Miracle-dz/UniFAR-FTUs huggingface dataset:
Septic arthritis of the postpartum pubic symphysis associated with ovarian vein thrombosis

Septic arthritis of the symphysis pubis is a rare infectious localization, often occurring in patients with a particular terrain (urological and pelvic surgery, sports). Pubic symphysitis associated with pregnancy is exceptional. The symptoms can mimic a myriad of pathologies masking the true diagnosis. X-ray signs may be delayed or undetected in some radiological investigations. As a result, the diagnosis may be missed and treatment delayed. We describe a case of septic arthritis of the pubic symphysis discovered postpartum associated with thrombosis of the right ovarian vein, presented to our service and which highlights the diagnostic difficulties and recalls the importance of maintaining a high level of suspicion based on clinical symptoms and key points for diagnosis in order to ensure prompt treatment and avoid serious complications.
Adventure Based Counseling and Invitational Theory Adventure Based Counseling and Invitational Theory

Invitational theory offers a framework within which counselors (as well as others) may improve and enrich the physical and psychological environments of their institutions while encouraging the development of the people who live and work there. Adventure based counseling provides a similar framework, focusing on many of the same core tenets of invitational theory. This paper addresses the combination of invitational theory and adventure based counseling, and provides examples of activities that may be implemented in the process.
On the Neural Tangent Kernel of Deep Networks with Orthogonal Initialization

The prevailing thinking is that orthogonal weights are crucial to enforcing dynamical isometry and speeding up training. The increase in learning speed that results from orthogonal initialization in linear networks has been well-proven. However, while the same is believed to also hold for nonlinear networks when the dynamical isometry condition is satisfied, the training dynamics behind this contention have not been thoroughly explored. In this work, we study the dynamics of ultra-wide networks across a range of architectures, including Fully Connected Networks (FCNs) and Convolutional Neural Networks (CNNs) with orthogonal initialization via neural tangent kernel (NTK). Through a series of propositions and lemmas, we prove that two NTKs, one corresponding to Gaussian weights and one to orthogonal weights, are equal when the network width is infinite. Further, during training, the NTK of an orthogonally-initialized infinite-width network should theoretically remain constant. This suggests that the orthogonal initialization cannot speed up training in the NTK (lazy training) regime, contrary to the prevailing thoughts. In order to explore under what circumstances can orthogonality accelerate training, we conduct a thorough empirical investigation outside the NTK regime. We find that when the hyper-parameters are set to achieve a linear regime in nonlinear activation, orthogonal initialization can improve the learning speed with a large learning rate or large depth.
A Systematic Study of Galactic Outflows via Fluorescence Emission: Implications for Their Size and Structure

Galactic outflows play a major role in the evolution of galaxies, but the underlying physical processes are poorly understood. This is mainly because we have little information about the outflow structure, especially on large scales. In this paper, we probe the structure of galactic outflows in low-z starbursts using a combination of ultraviolet spectroscopy and imaging of the fluorescence emission lines (associated with transitions to excited fine-structure levels) and spectroscopy of the corresponding strongly blueshifted resonance absorption lines. We find that, in the majority of cases, the observed fluorescence emission lines are much weaker and narrower than the absorption lines, originating in the star-forming interstellar medium and/or the slowest-moving part of the inner outflow. In a minority of cases, the outflowing absorbing material does make a significant contribution to the fluorescence emission. These latter systems are characterized by both strong Lyα emission lines and weak low-ionization absorption lines (both known to be empirical signs of Lyman-continuum leakage). We argue that the observed weakness of emission from the outflow seen in the majority of cases is due to the missing emission arising on scales larger than those encompassed by the aperture of the Cosmic Origins Spectrograph on board the Hubble Space Telescope. This implies shallow radial density profiles in these outflows, and suggests that most of the observed absorbing material must be created/injected at radii much larger than that of the starburst. This has important implications both for our understanding of the physics of galactic outflows and for our estimation of their principal properties.
Exploring Erasure Coding Techniques for High Availability of Intermediate Data

