Miracle-dz/UniFAR-FTUs huggingface dataset:
Graph Enhanced Contrastive Learning for Radiology Findings Summarization

The impression section of a radiology report summarizes the most prominent observation from the findings section and is the most important section for radiologists to communicate to physicians. Summarizing findings is time-consuming and can be prone to error for inexperienced radiologists, and thus automatic impression generation has attracted substantial attention. With the encoder-decoder framework, most previous studies explore incorporating extra knowledge (e.g., static pre-defined clinical ontologies or extra background information). Yet, they encode such knowledge by a separate encoder to treat it as an extra input to their models, which is limited in leveraging their relations with the original findings. To address the limitation, we propose a unified framework for exploiting both extra knowledge and the original findings in an integrated way so that the critical information (i.e., key words and their relations) can be extracted in an appropriate way to facilitate impression generation. In detail, for each input findings, it is encoded by a text encoder and a graph is constructed through its entities and dependency tree. Then, a graph encoder (e.g., graph neural networks (GNNs)) is adopted to model relation information in the constructed graph. Finally, to emphasize the key words in the findings, contrastive learning is introduced to map positive samples (constructed by masking non-key words) closer and push apart negative ones (constructed by masking key words). The experimental results on two datasets, OpenI and MIMIC-CXR, confirm the effectiveness of our proposed method, where the state-of-the-art results are achieved.
Quantized GAN for Complex Music Generation from Dance Videos

We present Dance2Music-GAN (D2M-GAN), a novel adversarial multi-modal framework that generates complex musical samples conditioned on dance videos. Our proposed framework takes dance video frames and human body motions as input, and learns to generate music samples that plausibly accompany the corresponding input. Unlike most existing conditional music generation works that generate specific types of mono-instrumental sounds using symbolic audio representations (e.g., MIDI), and that usually rely on pre-defined musical synthesizers, in this work we generate dance music in complex styles (e.g., pop, breaking, etc.) by employing a Vector Quantized (VQ) audio representation, and leverage both its generality and high abstraction capacity of its symbolic and continuous counterparts. By performing an extensive set of experiments on multiple datasets, and following a comprehensive evaluation protocol, we assess the generative qualities of our proposal against alternatives. The attained quantitative results, which measure the music consistency, beats correspondence, and music diversity, demonstrate the effectiveness of our proposed method. Last but not least, we curate a challenging dance-music dataset of in-the-wild TikTok videos, which we use to further demonstrate the efficacy of our approach in real-world applications -- and which we hope to serve as a starting point for relevant future research.
Exotic cyclic cohomology classes and Lipschitz algebras

We study the noncommutative geometry of algebras of Lipschitz continuous and H\"older continuous functions where non-classical and novel differential geometric invariants arise. Indeed, we introduce a new class of Hochschild and cyclic cohomology classes that pair non-trivially with higher algebraic $K$-theory yet vanish when restricted to the algebra of smooth functions. Said cohomology classes provide additional methods to extract numerical invariants from Connes-Karoubi's relative sequence in $K$-theory.
Considering the two Spin and the two Angular Momenta String Solutions in $AdS_5 \times S^5$

S In this paper, we consider two almost opposite sectors of actual string conﬁg-uration ans¨atze in AdS 5 × S 5 , which anyway have almost the same features: The two spin solution, which has constant angles in S 5 and the two angular momenta solution, which has constant ”angels” in AdS 5 , however for the two angular momenta solution, we have to take the time coordinate from AdS 5 , thus there is a little asymmetry between the two string conﬁgurations in AdS 5 × S 5 . Without being autistic, there is around 69 equations between the similar equations in the two sectors, compare equations (34) and (104) and also compare equations (64) and (134). Again, without being autistic, the text after the equations in the two sectors, is almost precisely the same. In our notation, the diﬀerence between the two sectors is as follows; The string conﬁgurations of this paper, are both solvable by the Neumann System. However, our setup in this paper is generally for the Neumann-Rosochatius System, which is also solvable, since we intend to generalize our results from the Neumann System to the Neumann-Rosochatius System and to several types of deformed Neumann-Rosochatius Systems. In the second part of this paper, which is independent of any string conﬁgurations in AdS 5 × S 5 and concerns String Cosmology in D = 10 dimensions. I will seriously argue that there was no Big Bang; I truly believe that the Universe has been there forever!
Differential current-based fault protection with adaptive threshold for multiple PV-based DC microgrid

