Miracle-dz/UniFAR-FTUs huggingface dataset:
IMRAM: Iterative Matching With Recurrent Attention Memory for Cross-Modal Image-Text Retrieval

Enabling bi-directional retrieval of images and texts is important for understanding the correspondence between vision and language. Existing methods leverage the attention mechanism to explore such correspondence in a fine-grained manner. However, most of them consider all semantics equally and thus align them uniformly, regardless of their diverse complexities. In fact, semantics are diverse (i.e. involving different kinds of semantic concepts), and humans usually follow a latent structure to combine them into understandable languages. It may be difficult to optimally capture such sophisticated correspondences in existing methods. In this paper, to address such a deficiency, we propose an Iterative Matching with Recurrent Attention Memory (IMRAM) method, in which correspondences between images and texts are captured with multiple steps of alignments. Specifically, we introduce an iterative matching scheme to explore such fine-grained correspondence progressively. A memory distillation unit is used to refine alignment knowledge from early steps to later ones. Experiment results on three benchmark datasets, i.e. Flickr8K, Flickr30K, and MS COCO, show that our IMRAM achieves state-of-the-art performance, well demonstrating its effectiveness. Experiments on a practical business advertisement dataset, named KWAI-AD, further validates the applicability of our method in practical scenarios.
Divided Differences, Falling Factorials, and Discrete Splines: Another Look at Trend Filtering and Related Problems

This paper serves as a postscript of sorts to Tibshirani (2014); Wang et al. (2014), who developed continuous-time formulations and properties of trend filtering, a discrete-time smoothing tool proposed (independently) by Steidl et al. (2006); Kim et al. (2009). The central object of study is the falling factorial basis, as it was called by Tibshirani (2014); Wang et al. (2014). Its span turns out to be a space of piecewise polynomials that has a classical place in spline theory, called discrete splines (Mangasarian and Schumaker, 1971, 1973; Schumaker, 2007). At the Tibshirani (2014); Wang et al. (2014), we were not fully aware of these connections. The current paper attempts to rectify this by making these connections explicit, reviewing (and making use of) some of the important existing work on discrete splines, and contributing several new perspectives and new results on discrete splines along the way.
Searching for multiple populations in the integrated light of the young and extremely massive clusters in the merger remnant NGC 7252

Recent work has shown that the properties of multiple populations (MPs) within massive stellar clusters (i.e. in the extent of their abundance variations as well as the fraction of stars that show the anomalous chemistry) depend on the mass as well as the age of the host cluster. Such correlations are largely unexpected in current models for the formation of MPs and hence provide essential insight into their origin. Here, we extend our previous study into the presence or absence of MPs using integrated light spectroscopy of the ∼600 Myr, massive (∼107–108 M⊙) clusters, W3 and W30, in the galactic merger remnant, NGC 7252. Due to the extreme mass of both clusters, the expectation is that they should host rather extreme abundance spreads, manifested through high mean [Na/Fe] abundances. However, we do not find evidence for a strong [Na/Fe] enhancement, with the observations being consistent with the solar value. This suggests that age is playing a key role, or alternatively that MPs only manifest below a certain stellar mass, as the integrated light at all ages above ∼100 Myr is dominated by stars near or above the main-sequence turn-off.
Factors influencing ovarian cancer survival worldwide.

Ovarian cancer survival varies widely worldwide. This variation may be explained by several factors, including international variation in the histological subtypes of ovarian cancer, stage at diagnosis and race/ethnicity. Data used for this thesis were extracted from the CONCORD-2 study. The CONCORD-2 study collected data for 793,098 adult women (aged 15-99 years) in 61 countries who were diagnosed during the 15-year period 1995-2009 with a cancer of the ovary. Ovarian cancer was defined broadly to include tumours of the fallopian tube, uterine ligaments and adnexa, other specified and unspecified female genital organs, peritoneum or retroperitoneum. Age-standardised net survival was the main outcome for each analysis. The worldwide distribution of and international variation in histological groups of ovarian cancer was examined, as an approach to understanding international differences in overall ovarian cancer survival. International comparisons of ovarian cancer survival have traditionally analysed ovarian cancer as a single homogenous group. However, ovarian cancer comprises several histologically distinct subtypes, which have very different survival outcomes. Survival from the most common histology, type II epithelial, was much lower than that for other histological groups in most countries. International differences in stage-specific net survival were also explored, where adequate data were available, in order to understand the impact of stage at diagnosis on survival. Survival from localised tumours was much higher overall, and for each histological group, than for advanced-stage disease in all countries. Net survival by race was estimated for Israel, New Zealand and the United States. Survival was consistently higher for the majority racial group than for the minority group. The results presented in this thesis provide a valuable contribution to the understanding of variations in ovarian cancer survival, which may thus be used to inform health care policies and plans to reduce disparities in survival.
Adaptive Extremum Seeking Using Recursive Least Squares

