accidents in the U.S. happen at intersections due to problematic signal timing, urging the development of safety-oriented intersection control. However, existing studies on adaptive traffic signal control using reinforcement learning technologies have focused mainly on minimizing traffic delay but neglecting the potential exposure to unsafe conditions. We, for the first time, incorporate road safety standards as enforcement to ensure the safety of existing reinforcement learning methods, aiming toward operating intersections with zero collisions. We have proposed a safety-enhanced residual reinforcement learning method (SafeLight) and employed multiple optimization techniques, such as multi-objective loss function and reward shaping for better knowledge integration. Extensive experiments are conducted using both synthetic and real-world benchmark datasets. Results show that our method can significantly reduce collisions while increasing traffic mobility.
A step towards treatment planning for microbeam radiation therapy: fast peak and valley dose predictions with 3D U-Nets

Fast and accurate dose predictions are one of the bottlenecks in treatment planning for microbeam radiation therapy (MRT). In this paper, we propose a machine learning (ML) model based on a 3D U-Net. Our approach predicts separately the large doses of the narrow high intensity synchrotron microbeams and the lower valley doses between them. For this purpose, a concept of macro peak doses and macro valley doses is introduced, describing the respective doses not on a microscopic level but as macroscopic quantities in larger voxels. The ML model is trained to mimic full Monte Carlo (MC) data. Complex physical effects such as polarization are therefore automatically taking into account by the model. The macro dose distribution approach described in this study allows for superimposing single microbeam predictions to a beam array field making it an interesting candidate for treatment planning. It is shown that the proposed approach can overcome a main obstacle with microbeam dose predictions by predicting a full microbeam irradiation field in less than a minute while maintaining reasonable accuracy.
Orientation recognition and correction of Cardiac MRI with deep neural network

In this paper, the problem of orientation correction in cardiac MRI images is investigated and a framework for orientation recognition via deep neural networks is proposed. For multi-modality MRI, we introduce a transfer learning strategy to transfer our proposed model from single modality to multi-modality. We embed the proposed network into the orientation correction command-line tool, which can implement orientation correction on 2D DICOM and 3D NIFTI images. Our source code, network models and tools are available at https://github.com/Jy-stdio/MSCMR_orient/
Embedding a Differentiable Mel-Cepstral Synthesis Filter to a Neural Speech Synthesis System

This paper integrates a classic mel-cepstral synthesis filter into a modern neural speech synthesis system towards end-to-end controllable speech synthesis. Since the mel-cepstral synthesis filter is explicitly embedded in neural waveform models in the proposed system, both voice characteristics and the pitch of synthesized speech are highly controlled via a frequency warping parameter and fundamental frequency, respectively. We implement the mel-cepstral synthesis filter as a differentiable and GPU-friendly module to enable the acoustic and waveform models in the proposed system to be simultaneously optimized in an end-to-end manner. Experiments show that the proposed system improves speech quality from a baseline system maintaining controllability. The core PyTorch modules used in the experiments are publicly available on GitHub1.
Phonemic Adversarial Attack against Audio Recognition in Real World

Recently, adversarial attacks for audio recognition have at- tracted much attention. However, most of the existing studies mainly rely on the coarse-grain audio features at the in- stance level to generate adversarial noises, which leads to ex-pensive generation time costs and weak universal attacking ability. Motivated by the observations that all audio speech consists of fundamental phonemes, this paper proposes a p honemic a dversarial a t tack (PAT) paradigm, which attacks the ﬁne-grain audio features at the phoneme level commonly shared across audio instances, to generate phonemic adversarial noises, enjoying the more general attacking ability with fast generation speed. Speciﬁcally, for accelerating the generation , a phoneme density balanced sampling strategy is in- troduced to sample quantity less but phonemic features abundant audio instances as the training data via estimating the phoneme density, which substantially alleviates the heavy dependency on the large training dataset. Moreover, for pro- moting universal attacking ability , the phonemic noise is optimized in an asynchronous way with a sliding window, which enhances the phoneme diversity and thus well captures the critical fundamental phonemic patterns. By conducting extensive experiments, we comprehensively investigate the proposed PAT framework and demonstrate that it outperforms the SOTA baselines by large margins ( i.e. , at least 11 × speed up and 78% attacking ability improvement) 1 .
Exploring the Effectiveness of Mask-Guided Feature Modulation as a Mechanism for Localized Style Editing of Real Images

