Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual-Distribution Discrepancy for Anomaly Detection in Chest X-Rays

Chest X-ray (CXR) is the most typical radiological exam for diagnosis of various diseases. Due to the expensive and time-consuming annotations, detecting anomalies in CXRs in an unsupervised fashion is very promising. However, almost all of the existing methods consider anomaly detection as a One-Class Classiﬁcation (OCC) problem. They model the distribution of only known normal images during training and identify the samples not conforming to normal proﬁle as anomalies in the testing phase. A large number of unlabeled images containing anomalies are thus ignored in the training phase, although they are easy to obtain in clinical practice. In this paper, we propose a novel strategy, Dual-distribution Discrepancy for Anomaly Detection (DDAD), utilizing both known normal images and unlabeled images. The proposed method consists of two modules, denoted as A and B. During training, module A takes both known normal and unlabeled images as inputs, capturing anomalous features from unlabeled images in some way, while module B models the distribution of only known normal images. Subsequently, the inter-discrepancy between modules A and B, and intra-discrepancy inside module B are designed as anomaly scores to indicate anomalies. Experiments on three CXR datasets demonstrate that the proposed DDAD achieves consistent, signiﬁcant gains and outperforms state-of-the-art methods. Code is available at https://github.com/caiyu6666/DDAD .
Adaptive Neural Network-based Unscented Kalman Filter for Spacecraft Pose Tracking at Rendezvous

This paper presents a neural network-based Unscented Kalman Filter (UKF) to track the pose (i.e., position and orientation) of a known, noncooperative, tumbling target spacecraft in a close-proximity rendezvous scenario. The UKF estimates the relative orbital and attitude states of the target with respect to the servicer based on the pose information extracted from incoming monocular images of the target spacecraft with a Convolutional Neural Network (CNN). In order to enable reliable tracking, the process noise covariance matrix of the UKF is tuned online using adaptive state noise compensation. Speciﬁcally, the closed-form process noise model for the relative attitude dynamics is newly derived and implemented. In order to enable a comprehensive analysis of the performance and robustness of the proposed CNN-powered UKF, this paper also introduces the Satellite Hardware-In-the-loop Rendezvous Trajectories (SHIRT) dataset which comprises the labeled imagery of two representative rendezvous trajectories in low Earth orbit. For each trajectory, two sets of images are respectively created from a graphics renderer and a robotic testbed to allow testing the ﬁlter’s robustness across domain gap. The proposed UKF is evaluated on both domains of the trajectories in SHIRT and is shown to have sub-decimeter-level position and degree-level orientation errors at steady-state.
Pulsar wind nebulae of runaway massive stars

A significant fraction of massive stars move at speed through the interstellar medium of galaxies. After their death as core-collapse supernovae, a possible final evolutionary state is that of a fast-rotating magnetized neutron star, shaping its circumstellar medium into a pulsar wind nebula. Understanding the properties of pulsar wind nebulae requires knowledge of the evolutionary history of their massive progenitors. Using two-dimensional magnetohydrodynamical simulations, we demonstrate that, in the context of a runaway high-mass red-supergiant supernova progenitor, the morphology of its subsequent pulsar wind nebula is strongly affected by the wind of the defunct progenitor star pre-shaping the stellar surroundings throughout its entire past life. In particular, pulsar wind nebulae of obscured runaway massive stars harbour asymmetries as a function of the morphology of the progenitor’s wind-blown cavity, inducing projected asymmetric up–down synchrotron emission.
Select Porcine Elongation Factor 1α Sequences Mediate Stable High-Level and Upregulated Expression of Heterologous Genes in Porcine Cells in Response to Primate Serum

Genetically engineered (GE) pigs with various combinations of genetic profiles have been developed using heterologous promoters. This study aimed to identify autologous promoters for high and ubiquitous expression of xenotransplantation relevant genes in GE pigs. A 1.4 kb upstream regulatory sequence of porcine elongation factor 1α (pEF1α) gene was selected and isolated for use as a promoter. Activity of the pEF1α promoter was subsequently compared with that of the cytomegalovirus (CMV) promoter, CMV enhancer/chicken β-actin (CAG) promoter, and human EF1α (hEF1α) promoter in different types of pig-derived cells. Comparative analysis of luciferase and mutant human leukocyte antigen class E-F2A-β-2 microglobulin (HLA-E) expression driven by pEF1α, CMV, CAG, and hEF1α promoters revealed the pEF1α promoter mediated comparable expression levels with those of the CAG promoter in porcine ear skin fibroblasts (PEFs) and porcine kidney-15 (PK-15) cells, but lower than those of the CAG promoter in porcine aortic endothelial cells (PAECs). The pEF1α promoter provided long-term stable HLA-E expression in PEFs, but the CAG promoter failed to sustain those levels of expression. For xenogeneic serum-induced cytotoxicity assays, the cells were cultured for several hours in growth medium supplemented with primate serum. Notably, the pEF1α promoter induced significant increases in luciferase and HLA-E expression in response to primate serum in PAECs compared with those driven by the CAG promoter, suggesting the pEF1α promoter could regulate temporal expression of heterologous genes under xenogeneic-cytotoxic conditions. These results suggest the pEF1α promoter may be valuable for development of GE pigs spatiotemporally and stably expressing immunomodulatory genes for xenotransplantation.
Reaping the advantages of organizational innovation. A case study towards the possibilities and obstructions for adaptation