Extremum seeking (ES) optimization approach has been very popular due to its non-model based analysis and implementation. This approach has been mostly used with gradient based search algorithms. Since least squares (LS) algorithms are typically observed to be superior, in terms of convergence speed and robustness to measurement noises, over gradient algorithms, it is expected that LS based ES schemes will also provide faster convergence and robustness to sensor noises. In this paper, with this motivation, a recursive least squares (RLS) estimation based ES scheme is designed and analysed for application to scalar parameter and vector parameter static map and dynamic systems. Asymptotic convergence to the extremum is established for all the cases. Simulation studies are provided to validate the performance of proposed scheme.
Relação entre função pulmonar e as propriedades musculares em pacientes com doença pulmonar obstrutiva crônica Relationship between lung function and muscle properties in patients with chronic obstructive pulmonary disease

Objective: To determine the interrelationships between lung function and attributes mass and muscle strength in patients with chronic obstructive pulmonary disease (COPD). Method: 22 patients with COPD underwent spirometry evaluation, body composition by bioelectrical impedance and strength of the quadriceps muscle by the analysis of maximal voluntary contraction (MVC). To test the relationships between indicators forced expiratory volume in one second (FEV1), lean mass (LM) and CVM Pearson correlations were performed. Results: There were significant relationships between FEV1 and MM (r = 0.48, p = 0.02), FEV1 and MVC (r = 0.45, p = 0.05), as well as MM and MVC (r = 0.67, p = 0.002). Conclusion: The results of this study demonstrate that in patients with stable moderate COPD, obstructive respiratory disorder, evidenced by spirometric indicator FEV1 was related to the attributes of mass and peripheral muscle strength, and the decline of force was proportional to muscle mass remaining.
Adversarial Online Learning with Changing Action Sets: Efficient Algorithms with Approximate Regret Bounds

We revisit the problem of online learning with sleeping experts/bandits: in each time step, only a subset of the actions are available for the algorithm to choose from (and learn about). The work of Kleinberg et al. [2010] showed that there exist no-regret algorithms which perform no worse than the best ranking of actions asymptotically. Unfortunately, achieving this regret bound appears computationally hard: Kanade and Steinke [2014] showed that achieving this no-regret performance is at least as hard as PAC-learning DNFs, a notoriously difficult problem. In the present work, we relax the original problem and study computationally efficient no-approximate-regret algorithms: such algorithms may exceed the optimal cost by a multiplicative constant in addition to the additive regret. We give an algorithm that provides a no-approximate-regret guarantee for the general sleeping expert/bandit problems. For several canonical special cases of the problem, we give algorithms with significantly better approximation ratios; these algorithms also illustrate different techniques for achieving no-approximate-regret guarantees.
Relationship of fusion protein status and outcome for children with intermediate-risk rhabdomyosarcoma: A Children's Oncology Group report.

9535 Background: Rhabdomyosarcoma (RMS) includes both alveolar (ARMS) and embryonal (ERMS) subtypes, with ARMS generally having a worse prognosis. Most ARMS express one of two fusion genes generated by balanced translocations fusing DNA binding domains of PAX3 or PAX7 with FOXO1 (P3F and P7F, respectively). The prognostic value of fusion gene versus histological subtype is not clear. We carried out a multi-institutional clinical trial through the Children’s Oncology Group (COG) to evaluate this molecular feature. Methods: All participants were enrolled on COG D9803 from 1999-2005 for children and young adults with intermediate risk ARMS or ERMS, and were treated with vincristine, dactinomycin, and cyclophosphamide (+/- topotecan), radiotherapy, and surgery. Diagnostic specimens were reviewed centrally and fusion gene was assessed by fluorescence in situ hybridization (FISH), RT-PCR, or both. Event-free (EFS) and overall survival (OAS) at 5 years (yr) was correlated with histology and fusion gene status. R.
Secure Cloud Storage with Client-Side Encryption Using a Trusted Execution Environment

With the evolution of computer systems, the amount of sensitive data to be stored as well as the number of threats on these data grow up, making the data confidentiality increasingly important to computer users. Currently, with devices always connected to the Internet, the use of cloud data storage services has become practical and common, allowing quick access to such data wherever the user is. Such practicality brings with it a concern, precisely the confidentiality of the data which is delivered to third parties for storage. In the home environment, disk encryption tools have gained special attention from users, being used on personal computers and also having native options in some smartphone operating systems. The present work uses the data sealing, feature provided by the Intel Software Guard Extensions (Intel SGX) technology, for file encryption. A virtual file system is created in which applications can store their data, keeping the security guarantees provided by the Intel SGX technology, before send the data to a storage provider. This way, even if the storage provider is compromised, the data are safe. To validate the proposal, the Cryptomator software, which is a free client-side encryption tool for cloud files, was integrated with an Intel SGX application (enclave) for data sealing. The results demonstrate that the solution is feasible, in terms of performance and security, and can be expanded and refined for practical use and integration with cloud synchronization services.
Identification of relevant performance indicators in round-robin tournaments