Scientific computing workflows generate enormous distributed data that is short-lived, yet critical for job completion time. This class of data is called intermediate data. A common way to achieve high data availability is to replicate data. However, an increasing scale of intermediate data generated in modern scientific applications demands new storage techniques to improve storage efficiency. Erasure Codes, as an alternative, can use less storage space while maintaining similar data availability. In this paper, we adopt erasure codes for storing intermediate data and compare its performance with replication. We also use the metric of Mean-Time-To-Data-Loss (MTTDL) to estimate the lifetime of intermediate data. We propose an algorithm to proactively relocate data redundancy from vulnerable machines to reliable ones to improve data availability with some extra network overhead. Furthermore, we propose an algorithm to assign redundancy units of data physically close to each other on the network to reduce the network bandwidth for reconstructing data when it is being accessed.
Closing Gaps in Asymptotic Fair Division

We study a resource allocation setting where $m$ discrete items are to be divided among $n$ agents with additive utilities, and the agents' utilities for individual items are drawn at random from a probability distribution. Since common fairness notions like envy-freeness and proportionality cannot always be satisfied in this setting, an important question is when allocations satisfying these notions exist. In this paper, we close several gaps in the line of work on asymptotic fair division. First, we prove that the classical round-robin algorithm is likely to produce an envy-free allocation provided that $m=\Omega(n\log n/\log\log n)$, matching the lower bound from prior work. We then show that a proportional allocation exists with high probability as long as $m\geq n$, while an allocation satisfying envy-freeness up to any item (EFX) is likely to be present for any relation between $m$ and $n$. Finally, we consider a related setting where each agent is assigned exactly one item and the remaining items are left unassigned, and show that the transition from non-existence to existence with respect to envy-free assignments occurs at $m=en$.
Graphsurge: Graph Analytics on View Collections Using Differential Computation

This paper presents the design and implementation of a new open-source view-based graph analytics system called Graphsurge. Graphsurge is designed to support applications that analyze multiple snapshots or views of a large-scale graph. Users program Graphsurge through a declarative graph view definition language (GVDL) to create views over input graphs and a Differential Dataflow-based programming API to write analytics computations. A key feature of GVDL is the ability to organize views into view collections, which allows Graphsurge to automatically share computation across views, without users writing any incrementalization code, by performing computations differentially. We then introduce two optimization problems that naturally arise in our setting. First is the collection ordering problem to determine the order of views that leads to minimum differences across consecutive views. We prove this problem is NP-hard and show a constant-factor approximation algorithm drawn from literature. Second is the collection splitting problem to decide on which views to run computations differentially vs from scratch, for which we present an adaptive solution that makes decisions at runtime. We present extensive experiments to demonstrate the benefits of running computations differentially for view collections and our collection ordering and splitting optimizations.
Hybrid Attention Networks for Flow and Pressure Forecasting in Water Distribution Systems

Multivariate geo-sensory time series prediction is challenging because of the complex spatial and temporal correlations. In urban water distribution systems (WDSs), numerous spatial-correlated sensors have been deployed to continuously collect hydraulic data. Forecasts of the monitored flow and pressure time series are of vital importance for operational decision making, alerts, and anomaly detection. To address this issue, we proposed a hybrid dual-stage spatial–temporal attention-based recurrent neural networks (hDS-RNN). Our model consists of two stages: a spatial attention-based encoder and a temporal attention-based decoder. Specifically, a hybrid spatial attention mechanism that employs inputs along the temporal and spatial axes is proposed. Experiments on a real-world data set are conducted, which demonstrate that our model outperformed seven baseline models in flow and pressure predictions in WDS.
Comparison Between the Joint and Successive Decoding Schemes for the Binary CEO Problem

A comparison between the joint and the successive decoding schemes for a two-link case binary Chief Executive Officer (CEO) problem is presented. We utilize the logarithmic loss as the criterion for evaluating the total distortion of the decoding techniques. The binary symmetric channel (BSC) is considered as the test-channel model, which provides a theoretical rate-distortion bound. Simulation results show that the joint decoding scheme provides smaller compression rate and distortion values compared to the successive decoding scheme. On the other hand the successive decoding method has lower complexity; therefore, it is efficient for the cases with higher number of links.
Picture naming in the context of semantically related distractors: A Bayesian meta-analysis 40 years after Lupker 1979s first reaction time study

