Miracle-dz/UniFAR-FTUs huggingface dataset:
Lipschitz regularity of deep neural networks: analysis and efficient estimation

Deep neural networks are notorious for being sensitive to small well-chosen perturbations, and estimating the regularity of such architectures is of utmost importance for safe and robust practical applications. In this paper, we investigate one of the key characteristics to assess the regularity of such methods: the Lipschitz constant of deep learning architectures. First, we show that, even for two layer neural networks, the exact computation of this quantity is NP-hard and state-of-art methods may significantly overestimate it. Then, we both extend and improve previous estimation methods by providing AutoLip, the first generic algorithm for upper bounding the Lipschitz constant of any automatically differentiable function. We provide a power method algorithm working with automatic differentiation, allowing efficient computations even on large convolutions. Second, for sequential neural networks, we propose an improved algorithm named SeqLip that takes advantage of the linear computation graph to split the computation per pair of consecutive layers. Third we propose heuristics on SeqLip in order to tackle very large networks. Our experiments show that SeqLip can significantly improve on the existing upper bounds. Finally, we provide an implementation of AutoLip in the PyTorch environment that may be used to better estimate the robustness of a given neural network to small perturbations or regularize it using more precise Lipschitz estimations.
Discourse Representation Structure Parsing

We introduce an open-domain neural semantic parser which generates formal meaning representations in the style of Discourse Representation Theory (DRT; Kamp and Reyle 1993). We propose a method which transforms Discourse Representation Structures (DRSs) to trees and develop a structure-aware model which decomposes the decoding process into three stages: basic DRS structure prediction, condition prediction (i.e., predicates and relations), and referent prediction (i.e., variables). Experimental results on the Groningen Meaning Bank (GMB) show that our model outperforms competitive baselines by a wide margin.
Intestinal HCO − 3 secretion in marine teleost fish : evidence for an apical rather than a basolateral Cl − / HCO − 3 exchanger

Intestinal fluid was collected from 11 marine teleost fish from the Baltic sea and the Pacific ocean. The anterior, mid and posterior segments of the intestine contained 33–110 mM of HCO3 equivalents (with exception of the Atlantic cod which contained only 5–15 mM). Considering literature values of transepithelial potentials and concentration gradients, these high levels of HCO3 equivalents are probably the result of active HCO − 3 transport. Possible HCO − 3 transport mechanisms were studied in the Pacific sanddab (Citharichthys sordidus) in vitro. Measurements of net secretion of HCO3 equivalents across the intestinal epithelium revealed mucosal DIDS sensitivity (10−4 M) and Cl−-dependence of the HCO3 equivalent net flux, but no serosal DIDS (10−4 M) sensitivity. Net Na+ uptake was abolished in the absence of Cl−, but some Cl− uptake persisted in the absence of Na+, at a rate similar to that of net HCO3 secretion. Anterior, mid and posterior segments of the intestine performed similarly. These observations support the presence of an apical rather than a basolateral Cl−/HCO3 exchanger and thus contrast the currently accepted model for intestinal HCO3 secretion. This apical Cl−/HCO − 3 exchanger alone, however, is not sufficient for maintaining the observed HCO3 equivalents gradient in vivo. We suggest a coupling of cytosolic carbonic anhydrase, a basolateral proton pump and the apical Cl−/HCO3 exchanger to explain the intestinal HCO − 3 transport.
Neural Syntactic Generative Models with Exact Marginalization

We present neural syntactic generative models with exact marginalization that support both dependency parsing and language modeling. Exact marginalization is made tractable through dynamic programming over shift-reduce parsing and minimal RNN-based feature sets. Our algorithms complement previous approaches by supporting batched training and enabling online computation of next word probabilities. For supervised dependency parsing, our model achieves a state-of-the-art result among generative approaches. We also report empirical results on unsupervised syntactic models and their role in language modeling. We find that our model formulation of latent dependencies with exact marginalization do not lead to better intrinsic language modeling performance than vanilla RNNs, and that parsing accuracy is not correlated with language modeling perplexity in stack-based models.
Nursing Home Infection Control Program Characteristics, CMS Citations, and Implementation of Antibiotic Stewardship Policies: A National Study

