Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Lesion Volume Measurements on Digital Mammograms

Lesion volume is an important predictor for prognosis in breast cancer. We make a step towards a more accurate lesion volume measurement on digital mammograms by developing a model that allows to estimate lesion volumes on processed mammograms, which are the images routinely used by radiologists in clinical practice as well as in breast cancer screening and are available in medical centers. Processed mammograms are obtained from raw mammograms, which are the X-ray data coming directly from the scanner, by applying certain vendor-specific non-linear transformations. At the core of our volume estimation method is a physics-based algorithm for measuring lesion volumes on raw mammograms. We subsequently extend this algorithm to processed mammograms via a deep learning image-to-image translation model that produces synthetic raw mammograms from processed mammograms in a multi-vendor setting. We assess the reliability and validity of our method using a dataset of 1778 mammograms with an annotated mass. Firstly, we investigate the correlations between lesion volumes computed from mediolateral oblique and craniocaudal views, with a resulting Pearson correlation of 0.93 [95% confidence interval (CI) 0.92 - 0.93]. Secondly, we compare the resulting lesion volumes from true and synthetic raw data, with a resulting Pearson correlation of 0.998 [95% CI 0.998 - 0.998] . Finally, for a subset of 100 mammograms with a malign mass and concurrent MRI examination available, we analyze the agreement between lesion volume on mammography and MRI, resulting in an intraclass correlation coefficient of 0.81 [95% CI 0.73 - 0.87] for consistency and 0.78 [95% CI 0.66 - 0.86] for absolute agreement. In conclusion, we developed an algorithm to measure mammographic lesion volume that reached excellent reliability and good validity, when using MRI as ground truth.
Methods Matter: p-Hacking and Publication Bias in Causal Analysis in Economics: Comment

Brodeur, Cook, and Heyes (2020) study hypothesis tests from economic articles and find evidence for p-hacking and publication bias, in particular for instrumental variable and difference-in-difference studies. When adjusting for rounding errors (introducing a novel method), statistical evidence for p-hacking from randomization tests and caliper tests at the 5 percent significance threshold vanishes for difference-in-differnce studies but remains for instrumental variable studies. Results at the 1 percent and 10 percent significance thresholds remain largely similar. In addition, Brodeur, Cook, and Heyes derive latent distributions of z-statistics absent publication bias using two different approaches. We establish for each approach a result that challenges its applicability. (JEL A14, C12, C52)
A Risk-Aware Control: Integrating Worst-Case CVaR with Control Barrier Function

This paper proposes a risk-aware control approach to enforce safety for discrete-time nonlinear systems subject to stochastic uncertainties. We derive some useful results on the worst-case Conditional Value-at-Risk (CVaR) and define a discrete-time risk-aware control barrier function using the worst-case CVaR. On this basis, we present optimization-based control approaches that integrate the worst-case CVaR into the control barrier function, taking into account both safe set and tail risk considerations. In particular, three types of safe sets are discussed in detail: half-space, polytope, and ellipsoid. It is shown that control inputs for the half-space and polytopic safe sets can be obtained via quadratic programs, while control inputs for the ellipsoidal safe set can be computed via a semidefinite program. Through numerical examples of an inverted pendulum, we compare its performance with existing methods and demonstrate the effectiveness of our proposed controller.
Randomized and Deterministic Maximin-share Approximations for Fractionally Subadditive Valuations

We consider the problem of guaranteeing maximin-share (MMS) when allocating a set of indivisible items to a set of agents with fractionally subadditive (XOS) valuations. For XOS valuations, it has been previously shown that for some instances no allocation can guarantee a fraction better than $1/2$ of maximin-share to all the agents. Also, a deterministic allocation exists that guarantees $0.219225$ of the maximin-share of each agent. Our results involve both deterministic and randomized allocations. On the deterministic side, we improve the best approximation guarantee for fractionally subadditive valuations to $3/13 = 0.230769$. We develop new ideas on allocating large items in our allocation algorithm which might be of independent interest. Furthermore, we investigate randomized algorithms and the Best-of-both-worlds fairness guarantees. We propose a randomized allocation that is $1/4$-MMS ex-ante and $1/8$-MMS ex-post for XOS valuations. Moreover, we prove an upper bound of $3/4$ on the ex-ante guarantee for this class of valuations.
JL-lemma derived Optimal Projections for Discriminative Dictionary Learning