The success of Deep Generative Models at high-resolution image generation has led to their extensive utilization for style editing of real images. Most existing methods work on the principle of inverting real images onto their latent space, followed by determining controllable directions. Both inversion of real images and determination of controllable latent directions are computationally expensive operations. Moreover, the determination of controllable latent directions requires additional human supervision. This work aims to explore the efficacy of mask-guided feature modulation in the latent space of a Deep Generative Model as a solution to these bottlenecks. To this end, we present the SemanticStyle Autoencoder (SSAE), a deep Generative Autoencoder model that leverages semantic mask-guided latent space manipulation for highly localized photorealistic style editing of real images. We present qualitative and quantitative results for the same and their analysis. This work shall serve as a guiding primer for future work.
A systematic investigation into the effect of roughness on self-propelled swimming plates

Abstract This study examines the effects of surface topography on the flow and performance of a self-propelled swimming (SPS) body. We consider a thin flat plate with an egg-carton roughness texture undergoing prescribed undulatory swimming kinematics at Strouhal number $0.3$ and tail amplitude to length ratio $0.1$; we use plate Reynolds numbers $Re=6$, 12 and $24\times 10^3$, and focus on $12\,000$. As the roughness wavelength is decreased, we find that the undulation wave speed must be increased to overcome the additional drag from the roughness and maintain SPS. Correspondingly, the extra wave speed raises the power required to maintain SPS, making the swimmer less efficient. To decouple the roughness and the kinematics, we compare the rough plates to equivalent smooth cases by matching the kinematic conditions. We find that all but the longest roughness wavelengths reduce the required swimming power and the unsteady amplitude of the forces when compared to a smooth plate undergoing identical kinematics. Additionally, roughness can enhance flow enstrophy by up to 116 % compared to the smooth cases without a corresponding spike in forces; this suggests that the increased mixing is not due to increased vorticity production at the wall. Instead, the enstrophy is found to peak strongly when the roughness wavelength is approximately twice the boundary layer thickness over the $Re$ range, indicating the roughness induces large-scale secondary flow structures that extend to the edge of the boundary layer. This study reveals the nonlinear interaction between roughness and kinematics beyond a simple increase or decrease in drag, illustrating that roughness studies on static shapes do not transfer directly to unsteady swimmers.
Variable-Based Fault Localization via Enhanced Decision Tree

Fault localization, aiming at localizing the root cause of the bug under repair, has been a longstanding research topic. Although many approaches have been proposed in the last decades, most of the existing studies work at coarse-grained statement or method levels with very limited insights about how to repair the bug (granularity problem), but few studies target the finer-grained fault localization. In this paper, we target the granularity problem and propose a novel finer-grained variable-level fault localization technique. Specifically, the basic idea of our approach is that fault-relevant variables may exhibit different values in failed and passed test runs, and variables that have higher discrimination ability have a larger possibility to be the root causes of the failure. Based on this, we propose a program-dependency-enhanced decision tree model to boost the identification of fault-relevant variables via discriminating failed and passed test cases based on the variable values. To evaluate the effectiveness of our approach, we have implemented it in a tool called VarDT and conducted an extensive study over the Defects4J benchmark. The results show that VarDT outperforms the state-of-the-art fault localization approaches with at least 268.4% improvement in terms of bugs located at Top-1, and the average improvement is 351.3%. Besides, to investigate whether our finer-grained fault localization result can further improve the effectiveness of downstream APR techniques, we have adapted VarDT to the application of patch filtering, where we use the variables located by VarDT to filter incorrect patches. The results denote that VarDT outperforms the state-of-the-art PATCH-SIM and BATS by filtering 14.8% and 181.8% more incorrect patches, respectively, demonstrating the effectiveness of our approach. It also provides a new way of thinking for improving automatic program repair techniques.
Data-driven quadratic modeling in the Loewner framework