Recently, the Centers for Medicare & Medicaid Services (CMS) final rule required that nursing homes (NHs) develop an infection control program that includes an antibiotic stewardship component and employs a trained infection preventionist (IP). The objectives of this study were to provide a baseline assessment of (1) NH facility and infection control program characteristics associated with having an infection control deficiency citation and (2) associations between IP training and the presence of antibiotic stewardship policies, controlling for NH characteristics. A cross-sectional survey of 2514 randomly sampled US NHs was conducted to assess IP training, staff turnover, and infection control program characteristics (ie, frequency of infection control committee meetings and the presence of 7 antibiotic stewardship policies). Responses were linked to concurrent Certification and Survey Provider Enhanced Reporting data, which contain information about NH facility characteristics and citations. Descriptive statistics and multivariable regression analyses were conducted to account for NH characteristics. Surveys were received from 990 NHs; 922 had complete data. One-third of NHs in this sample received an infection control deficiency citation. The NHs that received deficiency citations were more likely to have committees that met weekly/monthly versus quarterly (P < .01). The IPs in 39% of facilities had received specialized training. Less than 3% of trained IPs were certified in infection control. The NHs with trained IPs were more likely to have 5 of the 7 components of antibiotic stewardship in place (all P < .05). The IP training, although infrequent, was associated with the presence of antibiotic stewardship policies. Receiving an infection control citation was associated with more frequent infection control committee meetings. Training and support of IPs is needed to ensure infection control and antibiotic stewardship in NHs. As the CMS rule becomes implemented, more research is warranted. There is a need for increase in trained IPs in US NHs. These data can be used to evaluate the effectiveness of the CMS final rule on infection management processes in US NHs.
Brucellosis in Adults and Children: A 10-Year Case Series at Two Large Academic Hospitals in Houston, Texas

Objectives Brucellosis is one of the most common zoonoses worldwide. Most cases in the United States occur among travelers or immigrants from endemic regions, mostly Central America. In this study, we aimed at describing and comparing the epidemiology and clinical presentation of brucellosis in pediatric and adult patients at two large tertiary care centers in Houston, Texas. Methods We identified patients diagnosed as having brucellosis between January 2000 and December 2009 by searching electronic medical records and reviewing microbiology records for positive cultures. Cases were defined as those with a positive blood culture for Brucella sp, a serum agglutination titer ≥1:80 (or both positive blood culture and serum agglutination titer ≥1:80), along with an epidemiologic risk factor and clinical presentation that is consistent with brucellosis. Results Six adult and 12 pediatric cases were identified; 13 of 18 (72%) cases were immigrants, mostly from Central America. The median ages for adult and pediatric patients were 53 and 3 years old, respectively. Ingestion of unpasteurized milk products was frequently reported. Common clinical features included fever (83%), arthralgias or arthritis (67%), and hepatosplenomegaly (61%). Positive blood cultures were more frequently reported among children than adults (83% vs 33%, P = 0.03). The most common laboratory finding was mildly elevated transaminases. Three adults (50%) but no children developed thrombocytopenia (P = 0.02). Relapsed infection was a frequent occurrence. Conclusions In the southern United States, brucellosis is an important consideration in the differential diagnosis of immigrants presenting with undifferentiated fever and joint complaints. A careful history often reveals an epidemiologic risk factor such as ingestion of unpasteurized dairy products.
The model order limit: Deep sparse factorization for resting brain

The decomposition of resting state Functional Magnetic Resonance Imaging (rs-fMRI) data by Dictionary Learning (DL) using sparsity constraint have been recently shown to be an alternative to traditional consideration of independence criteria to obtain Resting State Networks. However, a single level decomposition is not suitable when applied for group rs-fMRI analysis, as it requires a large model order or an equivalently large number of dictionary atoms to appropriately identify distinct brain networks. This is computationally expensive for group rs-fMRI analysis in resource constraint environments. In this work, we have proposed a deep sparse factorization method for multi-level decomposition of rs-fMRI data. Preliminary results from this study show that a multi-layer framework with very small model order can reveal better spatial maps of resting brain that share equivalent temporal dynamics. In addition, the proposed approach outperforms the existing DL approaches with single level decomposition in the metric of quality as well as computational complexity.
Common Wheat Chromosome 5B Composition Analysis Using Low‐Coverage 454 Sequencing