A new differential current-based fast fault detection and accurate fault distance calculation is proposed for photovoltaic (PV)-based DC microgrid. A multiterminal direct current (MTDC) distribution network is studied as an adequate solution for present low-voltage utility grid scenario, where local distributed generators (DGs) are incorporated primarily by power electronics based DC-DC converters, DC-AC voltage-source converters (VSCs). PV and diesel generator (as auxiliary source) are considered for cascaded common DC bus, and AC utility bus integration is achieved by VSC unit for the proposed MTDC network. DC microgrid protection is quite significant research focus due to the absence of well-defined standards. Pole-to-pole, pole-to-ground, PV-side DC series and ground arc faults are basically considered as DC distribution network hazards. A discrete model differential current solution is considered to detect, classify and locate the faults by modified cumulative sum average approach. A comprehensive case study is presented with different DC loadings, to deliberate effectiveness of the proposed protection scheme in terms of percentage error and trip time ( Ts ). The result verification is conducted in MATLAB environment as well as TMS320C6713 digital signal processor- based test bench with the proposed multiple DGs based DC microgrid.
Estimation and inference for high-dimensional nonparametric additive instrumental-variables regression

The method of instrumental variables provides a fundamental and practical tool for causal inference in many empirical studies where unmeasured confounding between the treatments and the outcome is present. Modern data such as the genetical genomics data from these studies are often high-dimensional. The high-dimensional linear instrumental-variables regression has been considered in the literature due to its simplicity albeit a true nonlinear relationship may exist. We propose a more data-driven approach by considering the nonparametric additive models between the instruments and the treatments while keeping a linear model between the treatments and the outcome so that the coefficients therein can directly bear causal interpretation. We provide a two-stage framework for estimation and inference under this more general setup. The group lasso regularization is first employed to select optimal instruments from the high-dimensional additive models, and the outcome variable is then regressed on the fitted values from the additive models to identify and estimate important treatment effects. We provide non-asymptotic analysis of the estimation error of the proposed estimator. A debiasing procedure is further employed to yield valid inference. Extensive numerical experiments show that our method can rival or outperform existing approaches in the literature. We finally analyze the mouse obesity data and discuss new findings from our method.
Three-manifolds with non-negatively pinched Ricci curvature

We show that every complete non-compact three-manifold with non-negatively pinched Ricci curvature admits a complete Ricci ﬂow solution for all positive time, with scale-invariant curvature decay and preserva-tion of pinching. Combining with recent work of Lott and Deruelle-Schulze-Simon gives a proof of Hamilton’s pinching conjecture without additional hypotheses.
The MURALES survey. VI. Properties and origin of the extended line emission structures in radio galaxies

This is the sixth paper presenting the results of the MUse RAdio Loud Emission line Snapshot survey. We observed 37 radio sources from the 3C sample with z < 0 . 3 and a declination < 20 ◦ with the Multi Unit Spectroscopic Explorer optical integral ﬁeld spec-trograph at the Very Large Telescope. Here, we focus on the properties of the extended emission line regions (EELRs) that can be studied with unprecedented detail thanks to the depth of these observations. Line emission in the ten FR Is is, in most cases, conﬁned to within (cid:46) 4kpc, while large-scale ( (cid:38) 4kpc) ionized gas is seen in all but two of the 26 FR IIs. It usually takes the form of elongated or ﬁlamentary structures, typically extending between 10 and 30kpc, but also reaching distances of ∼ 80kpc. We ﬁnd that the large-scale ionized gas structures show a tendency to be oriented at large angles from the radio axis, and that the gas on a scale of a few kiloparsecs from the nucleus often shows ordered rotation with a kinematical axis forming a median angle of 65 ◦ with the radio axis. We also discuss the velocity ﬁeld and ionization properties of the EELRs. The observed emission line structures appear to be associated with gaseous “superdisks” that formed after a gas-rich merger. The di ﬀ erent properties of the EELR can be explained with a combination of the source evolutionary state and the orientation of the superdisk with respect to the radio axis. The general alignment between the superdisks and radio axis might be produced by stable and coherent accretion maintained over long timescales.
Free-riding and mitigation by future political representation

A number of recent studies have studied incentives for free-riding in connection with local government consolidations. This paper contributes with a study of how these incentives can be mitigated by future political representation, by which is meant how a local area stands to be represented in a future, consolidated political assembly. This is examined on recent municipal consolidations in Denmark and a novelty of this case is that the municipal election period was extended by a year after elections were held. In this extra fiscal year, local politicians in the consolidating municipal councils knew with certainty the composition of the new, consolidated council, while they at the same time retained decision-making power (subject to some restrictions). I examine a panel of 217 municipalities over the five-year municipal election period from 2002 to 2006 and the findings show evidence of free-riding, and that free-riding is mitigated by future representation in the council of the consolidated municipality. The findings suggest that, on the one hand, it can be problematic to add an extra fiscal year after elections have been held; but also, on the other hand, that mitigation of free-riding by future representation constitutes a local solution to common pool problems.
Estimates for stress concentration between two adjacent rigid inclusions in two-dimensional Stokes flow