This study deals with the ability of management to successfully innovate their organizations by means of adaptation. Two main theories, organizational ecology (OE) and contingency theory (CT) have used the concept of organizational form to debate if inertia (OE) or adaptation (CT) is more the order of the day. This study wants to make a contribution towards this debate by asking to what extent managers of existing firms can (or cannot) support innovation by means of organizational adaptation. We use evolutionary economics to deal with this question because on the one hand this field has arguments for both organizational change and stability and other hand it enable us to extend the evolutionary model with the concept organizational form. The results show that organizational innovation is not only a product of selection on population level. A case study of the automobile industry demonstrates that new subpopulations are created by changing incumbents and not only by new entrants.
Dual Windows Are Significant: Learning from Mediastinal Window and Focusing on Lung Window

Since the pandemic of COVID-19, several deep learning methods were proposed to analyze the chest Computed Tomography (CT) for diagnosis. In the current situation, the disease course classification is significant for medical personnel to decide the treatment. Most previous deep-learning-based methods extract features observed from the lung window. However, it has been proved that some appearances related to diagnosis can be observed better from the mediastinal window rather than the lung window, e.g., the pulmonary consolidation happens more in severe symptoms. In this paper, we propose a novel Dual Window RCNN Network (DWRNet), which mainly learns the distinctive features from the successive mediastinal window. Regarding the features extracted from the lung window, we introduce the Lung Window Attention Block (LWA Block) to pay additional attention to them for enhancing the mediastinal-window features. Moreover, instead of picking up specific slices from the whole CT slices, we use a Recurrent CNN and analyze successive slices as videos. Experimental results show that the fused and representative features improve the predictions of disease course by reaching the accuracy of 90.57%, against the baseline with an accuracy of 84.86%. Ablation studies demonstrate that combined dual window features are more efficient than lung-window features alone, while paying attention to lung-window features can improve the model's stability. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting

The performance of time series forecasting has recently been greatly improved by the introduction of transformers. In this paper, we propose a general multi-scale framework that can be applied to the state-of-the-art transformer-based time series forecasting models (FEDformer, Autoformer, etc.). By iteratively refining a forecasted time series at multiple scales with shared weights, introducing architecture adaptations, and a specially-designed normalization scheme, we are able to achieve significant performance improvements, from 5.5% to 38.5% across datasets and transformer architectures, with minimal additional computational overhead. Via detailed ablation studies, we demonstrate the effectiveness of each of our contributions across the architecture and methodology. Furthermore, our experiments on various public datasets demonstrate that the proposed improvements outperform their corresponding baseline counterparts. Our code is publicly available in https://github.com/BorealisAI/scaleformer.
Stable Vortex Particle Method Formulation for Meshless Large-Eddy Simulation

A novel formulation of the vortex particle method (VPM) is developed for large-eddy simulation (LES) in a meshless scheme that is numerically stable. A new set of VPM governing equations are derived from the LES-filtered Navier–Stokes equations. The new equations reinforce the conservation of angular momentum by resizing vortex elements subject to vortex stretching. In addition to the VPM reformulation, a new anisotropic dynamic model of subfilter-scale (SFS) vortex stretching is developed. This SFS model is well suited for turbulent flows with coherent vortical structures, where the predominant cascade mechanism is vortex stretching. The mean and fluctuating components of turbulent flow and Reynolds stresses are validated through the simulation of a turbulent round jet. The computational efficiency of the scheme is showcased in the simulation of an aircraft rotor in hover, showing our meshless LES to be 100 times faster than a mesh-based LES with similar fidelity. The implementation of our meshless LES scheme is released as open-source software, called FLOWVPM.
Robust PCA for Subspace Estimation in User-Centric Cell-Free Wireless Networks