The sequencing of individual chromosomes of common wheat is in progress. The molecular size of wheat chromosome 5B is nearly 870 Mb (5BL = 580 Mb and 5BS = 290 Mb). We produced the first low coverage 454‐sequencing of the long and short arms of wheat chromosome 5B (110,793 and 39,695 reads, which compose 8 and 6% of total 5BL and 5BS length, respectively) and calculated the ratios of the different families of repetitive sequences, including transposable elements (TEs), satellite repeats (Afa, pSc119.2, 5S rDNA and 45S rDNA), and microsatellites, as well as direct and inverted repeat motifs. The TEs accounted for 70% of the total analyzed nucleotide sequences. The content of the Cereba retrotransposon family differed between the two arms of chromosome 5B. Comparing the reads of chromosome 5B with the data from chromosome 5A, we found the retrotransposons Fatima and Sakura and DNA transposon Jorge were prevalent in 5B. The hypothetical coding sequences accounted for 2.0% of the short arm and 2.07% of the long arm. Using in silico mapping, we identified the regions of synteny with rice and Brachypodium chromosomes (1,073,526 and 1,767,298 bp aligned, respectively), and the result was consistent with the data from the expressed sequence tag (EST) mapping of wheat 5B chromosome to the genomes of these grasses. Thus, these results show that low coverage survey sequencing can provide useful information about the composition and evolution of wheat chromosome 5B.
The evaluation of American compound option prices under stochastic volatility and stochastic interest rates

A compound option (the mother option) gives the holder the right, but not the obligation, to buy (long) or sell (short) the underlying option (the daughter option). In this paper, we consider the problem of pricing American-type compound options when the underlying dynamics follow Heston’s stochastic volatility and with stochastic interest rate driven by Cox–Ingersoll–Ross processes. We use a partial differential equation (PDE) approach to obtain a numerical solution. The problem is formulated as the solution to a two-pass free-boundary PDE problem, which is solved via a sparse grid approach and is found to be accurate and efficient compared with the results from a benchmark solution based on a least-squares Monte Carlo simulation combined with the projected successive over-relaxation method.
Gene family innovation, conservation and loss on the animal stem lineage

Choanoflagellates, the closest living relatives of animals, can provide unique insights into the changes in gene content that preceded the origin of animals. However, only two choanoflagellate genomes are currently available, providing poor coverage of their diversity. We sequenced transcriptomes of 19 additional choanoflagellate species to produce a comprehensive reconstruction of the gains and losses that shaped the ancestral animal gene repertoire. We identified ~1944 gene families that originated on the animal stem lineage, of which only 39 are conserved across all animals in our study. In addition, ~372 gene families previously thought to be animal-specific, including Notch, Delta, and homologs of the animal Toll-like receptor genes, instead evolved prior to the animal-choanoflagellate divergence. Our findings contribute to an increasingly detailed portrait of the gene families that defined the biology of the Urmetazoan and that may underpin core features of extant animals.
The Effect of a Hydrolyzed Polysaccharide Dietary Supplement on Biomarkers in Adults with Nonalcoholic Fatty Liver Disease