It is vital important in material sciences and fluid mechanics to study the field enhancements in the narrow region between two inclusions. Complex fluids including particle suspensions usually result in complicated flow behavior. In this paper we establish the pointwise upper bounds of the gradient and the second-order partial derivatives for the Stokes flow when two rigid particles are closely spaced suspending in an open bounded domain and away from the boundary in dimension two. Moreover, the lower bounds of the gradient estimates at the narrowest place of the neck region show the optimality of the blow-up rate. These results are valid for inclusions with arbitrary shape.
Neutrino driven explosions aided by axion cooling in multidimensional simulations of core-collapse supernovae

In this study, we present the first multidimensional core-collapse supernovae (CCSNe) simulations including QCD axions in order to assess the impact on the CCSN explosion mechanism. We include axions in our simulations through the nucleon-nucleon bremsstrahlung emission channel and as a pure energy-sink term under the assumption that the axions free-stream after being emitted. We perform both spherically symmetric (1D) and axisymmetric (2D) simulations. In 1D, we utilize a parametrized heating scheme to achieve explosions, whereas in 2D we self-consistently realize explosions through the neutrino heating mechanism. Our 2D results for a 20 M ⊙ progenitor show an impact of the axion emission on the shock behavior and the explosion time when considering values of the PecceiQuinn energy scale f a ≤ 2 × 10 8 GeV. The strong cooling due to the axion emission accelerates the contraction of the core and leads to more efficient neutrino heating and earlier explosions. For the axion emission formalism utilized, the values of f a that impact the explosion are close to, but in tension with current limits based on the neutrinos detected from SN 1987A. However, given the nonlinear behavior of the emission and the multidimensional nature of CCSNe,we suggest that a self-consistent, multidimensional approach to simulating CCSNe, including any late time accretion and cooling, is needed to fully explore the axion bounds from supernovae and the impact on the CCSN explosion mechanism.
Semi-Weakly Supervised Object Detection by Sampling Pseudo Ground-Truth Boxes

Semi- and weakly-supervised learning have recently attracted considerable attention in the object detection literature since they can alleviate the cost of annotation needed to successfully train deep learning models. State-of-art approaches for semi-supervised learning rely on student-teacher models trained using a multi-stage process, and considerable data augmentation. Custom networks have been developed for the weakly-supervised setting, making it difficult to adapt to different detectors. In this paper, a weakly semi-supervised training method is introduced that reduces these training challenges, yet achieves state-of-the-art performance by leveraging only a small fraction of fully-labeled images with information in weakly-labeled images. In particular, our generic sampling-based learning strategy produces pseudo ground-truth (GT) bounding box annotations in an online fashion, eliminating the need for multi-stage training, and student-teacher network configurations. These pseudo GT boxes are sampled from weakly-labeled images based on the categorical score of object proposals accumulated via a score propagation process. Empirical results11Our code is available at: https://github.com/akhilpm/SemiWSOD on the Pascal VOC dataset, indicates that the proposed approach improves performance by 5.0% when using VOC 2007 as fully-labeled, and VOC 2012 as weak-labeled data. Also, with 5-10% fully annotated images, we observed an improvement of more than 10% in $m$AP, showing that a modest investment in image-level annotation, can substantially improve detection performance.
QUIP: Query-driven Missing Value Imputation

Missing values widely exist in real-world data sets, and failure to clean the missing data may result in the poor quality of answers to queries. \yiming{Traditionally, missing value imputation has been studied as an offline process as part of preparing data for analysis. } This paper studies query-time missing value imputation and proposes QUIP, which only imputes minimal missing values to answer the query. Specifically, by taking a reasonable good query plan as input, QUIP tries to minimize the missing value imputation cost and query processing overhead. QUIP proposes a new implementation of outer join to preserve missing values in query processing and a bloom filter based index structure to optimize the space and runtime overhead. QUIP also designs a cost-based decision function to automatically guide each operator to impute missing values now or delay imputations. Efficient optimizations are proposed to speed-up aggregate operations in QUIP, such as MAX/MIN operator. Extensive experiments on both real and synthetic data sets demonstrates the effectiveness and efficiency of QUIP, which outperforms the state-of-the-art ImputeDB by 2 to 10 times on different query sets and data sets, and achieves the order-of-magnitudes improvement over the offline approach.
Continuous Integration of Data Histories into Consistent Namespaces

We describe a policy-based approach to the scaling of shared data services, using a hierarchy of calibrated data pipelines to automate the continuous integration of data flows. While there is no unique solution to the problem of time order, we show how to use a fair interleaving to reproduce reliable `latest version' semantics in a controlled way, by trading locality for temporal resolution. We thus establish an invariant global ordering from a spanning tree over all shards, with controlled scalability. This forms a versioned coordinate system (or versioned namespace) with consistent semantics and self-protecting rate-limited versioning, analogous to publish-subscribe addressing schemes for Content Delivery Network (CDN) or Name Data Networking (NDN) schemes.
The Mu3e Experiment