A myriad of different data are generated to characterize a soccer match. Here we discuss which performance indicators are particularly helpful to forecast the future results of a team via an estimation of the underlying team strengths with minimum statistical uncertainty. We introduce an appropriate statistical framework and exemplify it for different performance indicators for the German premier soccer league. Two aspects are involved: (i) It is quantified how well the estimation process would work if no statistical noise due to finite information is present. The related score directly expresses to which degree the chosen performance indicator reflects the underlying team strength. (ii) Additionally, the reduction of the forecasting quality due to statistical noise is determined. From both pieces of information a normalized value can be constructed which is a direct measure of the overall forecasting quality. It turns out that the so-called packing rate works best. New perspectives of performance indicators enter when trying to understand the outcome of single matches based on match-specific observations from the same match. Implications for the purpose of forecasting as well as consequences for the interpretation of team strengths are discussed.
Phenomenological theory in reentrant uranium-based superconductors

We develop a phenomenological theory for a family of uranium-based heavy fermion superconductors $(\mathrm{URhGe}, \mathrm{UCoGe}, \mathrm{and} {\text{UTe}}_{2})$. The theory unifies the understanding of both superconductivity (SC) with a weak magnetic field and reentrant superconductivity (RSC) that appears near the first-order transition line with a high magnetic field. It is shown that the magnetizations along the easy and hard axes have opposite effects on SC. The RSC is induced by the fluctuation parallel to the direction of the magnetic field. The theory makes specific predictions about the variation of triplet SC order parameters $\stackrel{P\vec}{d}$ with applied external magnetic fields and the existence of a metastable state for the appearance of the RSC.
Error Correction with Systematic RLNC in Multi-Channel THz Communication Systems

The terahertz (THz) frequency band (0.3-lOTHz) has the advantage of large available bandwidth and is a candidate to satisfy the ever increasing mobile traffic in wireless communications. However, the THz channels are often absorbed by molecules in the atmosphere, which can decrease the signal quality resulting in high bit error rate of received data. In this paper, we study the usage of systematic random linear network coding (sRLNC) for error correction in generic THz systems with with 2N parallel channels, whereby N main high-bitrate channels are used in parallel with N auxiliary channels with lower bit rate. The idea behind this approach is to use low-bit rate channels to carry coding redundancy generated based on data sent over high-bit rate channels. The coding redundancy can be used at destination to compensate errors occurred during THz transmission. The presented analysis evaluates different settings of THz transmission system in term of the amount of coding redundancy, a code rate, required bit rate of auxiliary channels, the modulation format and transmission distance as required system configurations for a fault tolerant THz transmission.
Smoking and Self-justification among University Students

Abstract The present study explored university students’ perceptions of cigarette smoking. A sample of 60 participants who were attending a leadership training course for student leaders was selected. A 16 – item questionnaire was administered to the participants to gather their opinions on cigarette smoking. The study found that there was a high prevalence of cigarette smoking among the students. The onset of smoking was high at high school with a comparable proportion at university level. The main agents of influences were peers, television and radio. There was a high level of awareness of the effects of cigarette smoking, although, perceived benefits were cited as selfjustification for smoking. Most of the participants expressed willingness to quit smoking and suggested the use of lectures and graphic images as effective strategies for anti-smoking campaigns. The study recommended further research to explore gender differentiation on smoking.
Super Resolution Using Segmentation-Prior Self-Attention Generative Adversarial Network

Convolutional Neural Network (CNN) is intensively implemented to solve super resolution (SR) tasks because of its superior performance. However, the problem of super resolution is still challenging due to the lack of prior knowledge and small receptive field of CNN. We propose the Segmentation-Piror Self-Attention Generative Adversarial Network (SPSAGAN) to combine segmentation-priors and feature attentions into a unified framework. This combination is led by a carefully designed weighted addition to balance the influence of feature and segmentation attentions, so that the network can emphasize textures in the same segmentation category and meanwhile focus on the long-distance feature relationship. We also propose a lightweight skip connection architecture called Residual-in-Residual Sparse Block (RRSB) to further improve the super-resolution performance and save computation. Extensive experiments show that SPSAGAN can generate more realistic and visually pleasing textures compared to state-of-the-art SFTGAN and ESRGAN on many SR datasets.
Prediction with Spatio-temporal Point Processes with Self Organizing Decision Trees