To overcome difficulties in classifying large dimensionality data with a large number of classes, we propose a novel approach called JLSPCADL. This paper uses the Johnson-Lindenstrauss (JL) Lemma to select the dimensionality of a transformed space in which a discriminative dictionary can be learned for signal classification. Rather than reducing dimensionality via random projections, as is often done with JL, we use a projection transformation matrix derived from Modified Supervised PC Analysis (M-SPCA) with the JL-prescribed dimension. JLSPCADL provides a heuristic to deduce suitable distortion levels and the corresponding Suitable Description Length (SDL) of dictionary atoms to derive an optimal feature space and thus the SDL of dictionary atoms for better classification. Unlike state-of-the-art dimensionality reduction-based dictionary learning methods, a projection transformation matrix derived in a single step from M-SPCA provides maximum feature-label consistency of the transformed space while preserving the cluster structure of the original data. Despite confusing pairs, the dictionary for the transformed space generates discriminative sparse coefficients, with fewer training samples. Experimentation demonstrates that JLSPCADL scales well with an increasing number of classes and dimensionality. Improved label consistency of features due to M-SPCA helps to classify better. Further, the complexity of training a discriminative dictionary is significantly reduced by using SDL. Experimentation on OCR and face recognition datasets shows relatively better classification performance than other supervised dictionary learning algorithms.
A Spatially Non-stationary Fading Channel Model for Simulation and (Semi-) Analytical Study of ELAA-MIMO

In this paper, a novel spatially non-stationary fading channel model is proposed for multiple-input multiple-output (MIMO) system with extremely-large aperture service-array (ELAA). The proposed model incorporates three key factors which cause the channel spatial non-stationarity: 1) link-wise path-loss; 2) shadowing effect; 3) line-of-sight (LoS)/non-LoS state. With appropriate parameter configurations, the proposed model can be used to generate computer-simulated channel data that matches the published measurement data from practical ELAA-MIMO channels. Given such appealing results, the proposed fading channel model is employed to study the cumulative distribution function (CDF) of ELAA-MIMO channel capacity. For all of our studied scenarios, it is unveiled that the ELAA-MIMO channel capacity obeys the skew-normal distribution. Moreover, the channel capacity is also found close to the Gaussian or Weibull distribution, depending on users' geo-location and distribution. More specifically, for single-user equivalent scenarios or multiuser scenarios with short user-to-ELAA distances (e.g., 1 m), the channel capacity is close to the Gaussian distribution; and for others, it is close to the Weibull distribution. Finally, the proposed channel model is also employed to study the impact of channel spatial non-stationarity on linear MIMO receivers through computer simulations. The proposed fading channel model is available at https://github.com/ELAA-MIMO/non-stationary-fading-channel-model.
Deep Learning for Visual Localization and Mapping: A Survey

Deep-learning-based localization and mapping approaches have recently emerged as a new research direction and receive significant attention from both industry and academia. Instead of creating hand-designed algorithms based on physical models or geometric theories, deep learning solutions provide an alternative to solve the problem in a data-driven way. Benefiting from the ever-increasing volumes of data and computational power on devices, these learning methods are fast evolving into a new area that shows potential to track self-motion and estimate environmental models accurately and robustly for mobile agents. In this work, we provide a comprehensive survey and propose a taxonomy for the localization and mapping methods using deep learning. This survey aims to discuss two basic questions: whether deep learning is promising for localization and mapping, and how deep learning should be applied to solve this problem. To this end, a series of localization and mapping topics are investigated, from the learning-based visual odometry and global relocalization to mapping, and simultaneous localization and mapping (SLAM). It is our hope that this survey organically weaves together the recent works in this vein from robotics, computer vision, and machine learning communities and serves as a guideline for future researchers to apply deep learning to tackle the problem of visual localization and mapping.
Utilizing Mood-Inducing Background Music in Human-Robot Interaction

Past research has clearly established that music can affect mood and that mood affects emotional and cognitive processing, and thus decision-making. It follows that if a robot interacting with a person needs to predict the person's behavior, knowledge of the music the person is listening to when acting is a potentially relevant feature. To date, however, there has not been any concrete evidence that a robot can improve its human-interactive decision-making by taking into account what the person is listening to. This research fills this gap by reporting the results of an experiment in which human participants were required to complete a task in the presence of an autonomous agent while listening to background music. Specifically, the participants drove a simulated car through an intersection while listening to music. The intersection was not empty, as another simulated vehicle, controlled autonomously, was also crossing the intersection in a different direction. Our results clearly indicate that such background information can be effectively incorporated in an agent's world representation in order to better predict people's behavior. We subsequently analyze how knowledge of music impacted both participant behavior and the resulting learned policy. \setcounter{footnote}{2}\footnote{An earlier version of part of the material in this paper appeared originally in the first author's Ph.D. Dissertation~\cite{liebman2020sequential} but it has not appeared in any pear-reviewed conference or journal. }
Implementing Snort Intrusion Prevention System (IPS) for Network Forensic Analysis