The Mu3e experiment at the Paul Scherrer Institut will search for the lepton-number-violating decay \mueee, extending the sensitivity by four orders of magnitude compared to existing limits. This probe of new physics is complementary to the existing collider, dark matter and neutrino particle physics programmes, and part of a global programme investigating the charged lepton flavour sector. As well as the main \mueee search, Mu3e will also extend the sensitivity to low-mass dark photons, and additional flavour-violating decays involving long-lived or stable particles.
A Gaia View on the Star Formation in the Monoceros OB1 and R1 Associations

Stellar kinematics provides the key to understanding the star formation process. In this respect, we present a kinematic study of the Monoceros OB1 (Mon OB1) and R1 (Mon R1) associations using the recent Gaia data and radial velocities of stars derived from high-resolution spectroscopy and the literature. A total of 728 members are selected using the criteria based on the intrinsic properties of young stars, parallaxes, and proper motions. The spatial distribution and kinematic properties of members show that these associations have distinct substructures. In Mon OB1, we find one northern group and two southern groups. Mon R1 is composed of three small stellar groups that are spatially and kinematically distinct. Some stars are found in a halo around these two associations. We detect patterns of expansion for most stellar groups in the associations. In addition, two stellar groups in Mon OB1 show the signature of rotation, which provides an important constraint on cluster formation. The star formation history of Mon OB1 is slightly revised. Star formation first occurred in the southern region and subsequently in the northern region. Recent star-forming events ignited deeper into the southern region, while some stars are escaping from Mon OB1, forming a halo. Mon R1 might have formed at the same epoch as the formation of the northern group in Mon OB1. Given that star formation is taking place on different scales along a large arc-like structure, Mon OB1 and Mon R1 may be the results of hierarchical star formation.
Measuring Diagnostic Test Performance Using Imperfect Reference Tests: A Partial Identification Approach

Diagnostic tests are almost never perfect. Studies quantifying their performance use knowledge of the true health status, measured with a reference diagnostic test. Researchers commonly assume that the reference test is perfect, which is not the case in practice. When the assumption fails, conventional studies identify"apparent"performance or performance with respect to the reference, but not true performance. This paper provides the smallest possible bounds on the measures of true performance - sensitivity (true positive rate) and specificity (true negative rate), or equivalently false positive and negative rates, in standard settings. Implied bounds on policy-relevant parameters are derived: 1) Prevalence in screened populations; 2) Predictive values. Methods for inference based on moment inequalities are used to construct uniformly consistent confidence sets in level over a relevant family of data distributions. Emergency Use Authorization (EUA) and independent study data for the BinaxNOW COVID-19 antigen test demonstrate that the bounds can be very informative. Analysis reveals that the estimated false negative rates for symptomatic and asymptomatic patients are up to 3.17 and 4.59 times higher than the frequently cited"apparent"false negative rate.
FrequencyLowCut Pooling - Plug & Play against Catastrophic Overfitting

Over the last years, Convolutional Neural Networks (CNNs) have been the dominating neural architecture in a wide range of computer vision tasks. From an image and signal processing point of view, this success might be a bit surprising as the inherent spatial pyramid design of most CNNs is apparently violating basic signal processing laws, i.e. Sampling Theorem in their down-sampling operations. However, since poor sampling appeared not to affect model accuracy, this issue has been broadly neglected until model robustness started to receive more attention. Recent work [17] in the context of adversarial attacks and distribution shifts, showed after all, that there is a strong correlation between the vulnerability of CNNs and aliasing artifacts induced by poor down-sampling operations. This paper builds on these findings and introduces an aliasing free down-sampling operation which can easily be plugged into any CNN architecture: FrequencyLowCut pooling. Our experiments show, that in combination with simple and fast FGSM adversarial training, our hyper-parameter free operator significantly improves model robustness and avoids catastrophic overfitting.
Partitioning a 2-edge-coloured graph of minimum degree $2n/3 + o(n)$ into three monochromatic cycles

Lehel conjectured in the 1970s that every red and blue edge-coloured complete graph can be partitioned into two monochromatic cycles. This was conﬁrmed in 2010 by Bessy and Thomass´e. However, the host graph G does not have to be complete. It it suﬃces to require that G has minimum degree at least 3 n/ 4, where n is the order of G , as was shown recently by Letzter, conﬁrming a conjecture of Balogh, Bar´at, Gerbner, Gy´arf´as and S´ark¨ozy. This degree condition is asymptotically tight. Here we continue this line of research, by proving that for every red and blue edge-colouring of an n -vertex graph of minimum degree at least 2 n/ 3 + o ( n ), there is a partition of the vertex set into three monochromatic cycles. This approximately veriﬁes a conjecture of Pokrovskiy and is essentially tight.