: In this work, we present a non-intrusive, i.e., purely data-driven method that uses the Loewner framework (LF) along with nonlinear optimization techniques to identify or reduce quadratic control systems from input-output (i/o) time-domain measurements. At the heart of this method are optimization schemes that enforce interpolation of the symmetric generalized frequency response functions (GFRFs) as derived in the Volterra series framework. We consider harmonic input excitations to infer such measurements. After reaching the steady-state proﬁle, the symmetric GFRFs can be measured from the Fourier spectrum (phase and amplitude). By properly using these measurements, we can identify low-order nonlinear state-space models with non-trivial equilibrium state points in the quadratic form, such as the Lorenz attractor. In particular, for the multi-point equilibrium case, where measurements describe some local bifurcated models to diﬀerent coordinates, we achieve global model identiﬁcation after solving an operator alignment problem based on a constrained quadratic matrix equation. We test the new method for a more demanding system in terms of state dimension, i.e., the viscous Burgers’ equation with Robin boundary conditions. The complexity reduction and approximation accuracy are tested. Future directions and challenges conclude this work.
Feature Weaken: Vicinal Data Augmentation for Classification

Deep learning usually relies on training large-scale data sam- ples to achieve better performance. However, over-ﬁtting based on training data always remains a problem. Scholars have proposed various strategies, such as feature dropping and feature mixing, to improve the generalization continu- ously. For the same purpose, we subversively propose a novel training method, Feature Weaken, which can be regarded as a data augmentation method. Feature Weaken constructs the vicinal data distribution with the same cosine similarity for model training by weakening features of the original samples. In especially, Feature Weaken changes the spatial distribution of samples, adjusts sample boundaries, and reduces the gradient optimization value of back-propagation. This work can not only improve the classiﬁcation performance and generalization of the model, but also stabilize the model training and accelerate the model convergence. We conduct extensive experiments on classical deep convolution neural models with ﬁve common image classiﬁcation datasets and the Bert model with four common text classiﬁcation datasets. Com- pared with the classical models or the generalization improvement methods, such as Dropout, Mixup, Cutout, and CutMix, Feature Weaken shows good compatibility and performance. We also use adversarial samples to perform the robustness experiments, and the results show that Feature Weaken is effective in improving the robustness of the model.
Quench dynamics in holographic first-order phase transition

In this work, we investigate the real-time dynamics of quenching a state from phase separation in a holographic model of first-order phase transition. In addition to the typical phase-separated and high-energy final states, we have discovered a novel dynamical process that drives the system to a low-temperature supercooled final state within a narrow range of quench parameters. The critical behavior is also revealed during the fully non-linear dynamics. Following a sudden quench with critical parameters, the phase separation can be attracted to a critical nucleus. Specifically, the critical nucleus will subsequently shrink in size and eventually disappear for super-critical parameters, where the system is actually supercooled with a temperature lower than the initial one. While for sub-critical parameters, the nucleus will grow in size and finally reform a phase separation, where the absorbed quenching energy is reflected in the increment of the latent heat.
Electric dipole transition amplitudes for atoms and ions with one valence electron

Motivated by recent measurements for several alkali-metal atoms and alkali-metal-like ions, we perform a detailed study of electric dipole (E1) transition amplitudes in K, Ca+, Rb, Sr+, Cs, Ba+, Fr, and Ra+, which are of interest for studies of atomic parity violation, electric dipole moments, and polarizabilities. Using the all-orders correlation potential method, we perform high-precision calculations of E1 transition amplitudes between low-lying s, p, and d states. We perform a robust error analysis, and compare our calculations to many amplitudes for which there are high-precision experimental determinations. We find excellent agreement, with deviations at the level of ~0.1%. We also compare our results to other theoretical evaluations, and discuss the implications for uncertainty analyses. Further, combining calculations of branching ratios with recent measurements, we extract high-precision values for several E1 amplitudes of Ca+, Sr+, Cs, Fr, and Ra+.
Young radio jets breaking free: molecular and HI outflows in their centers

Abstract Our view of the central regions of AGN has been enriched by the discovery of fast and massive outflows of H I and molecular gas. Here we present a brief summary of results obtained for young (and restarted) radio AGN. We find that H I outflows tend to be particularly common in this group of objects. This supports the idea that the jet, expanding in a clumpy medium, plays a major role in driving these outflows. The clumpiness of the medium is confirmed by VLBI and ALMA observations. The H I observations reveal that, at least part of the gas, is distributed in clouds with sizes up to a few tens of pc and mass ~104Mȯ. A change of the conditions in the outflow, with an increasing fraction of diffuse components, as the radio jets grow, is suggested by the high resolution H I observations. The molecular gas completes the picture, showing that the radio plasma jet can couple well with the ISM, strongly affecting the kinematics, but also the physical conditions of the molecular gas. This is confirmed by numerical simulations reproducing, to first order, the kinematics of the gas.
Attacking Shortest Paths by Cutting Edges