The primary objective of the study was to evaluate the effect of a hydrolyzed polysaccharide, Rice Bran Arabinoxylan Compound (RBAC), on biomarkers in adults with nonalcoholic fatty liver disease (NAFLD). A 90-day randomized double-blind placebo-controlled trial examined the effect of RBAC on complete blood count, liver enzymes, lipids, oxidative stress markers, cytokines, and growth factors. Twenty-three adults with NAFLD were enrolled and randomly assigned to one of the two study conditions (n = 12 RBAC and n = 11 placebo) and consumed 1 gram/day of either compound for 90 days. Subjects were assessed at baseline and 45 and 90 days. No adverse effects were reported. Alkaline phosphatase significantly decreased (−3.1%; SD = 19.9; F[1,19] = 5.1, p = 0.03) in the RBAC group compared to placebo. Percent monocytes (17.9%; SD = 18.3; F[1,19] = 5.9, p = 0.02) and percent eosinophils (30.6%; SD = 30.5; F[1,19] = 12.3, p < 0.01) increased in the RBAC group. IFN-γ (156%; SD = 131.8; F[1,19] = 4.2, p = 0.06) and IL-18 (29.1%; SD = 64; F[1,19] = 5.3, p = 0.03) increased in the RBAC group compared to placebo. Other improvements were noted for platelets, neutrophils, neutrophil-lymphocyte ratio, γ-glutamyl transferase, and 4-hydroxynonenal. RBAC had beneficial effects on several biomarkers that add to the known immunomodulatory activities of RBAC, which may be promising for people with NAFLD.
Endoscopic ultrasound‐through‐the‐needle biopsy in pancreatic cystic lesions: A multicenter study

Tissue acquisition in pancreatic cystic lesions (PCL) is the ideal method for diagnosis and risk stratification for malignancy of these lesions. Direct sampling from the walls of PCL with different devices has shown better results than cytology from cystic fluid. We carried out a retrospective, multicenter study to evaluate the feasibility, safety, and diagnostic yield of a micro‐forceps, specifically designed to be used through a 19‐gauge needle after endoscopic ultrasonography (EUS)‐guided puncture of PCL.
Autonomous Thermalling as a Partially Observable Markov Decision Process (Extended Version)

Small uninhabited aerial vehicles (sUAVs) commonly rely on active propulsion to stay airborne, which limits flight time and range. To address this, autonomous soaring seeks to utilize free atmospheric energy in the form of updrafts (thermals). However, their irregular nature at low altitudes makes them hard to exploit for existing methods. We model autonomous thermalling as a POMDP and present a receding-horizon controller based on it. We implement it as part of ArduPlane, a popular open-source autopilot, and compare it to an existing alternative in a series of live flight tests involving two sUAVs thermalling simultaneously, with our POMDP-based controller showing a significant advantage.
The influence of temperature and photoperiod on the timing of brood onset in hibernating honey bee colonies

In order to save resources, honey bee (Apis mellifera) colonies in the temperate zones stop brood rearing during winter. Brood rearing is resumed in late winter to build up a sufficient worker force that allows to exploit floral resources in upcoming spring. The timing of brood onset in hibernating colonies is crucial and a premature brood onset could lead to an early depletion of energy reservoirs. However, the mechanisms underlying the timing of brood onset and potential risks of mistiming in the course of ongoing climate change are not well understood. To assess the relative importance of ambient temperature and photoperiod as potential regulating factors for brood rearing activity in hibernating colonies, we overwintered 24 honey bee colonies within environmental chambers. The colonies were assigned to two different temperature treatments and three different photoperiod treatments to disentangle the individual and interacting effects of temperature and photoperiod. Tracking in-hive temperature as indicator for brood rearing activity revealed that increasing ambient temperature triggered brood onset. Under cold conditions, photoperiod alone did not affect brood onset, but the light regime altered the impact of higher ambient temperature on brood rearing activity. Further the number of brood rearing colonies increased with elapsed time which suggests the involvement of an internal clock. We conclude that timing of brood onset in late winter is mainly driven by temperature but modulated by photoperiod. Climate warming might change the interplay of these factors and result in mismatches of brood phenology and environmental conditions.
Impulsivity and decision-making in obsessive-compulsive disorder after effective deep brain stimulation or treatment as usual