When participants in an experiment have to name pictures while ignoring distractor words superimposed on the picture or presented auditorily (i.e., picture-word interference paradigm), they take more time when the word to be named (or target) and distractor words are from the same semantic category (e.g., cat-dog). This experimental effect is known as the semantic interference effect, and is probably one of the most studied in the language production literature. The functional origin of the effect and the exact conditions in which it occurs are however still debated. Since Lupker reported the effect in the first response time experiment about 40 years ago, more than 300 similar experiments have been conducted. The semantic interference effect was replicated in many experiments, but several studies also reported the absence of an effect in a subset of experimental conditions. The aim of the present study is to provide a comprehensive theoretical review of the existing evidence to date and several Bayesian meta-analyses and meta-regressions to determine the size of the effect and explore the experimental conditions in which the effect surfaces. The results are discussed in the light of current debates about the functional origin of the semantic interference effect and its implications for our understanding of the language production system.
Filtering the Rejection Set While Preserving False Discovery Rate Control

Abstract Scientific hypotheses in a variety of applications have domain-specific structures, such as the tree structure of the international classification of diseases (ICD), the directed acyclic graph structure of the gene ontology (GO), or the spatial structure in genome-wide association studies. In the context of multiple testing, the resulting relationships among hypotheses can create redundancies among rejections that hinder interpretability. This leads to the practice of filtering rejection sets obtained from multiple testing procedures, which may in turn invalidate their inferential guarantees. We propose Focused BH, a simple, flexible, and principled methodology to adjust for the application of any prespecified filter. We prove that Focused BH controls the false discovery rate under various conditions, including when the filter satisfies an intuitive monotonicity property and the p-values are positively dependent. We demonstrate in simulations that Focused BH performs well across a variety of settings, and illustrate this method’s practical utility via analyses of real datasets based on ICD and GO.
Frequency-Guided Word Substitutions for Detecting Textual Adversarial Examples

Recent efforts have shown that neural text processing models are vulnerable to adversarial examples, but the nature of these examples is poorly understood. In this work, we show that adversarial attacks against CNN, LSTM and Transformer-based classification models perform word substitutions that are identifiable through frequency differences between replaced words and their corresponding substitutions. Based on these findings, we propose frequency-guided word substitutions (FGWS), a simple algorithm exploiting the frequency properties of adversarial word substitutions for the detection of adversarial examples. FGWS achieves strong performance by accurately detecting adversarial examples on the SST-2 and IMDb sentiment datasets, with F1 detection scores of up to 91.4% against RoBERTa-based classification models. We compare our approach against a recently proposed perturbation discrimination framework and show that we outperform it by up to 13.0% F1.
Exploring Effects of Random Walk Based Minibatch Selection Policy on Knowledge Graph Completion

In this paper, we have explored the effects of different minibatch sampling techniques in Knowledge Graph Completion. Knowledge Graph Completion (KGC) or Link Prediction is the task of predicting missing facts in a knowledge graph. KGC models are usually trained using margin, soft-margin or cross-entropy loss function that promotes assigning a higher score or probability for true fact triplets. Minibatch gradient descent is used to optimize these loss functions for training the KGC models. But, as each minibatch consists of only a few randomly sampled triplets from a large knowledge graph, any entity that occurs in a minibatch, occurs only once in most cases. Because of this, these loss functions ignore all other neighbors of any entity, whose embedding is being updated at some minibatch step. In this paper, we propose a new random-walk based minibatch sampling technique for training KGC models that optimizes the loss incurred by a minibatch of closely connected subgraph of triplets instead of randomly selected ones. We have shown results of experiments for different models and datasets with our sampling technique and found that the proposed sampling algorithm has varying effects on these datasets/models. Specifically, we find that our proposed method achieves state-of-the-art performance on the DB100K dataset.
A Unified DNN Weight Compression Framework Using Reweighted Optimization Methods

To address the large model size and intensive computation requirement of deep neural networks (DNNs), weight pruning techniques have been proposed and generally fall into two categories, i.e., static regularization-based pruning and dynamic regularization-based pruning. However, the former method currently suffers either complex workloads or accuracy degradation, while the latter one takes a long time to tune the parameters to achieve the desired pruning rate without accuracy loss. In this paper, we propose a unified DNN weight pruning framework with dynamically updated regularization terms bounded by the designated constraint, which can generate both non-structured sparsity and different kinds of structured sparsity. We also extend our method to an integrated framework for the combination of different DNN compression tasks.
Blind Adversarial Pruning: Towards The Comprehensive Robust Models With Gradually Pruning Against Blind Adversarial Attacks