The security trade confidentiality, integrity and availability are the main pillar of the information systems as every organization emphasize of the security. From last few decades, digital data is the main asset for every digital or non-digital organization. The proliferation of easily accessible attack software on the internet has lowered the barrier for individuals without hacking skills to engage in malicious activities. An Industrial organization operates a server that (Confluence) serves as a learning platform for newly hired employees or Management training officers, thereby making it vulnerable to potential attacks using readily available internet-based software. To mitigate this risk, it is essential to implement a security system capable of detecting and preventing attacks, as well as conducting investigations. This research project aims to develop a comprehensive security system that can detect attack attempts, initiate preventive measures, and carry out investigations by analyzing attack logs. The study adopted a survey methodology and spanned a period of four months, from March 1, 2023, to June 31, 2023. The outcome of this research is a robust security system that effectively identifies attack attempts, blocks the attacker's IP address, and employs network forensic techniques for investigation purposes. The findings indicate that deploying Snort in IPS mode on PfSense enables the detection of attacks targeting e-learning servers, triggering automatic preventive measures such as IP address blocking. The alerts generated by Snort facilitate investigative actions through network forensics, allowing for accurate reporting on the detrimental effects of the attacks.
HH 80/81: Structure and Kinematics of the Fastest Protostellar Outflow

Hubble Space Telescope (HST) images obtained in 2018 are combined with archival HST data taken in 1995 to detect changes and measure proper motions in the HH 80/81 shock complex, which is powered by the fastest known jet driven by a forming star, the massive object IRAS 18162-2048. Some persistent features close to the radio jet axis have proper motions of >1000 km s−1 away from IRAS 18162-2048. About 3–5 pc downstream from the IRAS source and beyond HH 80/81, Hα emission traces the rim of a parsec-scale bubble blown by the jet. Lower speed motions are seen in [S ii] away from the jet axis; these features have a large component of motion at right angles to the jet. We identify new HH objects and H2 shocks in the counterflow opposite HH 80/81. The northeastern counterflow to HH 80/81 exhibits an extended but faint complex of 2.12 μm H2 shocks. The inner portion of the outflow is traced by dim 1.64 μm [Fe ii] emission. The full extent of this outflow is at least 1500″ (∼10 pc in projection at a distance of 1.4 kpc). We speculate about the conditions responsible for the production of the ultrafast jet and the absence of prominent large-scale molecular outflow lobes.
Can GitHub Issues Help in the App Review Classifications?

App reviews reflect various user requirements that can aid in planning maintenance tasks. Recently, proposed approaches for automatically classifying user reviews rely on machine learning algorithms. Devine et al. demonstrated that models trained on existing labeled datasets exhibit poor performance when predicting new ones. Although integrating datasets improves the results to some extent, there is still a need for greater generalizability to be taken into consideration. Therefore, a comprehensive labeled dataset is essential to train a more precise model. This paper introduces an approach to train a more generalizable model by leveraging information from an additional source, such as the GitHub issue tracking system, that contains valuable information about user requirements. We propose an approach that assists in augmenting labeled datasets by utilizing information extracted from GitHub issues. First, we identify issues concerning review intentions (bug reports, feature requests, and others) by examining the issue labels. Then, we analyze issue bodies and define 19 language patterns for extracting targeted information. Finally, we augment the manually labeled review dataset with a subset of processed issues through the Within-App, Within-Context, and Between-App Analysis methods. The first two methods train the app-specific models, and the last suits the general-purpose models. We conducted several experiments to evaluate the proposed approach. Our results demonstrate that using labeled issues for data augmentation can improve the F1-score and recall to 13.9 and 29.9 in the bug reports, respectively, and to 7.5 and 13.5 for feature requests. Furthermore, we identify an effective volume range of 0.3 to 0.7, which provides better performance improvements.
Proof-of-principle demonstration of temporally multiplexed quantum repeater link based on atomic ensemble

Duan-Lukin-Cirac-Zoller quantum repeater protocol provides a feasible scheme to implement long-distance quantum communication and large-scale quantum networks. The elementary link, namely the entanglement between two atomic ensembles, is a fundamental component of quantum repeater. For practical quantum repeater, it is required that the elementary link can be prepared with high yield and the spin waves stored in atoms can be efficiently converted into photons on demand. However, so far, such quantum repeater link has not been demonstrated in experiments. Here, we demonstrate a proof-of-principle multiplexed quantum repeater link by entangling two temporally multiplexed quantum memory. Compared with a single-mode link, the successful preparation rate of the multiplexed link is increased by one order of magnitude. By using the cavity-enhanced scheme, the on-demand retrieval efficiency of atomic spin waves is improved to 70%, which is beneficial for the subsequent entanglement swapping between adjacent links. The realization of temporally multiplexed quantum repeater link with high retrieval efficiency lays a foundation for the development of practical quantum networks.
Improved learning theory for kernel distribution regression with two-stage sampling