Objective Impulsivity and impaired decision-making have been proposed as obsessive-compulsive disorder (OCD) endophenotypes, running in OCD and their healthy relatives independently of symptom severity and medication status. Deep brain stimulation (DBS) targeting the ventral limb of the internal capsule (vALIC) and the nucleus accumbens (Nacc) is an effective treatment strategy for treatment-refractory OCD. The effectiveness of vALIC-DBS for OCD has been linked to its effects on a frontostriatal network that is also implicated in reward, impulse control, and decision-making. While vALIC-DBS has been shown to restore reward dysfunction in OCD patients, little is known about the effects of vALIC-DBS on impulsivity and decision-making. The aim of the study was to compare cognitive impulsivity and decision-making between OCD patients undergoing effective vALIC-DBS or treatment as usual (TAU), and healthy controls. Methods We used decision-making performances under ambiguity on the Iowa Gambling Task and reflection impulsivity on the Beads Task to compare 20 OCD patients effectively treated with vALIC-DBS, 40 matched OCD patients undergoing effective TAU (medication and/or cognitive behavioural therapy), and 40 healthy subjects. Effective treatment was defined as at least 35% improvement of OCD symptoms. Results OCD patients, irrespective of treatment modality (DBS or TAU), showed increased reflection impulsivity and impaired decision-making compared to healthy controls. No differences were observed between OCD patients treated with DBS or TAU. Conclusion OCD patients effectively treated with vALIC-DBS or TAU display increased reflection impulsivity and impaired decision-making independent of the type of treatment.
Fully automated organ segmentation in male pelvic CT images

Accurate segmentation of prostate and surrounding organs at risk is important for prostate cancer radiotherapy treatment planning. We present a fully automated workflow for male pelvic CT image segmentation using deep learning. The architecture consists of a 2D organ volume localization network followed by a 3D segmentation network for volumetric segmentation of prostate, bladder, rectum, and femoral heads. We used a multi-channel 2D U-Net followed by a 3D U-Net with encoding arm modified with aggregated residual networks, known as ResNeXt. The models were trained and tested on a pelvic CT image dataset comprising 136 patients. Test results show that 3D U-Net based segmentation achieves mean (±SD) Dice coefficient values of 90 (±2.0)%, 96 (±3.0)%, 95 (±1.3)%, 95 (±1.5)%, and 84 (±3.7)% for prostate, left femoral head, right femoral head, bladder, and rectum, respectively, using the proposed fully automated segmentation method.
Conjugacy Separation Problem in Braids: an Attack on the Original Colored Burau Key Agreement Protocol

In this paper, we consider the conjugacy separation search problem in braid groups. We deeply redesign the algorithm presented in [18] and provide an experimental evidence that the problem can be solved for 100% of very long randomly generated instances. The lengths of tested randomly generated instances is increased by the factor of two compared to the lengths suggested in the original proposal for 120 bits of security. An implementation of our attack is freely available in CRAG, [6]. In particular, the implementation contains all challenging instances we had to deal with on a way to 100% success. We hope it will be useful to braid-group cryptography community.
The Impact of Q-Matrix Designs on Diagnostic Classification Accuracy in the Presence of Attribute Hierarchies

There is an increasing demand for assessments that can provide more fine-grained information about examinees. In response to the demand, diagnostic measurement provides students with feedback on their strengths and weaknesses on specific skills by classifying them into mastery or nonmastery attribute categories. These attributes often form a hierarchical structure because student learning and development is a sequential process where many skills build on others. However, it remains to be seen if we can use information from the attribute structure and work that into the design of the diagnostic tests. The purpose of this study is to introduce three approaches of Q-matrix design and investigate their impact on classification results under different attribute structures. Results indicate that the adjacent approach provides higher accuracy in a shorter test length when compared with other Q-matrix design approaches. This study provides researchers and practitioners guidance on how to design the Q-matrix in diagnostic tests, which are in high demand from educators.
Object Counting with Small Datasets of Large Images

We explore the problem of training one-look regression models for counting objects in datasets comprising a small number of high-resolution, variable-shaped images. To reduce overfitting when training on full resolution samples, we propose to use global sum pooling (GSP) instead of global average pooling (GAP) or fully connected (FC) layers at the backend of a convolutional neural network. Although computationally equivalent to GAP, we show via detailed experimentation that GSP allows convolutional networks to learn the counting task as a simple linear mapping problem generalized over the input shape and the number of objects present. We evaluate our approach on four different aerial image datasets - three car counting datasets (CARPK, PUCPR+, and COWC) and one new challenging dataset for wheat spike counting. Our GSP approach achieves state-of-the-art performance on all four datasets and GSP models trained with smaller-sized image patches localize objects better than their GAP counterparts.