With the growth of interest in the attack and defense of deep neural networks, researchers have increasingly focused on the robustness of their application to devices with limited memory, in order to deal with unknown-budget (blind) adversarial attacks under different compression ratios. We analyze the existing pruning methods and find that the robustness of the pruned models varies drastically with different pruning processes, and the robustness of the pruned model with adversarial training exhibits a high sensitivity to the budget of the adversarial examples. These methods cannot obtain models that are comprehensively robust when confronting blind adversarial attacks with different compression ratios. To address this problem, we propose an approach called blind adversarial pruning (BAP) that introduces the approach of blind adversarial training into the gradual pruning process, to ultimately obtain pruned models with comprehensive robustness under different compression ratios. The experimental results obtained using BAP for pruning classification models based on several benchmarks demonstrate the competitive performance of this method; the robustness of BAP models is more stable compared to various pruning processes, and BAP exhibits better comprehensive robustness against blind adversarial attacks.
The Parallax of VHS J1256–1257 from CFHT and Pan-STARRS-1

We present new parallax measurements from the CFHT Infrared Parallax Program and the Pan-STARRS 3$\pi$ Steradian Survey for the young ($\approx150-300$ Myr) triple system VHS J125601.92$-$125723.9. This system is composed of a nearly equal-flux binary ("AB") and a wide, possibly planetary-mass companion ("b"). The system's published parallactic distance ($12.7\pm1.0$ pc) implies absolute magnitudes unusually faint compared to known young objects and is in tension with the spectrophotometric distance for the central binary ($17.2\pm2.6$ pc). Our CFHT and Pan-STARRS parallaxes are consistent, and the more precise CFHT result places VHS J1256-1257 at $22.2^{+1.1}_{-1.2}$ pc. Our new distance results in higher values for the companion's mass ($19\pm5$ M$_{\rm Jup}$) and temperature ($1240\pm50$ K), and also brings the absolute magnitudes of all three components into better agreement with known young objects.
A general velocity correction scheme for two-way coupled point-particle simulations

The accuracy of Euler-Lagrange point-particle models employed in particle-laden fluid flow simulations depends on accurate estimation of the particle force through closure models. Typical force closure models require computation of the slip velocity at the particle location, which in turn requires accurate estimation of the undisturbed fluid velocity. However, when the fluid and particle phases are two-way coupled the fluid velocity field is disturbed by the presence of the particle. A common practice is to use the disturbed velocity to compute the particle force which can result in errors as much as 100% in predicting the particle dynamics. In this work, a general velocity correction scheme is developed that facilitates accurate estimation of the undisturbed fluid velocity in particle-laden fluid flows with and without no-slip walls. The model can handle particles of different size, arbitrary interpolation functions, anisotropic grids with large aspect ratios, and wall-bounded flows. The present correction scheme is motivated by the recent work of Esmaily & Horwitz (JCP, 2018) on unbounded particle-laden flows. Modifications necessary for wall-bounded flows are developed such that the undisturbed fluid velocity at any wall distance is accurately recovered, asymptotically approaching the unbounded scheme for particles far away from walls. A detailed series of verification tests were conducted on settling velocity of a particle in parallel and perpendicular motions to a no-slip wall. A range of flow parameters and grid configurations; involving anisotropic grids with aspect ratios typically encountered in particle-laden turbulent channel flows, were considered in detail. When the wall effects are accounted for, the present correction scheme reduces the errors in predicting the near-wall particle motion by one order of magnitude smaller values compared to the unbounded correction schemes.
Birational Rowmotion and Coxeter-Motion on Minuscule Posets

Birational rowmotion is a discrete dynamical system on the set of all positive real-valued functions on a finite poset, which is a birational lift of combinatorial rowmotion on order ideals. It is known that combinatorial rowmotion for a minuscule poset has order equal to the Coxeter number, and exhibits the file homomesy phenomenon for refined order ideal cardinality statistics. In this paper we generalize these results to the birational setting. Moreover, as a generalization of birational promotion on a product of two chains, we introduce birational Coxeter-motion on minuscule posets, and prove that it enjoys periodicity and file homomesy.