Identifying shortest paths between nodes in a network is a common graph analysis problem that is important for many applications involving routing of resources. An adversary that can manipulate the graph structure could alter traffic patterns to gain some benefit (e.g., make more money by directing traffic to a toll road). This article presents the Force Path Cut problem, in which an adversary removes edges from a graph to make a particular path the shortest between its terminal nodes. We prove that the optimization version of this problem is APX-hard but introduce PATHATTACK, a polynomial-time approximation algorithm that guarantees a solution within a logarithmic factor of the optimal value. In addition, we introduce the Force Edge Cut and Force Node Cut problems, in which the adversary targets a particular edge or node, respectively, rather than an entire path. We derive a nonconvex optimization formulation for these problems and derive a heuristic algorithm that uses PATHATTACK as a subroutine. We demonstrate all of these algorithms on a diverse set of real and synthetic networks, illustrating where the proposed algorithms provide the greatest improvement over baseline methods.
Deanthropomorphising NLP: Can a Language Model Be Conscious?

This work is intended as a voice in the discussion over previous claims that a pretrained large language model (LLM) based on the Transformer model architecture can be sentient. Such claims have been made concerning the LaMDA model and also concerning the current wave of LLM-powered chatbots, such as ChatGPT. This claim, if confirmed, would have serious ramifications in the Natural Language Processing (NLP) community due to wide-spread use of similar models. However, here we take the position that such a large language model cannot be sentient, or conscious, and that LaMDA in particular exhibits no advances over other similar models that would qualify it. We justify this by analysing the Transformer architecture through Integrated Information Theory of consciousness. We see the claims of sentience as part of a wider tendency to use anthropomorphic language in NLP reporting. Regardless of the veracity of the claims, we consider this an opportune moment to take stock of progress in language modelling and consider the ethical implications of the task. In order to make this work helpful for readers outside the NLP community, we also present the necessary background in language modelling.
Adaptive finite-time model estimation and control for manipulator visual servoing using sliding mode control and neural networks

ABSTRACT The image-based visual servoing without models of the system is challenging since it is hard to fetch an accurate estimation of hand-eye relationship via merely visual measurement. Whereas, the accuracy of the estimated hand-eye relationship expressed in local linear format with the Jacobian matrix is important to the whole system's performance. In this article, we proposed a finite-time controller as well as a Jacobian matrix estimator in a combination of online and offline ways. The local linear formulation is formulated first. Then, we use a combination of online and offline methods to boost the estimation of the highly coupled and nonlinear hand-eye relationship with data collected via depth camera. A neural network (NN) is pre-trained to give a relative reasonable initial estimation of the Jacobian matrix. Then, an online updating method is carried out to modify the offline trained NN for a more accurate estimation. Moreover, a sliding mode control algorithm is introduced to realize a finite-time controller. Compared with previous methods, our algorithm possesses better convergence speed. The proposed estimator possesses excellent performance in the accuracy of initial estimation and powerful tracking capabilities for time-varying estimation for the Jacobian matrix compared with other data-driven estimators. The proposed scheme acquires the combination of neural network and finite-time control effect which drives a faster convergence speed compared with the exponentially converge ones. Another main feature of our algorithm is that the state signals in the system are proven to be semi-global practical finite-time stable. Several experiments are carried out to validate the proposed algorithm's performance. GRAPHICAL ABSTRACT
Scaling behavior of impurities in mesoscopic Luttinger liquids

Using a functional renormalization group, we compute the flow of the renormalized impurity potential for a single impurity in a Luttinger liquid over the entire energy range from the microscopic scale of a lattice-fermion model down to the low-energy limit. The nonperturbative method provides a complete real-space picture of the effective impurity potential. We confirm the universality of the open chain fixed point, but it turns out that very large systems ${(10}^{4}\char21{}{10}^{5}$ sites) are required to reach the fixed point for realistic choices of the impurity and interaction parameters.