We study the spatio-temporal prediction problem, which has attracted the attention of many researchers due to its critical real-life applications. In particular, we introduce a novel approach to this problem. Our approach is based on the Hawkes process, which is a non-stationary and self-exciting point process. We extend the formulations of a standard point process model that can represent time-series data to represent a spatio-temporal data. We model the data as nonstationary in time and space. Furthermore, we partition the spatial region we are working on into subregions via an adaptive decision tree and model the source statistics in each subregion with individual but mutually interacting point processes. We also provide a gradient based joint optimization algorithm for the point process and decision tree parameters. Thus, we introduce a model that can jointly infer the source statistics and an adaptive partitioning of the spatial region. Finally, we provide experimental results on real-life data, which provides significant improvement due to space adaptation and joint optimization compared to standard well-known methods in the literature.
Better Captioning With Sequence-Level Exploration

Sequence-level learning objective has been widely used in captioning tasks to achieve the state-of-the-art performance for many models. In this objective, the model is trained by the reward on the quality of its generated captions (sequence-level). In this work, we show the limitation of the current sequence-level learning objective for captioning tasks from both theory and empirical result. In theory, we show that the current objective is equivalent to only optimizing the precision side of the caption set generated by the model and therefore overlooks the recall side. Empirical result shows that the model trained by this objective tends to get lower score on the recall side. We propose to add a sequence-level exploration term to the current objective to boost recall. It guides the model to explore more plausible captions in the training. In this way, the proposed objective takes both the precision and recall sides of generated captions into account. Experiments show the effectiveness of the proposed method on both video and image captioning datasets.
Single-, low-dose cyclophosphamide-associated severe hyponatremia with seizures in a patient with breast cancer

C yclophosphamide, an agent used to treat various malignant and autoimmune disorders, can cause severe hyponatremia with seizures in rare cases. The exact mechanism of cyclophosphamide-induced hyponatremia is poorly understood, but is thought to occur from a drugassociated antidiuretic hormone (ADH) release leading to free water retention. This unusual phenomenon of cyclophosphamide-associated syndrome of inappropriate antidiuretic hormone secretion (SIADH) has been described only in case reports, most of which reported the development of severe hyponatremia within a week after administration of cyclophosphamide. We report a unique case of a patient who developed severe, symptomatic hyponatremia with seizures, with her serum sodium decreasing from 137 mEq to 112 mEq within 30 hours after her first dose of low-dose cyclophosphamide (600 mg/m).
Quantum-Enhanced Velocimetry with Doppler-Broadened Atomic Vapor.

Traditionally, measuring the center-of-mass (c.m.) velocity of an atomic ensemble relies on measuring the Doppler shift of the absorption spectrum of single atoms in the ensemble. Mapping out the velocity distribution of the ensemble is indispensable when determining the c.m. velocity using this technique. As a result, highly sensitive measurements require preparation of an ensemble with a narrow Doppler width. Here, we use a dispersive measurement of light passing through a moving room temperature atomic vapor cell to determine the velocity of the cell in a single shot with a short-term sensitivity of 5.5 μm s^{-1} Hz^{-1/2}. The dispersion of the medium is enhanced by creating quantum interference through an auxiliary transition for the probe light under electromagnetically induced transparency condition. In contrast to measurement of single atoms, this method is based on the collective motion of atoms and can sense the c.m. velocity of an ensemble without knowing its velocity distribution. Our results improve the previous measurements by 3 orders of magnitude and can be used to design a compact motional sensor based on thermal atoms.
Trajectory Grouping with Curvature Regularization for Tubular Structure Tracking

Tubular structure tracking is a crucial task in the fields of computer vision and medical image analysis. The minimal paths-based approaches have exhibited their strong ability in tracing tubular structures, by which a tubular structure can be naturally modeled as a minimal geodesic path computed with a suitable geodesic metric. However, existing minimal paths-based tracing approaches still suffer from difficulties such as the shortcuts and short branches combination problems, especially when dealing with the images involving complicated tubular tree structures or background. In this paper, we introduce a new minimal paths-based model for minimally interactive tubular structure centerline extraction in conjunction with a perceptual grouping scheme. Basically, we take into account the prescribed tubular trajectories and curvature-penalized geodesic paths to seek suitable shortest paths. The proposed approach can benefit from the local smoothness prior on tubular structures and the global optimality of the used graph-based path searching scheme. Experimental results on both synthetic and real images prove that the proposed model indeed obtains outperformance comparing with the state-of-the-art minimal paths-based tubular structure tracing algorithms.