We consider a scalable user-centric cell-free massive MIMO network with distributed remote radio units (RUs), enabling macrodiversity and joint processing. Due to the limited uplink (UL) pilot dimension, multiuser interference in the UL pilot transmission phase makes channel estimation a non-trivial problem. We make use of two types of UL pilot signals, sounding reference signal (SRS) and demodulation reference signal (DMRS) pilots, for the estimation of the channel subspace and its instantaneous realization, respectively. The SRS pilots are transmitted over multiple time slots and resource blocks according to a Latin squares based hopping scheme, which aims at averaging out the interference of different SRS co-pilot users. We propose a robust principle component analysis approach for channel subspace estimation from the SRS signal samples, employed at the RUs for each associated user. The estimated subspace is further used at the RUs for DMRS pilot decontamination and instantaneous channel estimation. We provide numerical simulations to compare the system performance using our subspace and channel estimation scheme with the cases of ideal partial subspace/channel knowledge and pilot matching channel estimation. The results show that a system with a properly designed SRS pilot hopping scheme can closely approximate the performance of a genie-aided system.
White-box Membership Attack Against Machine Learning Based Retinopathy Classification

The advances in machine learning (ML) have greatly improved AI-based diagnosis aid systems in medical imaging. However, being based on collecting medical data specific to individuals induces several security issues, especially in terms of privacy. Even though the owner of the images like a hospital put in place strict privacy protection provisions at the level of its information system, the model trained over his images still holds disclosure potential. The trained model may be accessible to an attacker as: 1) White-box: accessing to the model architecture and parameters; 2) Black-box: where he can only query the model with his own inputs through an appropriate interface. Existing attack methods include: feature estimation attacks (FEA), membership inference attack (MIA), model memorization attack (MMA) and identification attacks (IA) [1]. In this work we focus on MIA against a model that has been trained to detect diabetic retinopathy from retinal images. Diabetic retinopathy is a condition that can cause vision loss and blindness in the people who have diabetes. MIA is the process of determining whether a data sample comes from the training data set of a trained ML model or not. From a privacy perspective in our use case where a diabetic retinopathy classification model is given to partners that have at their disposal images along with patients’ identifiers, inferring the membership status of a data sample can help to state if a patient has contributed or not to the training of the model.
Characterizing quantum criticality and steered coherence in the XY-Gamma chain

In this paper, we show that an effective spin Hamiltonian with various types of couplings can be engineered using quantum simulators in atomic-molecular-optical laboratories, dubbed the \emph{XY}-Gamma model. We analytically solve the one-dimensional short-range interacting case with the Jordan-Wigner transformation and establish the phase diagram. In the gapless phase, an incommensurate spiral order is manifested by the vector-chiral correlations. Between distinct gapped phases, a logarithmic scaling behavior of local measures, including spin correlations and the steered quantum coherence, is identified for the quantum critical points, yielding a compelling value of the correlation-length critical exponent. We derive explicit scaling forms of the excitation gap near the quantum critical points. The extracted critical exponents reveal the quantum phase transition on the boundary of Tomonaga-Luttinger liquid belongs to Lifshitz universality class.Our results may provide useful insights into the underlying mechanism in quantum criticality for state-of-the-art experiments of quantum simulation.
Simultaneous Enantiomer-Resolved Ramsey Spectroscopy for Chiral Molecules

We introduce a scheme to perform Ramsey spectroscopy on a racemic mixture of chiral molecules, simultaneously extracting the transition frequencies of the left- and right-handed molecules, known as enantiomers. By taking the diﬀerence between the enantio-speciﬁc frequencies, we isolate the weak force parity violation (PV) shift, which is predicted to break the symmetry between enantiomers. Ramsey interrogation of the two transitions is implemented by creating enantio-speciﬁc superpositions in a three level system using the enantiomer-dependent sign of the dipole moments. Our technique overcomes the need to alternate between enantio-pure samples to measure PV. We describe the advantages of the proposed method for precision metrology.
Towards Practical Differential Privacy in Data Analysis: Understanding the Effect of Epsilon on Utility in Private ERM

In this paper, we focus our attention on private Empirical Risk Minimization (ERM), which is one of the most commonly used data analysis method. We take the first step towards solving the above problem by theoretically exploring the effect of epsilon (the parameter of differential privacy that determines the strength of privacy guarantee) on utility of the learning model. We trace the change of utility with modification of epsilon and reveal an established relationship between epsilon and utility. We then formalize this relationship and propose a practical approach for estimating the utility under an arbitrary value of epsilon. Both theoretical analysis and experimental results demonstrate high estimation accuracy and broad applicability of our approach in practical applications. As providing algorithms with strong utility guarantees that also give privacy when possible becomes more and more accepted, our approach would have high practical value and may be likely to be adopted by companies and organizations that would like to preserve privacy but are unwilling to compromise on utility.
Technical Report (v1.0)--Pseudo-random Cartesian Sampling for Dynamic MRI