The distribution regression problem encompasses many important statistics and machine learning tasks, and arises in a large range of applications. Among various existing approaches to tackle this problem, kernel methods have become a method of choice. Indeed, kernel distribution regression is both computationally favorable, and supported by a recent learning theory. This theory also tackles the two-stage sampling setting, where only samples from the input distributions are available. In this paper, we improve the learning theory of kernel distribution regression. We address kernels based on Hilbertian embeddings, that encompass most, if not all, of the existing approaches. We introduce the novel near-unbiased condition on the Hilbertian embeddings, that enables us to provide new error bounds on the effect of the two-stage sampling, thanks to a new analysis. We show that this near-unbiased condition holds for three important classes of kernels, based on optimal transport and mean embedding. As a consequence, we strictly improve the existing convergence rates for these kernels. Our setting and results are illustrated by numerical experiments.
Interpolation of mountain weather forecasts by machine learning

Recent advances in numerical simulation methods based on physical models and their combination with machine learning have improved the accuracy of weather forecasts. However, the accuracy decreases in complex terrains such as mountainous regions because these methods usually use grids of several kilometers square and simple machine learning models. While deep learning has also made significant progress in recent years, its direct application is difficult to utilize the physical knowledge used in the simulation. This paper proposes a method that uses machine learning to interpolate future weather in mountainous regions using forecast data from surrounding plains and past observed data to improve weather forecasts in mountainous regions. We focus on mountainous regions in Japan and predict temperature and precipitation mainly using LightGBM as a machine learning model. Despite the use of a small dataset, through feature engineering and model tuning, our method partially achieves improvements in the RMSE with significantly less training time.
VideoCutLER: Surprisingly Simple Unsupervised Video Instance Segmentation

Existing approaches to unsupervised video instance segmentation typically rely on motion estimates and experience difficulties tracking small or divergent motions. We present VideoCutLER, a simple method for unsupervised multi-instance video segmentation without using motion-based learning signals like optical flow or training on natural videos. Our key insight is that using high-quality pseudo masks and a simple video synthesis method for model training is surprisingly sufficient to enable the resulting video model to effectively segment and track multiple instances across video frames. We show the first competitive unsupervised learning results on the challenging YouTubeVIS-2019 benchmark, achieving 50.7% APvideo^50 , surpassing the previous state-of-the-art by a large margin. VideoCutLER can also serve as a strong pretrained model for supervised video instance segmentation tasks, exceeding DINO by 15.9% on YouTubeVIS-2019 in terms of APvideo.
An Empirical Study of Consistency Regularization for End-to-End Speech-to-Text Translation

Consistency regularization methods, such as R-Drop (Liang et al., 2021) and CrossConST (Gao et al., 2023), have achieved impressive supervised and zero-shot performance in the neural machine translation (NMT) field. Can we also boost end-to-end (E2E) speech-to-text translation (ST) by leveraging consistency regularization? In this paper, we conduct empirical studies on intra-modal and cross-modal consistency and propose two training strategies, SimRegCR and SimZeroCR, for E2E ST in regular and zero-shot scenarios. Experiments on the MuST-C benchmark show that our approaches achieve state-of-the-art (SOTA) performance in most translation directions. The analyses prove that regularization brought by the intra-modal consistency, instead of modality gap, is crucial for the regular E2E ST, and the cross-modal consistency could close the modality gap and boost the zero-shot E2E ST performance.
Accurate complex Jacobi rotations

This note shows how to compute, to high relative accuracy under mild assumptions, complex Jacobi rotations for diagonalization of Hermitian matrices of order two, using the correctly rounded functions $\mathtt{cr\_hypot}$ and $\mathtt{cr\_rsqrt}$, proposed for standardization in the C programming language as recommended by the IEEE-754 floating-point standard. The rounding to nearest (ties to even) and the non-stop arithmetic are assumed. The numerical examples compare the observed with theoretical bounds on the relative errors in the rotations' elements, and show that the maximal observed departure of the rotations' determinants from unity is smaller than that of the transformations computed by LAPACK.
A Decomposition of Cylindric Partitions and Cylindric Partitions into Distinct Parts

We show that cylindric partitions are in one-to-one correspondence with a pair which has an ordinary partition and a colored partition into distinct parts. Then, we show the general form of the generating function for cylindric partitions into distinct parts and give some examples. We prove part of a conjecture by Corteel, Dousse, and Uncu. The approaches and proofs are elementary and combinatorial.