For an effective application of compressed sensing (CS), which exploits the underlying compressibility of an image, one of the requirements is that the undersampling artifact be incoherent (noise-like) in the sparsifying transform domain. For cardiovascular MRI (CMR), several pseudo-random sampling methods have been proposed that yield a high level of incoherence. In this technical report, we present a collection of five pseudo-random Cartesian sampling methods that can be applied to 2D cine and flow, 3D volumetric cine, and 4D flow imaging. Four out of the five presented methods yield fast computation for on-the-fly generation of the sampling mask, without the need to create and store pre-computed look-up tables. In addition, the sampling distribution is parameterized, providing control over the sampling density. For each sampling method in the report, (i) we briefly describe the methodology, (ii) list default values of the pertinent parameters, and (iii) provide a publicly available MATLAB implementation.
Counseling Summarization Using Mental Health Knowledge Guided Utterance Filtering

The psychotherapy intervention technique is a multifaceted conversation between a therapist and a patient. Unlike general clinical discussions, psychotherapy's core components (viz. symptoms) are hard to distinguish, thus becoming a complex problem to summarize later. A structured counseling conversation may contain discussions about symptoms, history of mental health issues, or the discovery of the patient's behavior. It may also contain discussion filler words irrelevant to a clinical summary. We refer to these elements of structured psychotherapy as counseling components. In this paper, the aim is mental health counseling summarization to build upon domain knowledge and to help clinicians quickly glean meaning. We create a new dataset after annotating 12.9K utterances of counseling components and reference summaries for each dialogue. Further, we propose ConSum, a novel counseling-component guided summarization model. ConSum undergoes three independent modules. First, to assess the presence of depressive symptoms, it filters utterances utilizing the Patient Health Questionnaire (PHQ-9), while the second and third modules aim to classify counseling components. At last, we propose a problem-specific Mental Health Information Capture (MHIC) evaluation metric for counseling summaries. Our comparative study shows that we improve on performance and generate cohesive, semantic, and coherent summaries. We comprehensively analyze the generated summaries to investigate the capturing of psychotherapy elements. Human and clinical evaluations on the summary show that ConSum generates quality summary. Further, mental health experts validate the clinical acceptability of the ConSum. Lastly, we discuss the uniqueness in mental health counseling summarization in the real world and show evidences of its deployment on an online application with the support of mpathic.ai
Optimization of Robot Trajectory Planning with Nature-Inspired and Hybrid Quantum Algorithms

We solve robot trajectory planning problems at industry-relevant scales. Our end-to-end solution integrates highly versatile random-key algorithms with model stacking and ensemble techniques, as well as path relinking for solution refinement. The core optimization module consists of a biased random-key genetic algorithm. Through a distinct separation of problem-independent and problem-dependent modules, we achieve an efficient problem representation, with a native encoding of constraints. We show that generalizations to alternative algorithmic paradigms such as simulated annealing are straightforward. We provide numerical benchmark results for industry-scale data sets. Our approach is found to consistently outperform greedy baseline results. To assess the capabilities of today's quantum hardware, we complement the classical approach with results obtained on quantum annealing hardware, using qbsolv on Amazon Braket. Finally, we show how the latter can be integrated into our larger pipeline, providing a quantum-ready hybrid solution to the problem.
Dark Matter Jets of Rotating Black Holes

We present a novel approach which produces Dark Matter jets along the rotation axis of Kerr black holes utilizing the Penrose process. The properties of these jets are investigated, as well as their potential to create Dark Matter overdensities in the solar system and in the vicinity of the black hole. We discover a highly collimated and long-range Dark Matter jet with a density that is most sensitive to the mass and distance to the black hole.
Language-Bridged Spatial-Temporal Interaction for Referring Video Object Segmentation

Referring video object segmentation aims to predict foreground labels for objects referred by natural language expressions in videos. Previous methods either depend on 3D ConvNets or incorporate additional 2D ConvNets as encoders to extract mixed spatial-temporal features. However, these methods suffer from spatial misalignment or false distractors due to delayed and implicit spatial-temporal interaction occurring in the decoding phase. To tackle these limitations, we propose a Language-Bridged Duplex Transfer (LBDT) module which utilizes language as an intermediary bridge to accomplish explicit and adaptive spatial-temporal interaction earlier in the encoding phase. Concretely, cross-modal attention is performed among the temporal encoder, referring words and the spatial encoder to aggregate and transfer language-relevant motion and appearance information. In addition, we also propose a Bilateral Channel Activation (BCA) module in the decoding phase for further denoising and highlighting the spatial-temporal consistent features via channel-wise activation. Extensive experiments show our method achieves new state-of-the-art performances on four popular benchmarks with 6.8% and 6.9% absolute AP gains on A2D Sentences and J-HMDB Sentences respectively, while consuming around 7× less computational overhead11https://github.com/dzh19990407/LBDT.