Miracle-dz/UniFAR-FTUs huggingface dataset:
Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Models

Overparameterized neural networks generalize well but are expensive to train. Ideally, one would like to reduce their computational cost while retaining their generalization benefits. Sparse model training is a simple and promising approach to achieve this, but there remain challenges as existing methods struggle with accuracy loss, slow training runtime, or difficulty in sparsifying all model components. The core problem is that searching for a sparsity mask over a discrete set of sparse matrices is difficult and expensive. To address this, our main insight is to optimize over a continuous superset of sparse matrices with a fixed structure known as products of butterfly matrices. As butterfly matrices are not hardware efficient, we propose simple variants of butterfly (block and flat) to take advantage of modern hardware. Our method (Pixelated Butterfly) uses a simple fixed sparsity pattern based on flat block butterfly and low-rank matrices to sparsify most network layers (e.g., attention, MLP). We empirically validate that Pixelated Butterfly is 3x faster than butterfly and speeds up training to achieve favorable accuracy--efficiency tradeoffs. On the ImageNet classification and WikiText-103 language modeling tasks, our sparse models train up to 2.5x faster than the dense MLP-Mixer, Vision Transformer, and GPT-2 medium with no drop in accuracy.
Charmless Quasi-two-body $B$ Decays in Perturbative QCD Approach: Taking $B\to K({\cal R}\to) K^+K^-$ As Examples

Three-body B decays not only significantly broaden the study of B meson decay mechanisms, but also provide information of resonant particles. Because of complicate dynamics, it is very hard for us to study the whole phase space in a specific approach. In this review, we take B → K(R → KK) decays [1] as examples and show the application of the perturbative QCD (PQCD) approach in studying the quasi-two-body B decays, where two particles move collinearly with large energy and the bachelor one recoils back. To describe the dynamics of two collinear particles, the (S, P and D)-wave functions of kaon-pair with different waves are introduced. By keeping the transverse momenta, all possible diagrams including the hard spectator diagrams and annihilation ones can be calculated in PQCD approach. Most results are well consistent with the current measurements from BaBar, Belle and LHCb experiments. Moreover, under the narrowwidth approximation we can extract the branching fractions of the two-body decays involving the resonant states, and also predict the branching fractions of the corresponding quasi-two-body decays B → K(R → ππ). All prediction are expected to be tested in the ongoing LHCb and Belle-II experiments.
Deep Neural Networks Learn Meta-Structures from Noisy Labels in Semantic Segmentation

How deep neural networks (DNNs) learn from noisy labels has been studied extensively in image classification but much less in image segmentation. So far, our understanding of the learning behavior of DNNs trained by noisy segmentation labels remains limited. In this study, we address this deficiency in both binary segmentation of biological microscopy images and multi-class segmentation of natural images. We generate extremely noisy labels by randomly sampling a small fraction (e.g., 10%) or flipping a large fraction (e.g., 90%) of the ground truth labels. When trained with these noisy labels, DNNs provide largely the same segmentation performance as trained by the original ground truth. This indicates that DNNs learn structures hidden in labels rather than pixel-level labels per se in their supervised training for semantic segmentation. We refer to these hidden structures in labels as meta-structures. When DNNs are trained by labels with different perturbations to the meta-structure, we find consistent degradation in their segmentation performance. In contrast, incorporation of meta-structure information substantially improves performance of an unsupervised segmentation model developed for binary semantic segmentation. We define meta-structures mathematically as spatial density distributions and show both theoretically and experimentally how this formulation explains key observed learning behavior of DNNs.
The Curse Revisited: When are Distances Informative for the Ground Truth in Noisy High-Dimensional Data?

Distances between data points are widely used in machine learning applications. Yet, when corrupted by noise, these distances -- and thus the models based upon them -- may lose their usefulness in high dimensions. Indeed, the small marginal effects of the noise may then accumulate quickly, shifting empirical closest and furthest neighbors away from the ground truth. In this paper, we exactly characterize such effects in noisy high-dimensional data using an asymptotic probabilistic expression. Previously, it has been argued that neighborhood queries become meaningless and unstable when distance concentration occurs, which means that there is a poor relative discrimination between the furthest and closest neighbors in the data. However, we conclude that this is not necessarily the case when we decompose the data in a ground truth -- which we aim to recover -- and noise component. More specifically, we derive that under particular conditions, empirical neighborhood relations affected by noise are still likely to be truthful even when distance concentration occurs. We also include thorough empirical verification of our results, as well as interesting experiments in which our derived 'phase shift' where neighbors become random or not turns out to be identical to the phase shift where common dimensionality reduction methods perform poorly or well for recovering low-dimensional reconstructions of high-dimensional data with dense noise.
Task-Driven Detection of Distribution Shifts with Statistical Guarantees for Robot Learning

Our goal is to perform out-of-distribution (OOD) detection, i.e., to detect when a robot is operating in environments that are drawn from a different distribution than the environments used to train the robot. We leverage Probably Approximately Correct (PAC)-Bayes theory in order to train a policy with a guaranteed bound on performance on the training distribution. Our key idea for OOD detection then relies on the following intuition: violation of the performance bound on test environments provides evidence that the robot is operating OOD. We formalize this via statistical techniques based on p-values and concentration inequalities. The resulting approach (i) provides guaranteed confidence bounds on OOD detection including bounds on both the false positive and false negative rates of the detector and (ii) is task-driven and sensitive only to changes that impact the robot's performance. We demonstrate our approach in both simulation and hardware for a grasping task using objects with unfamiliar shapes or poses and a drone performing vision-based obstacle avoidance in unfamiliar environments (including wind disturbances and different obstacle densities). Our examples demonstrate that we can perform task-driven OOD detection within just a handful of trials. Comparisons with baselines also demonstrate the advantages of our approach in terms of providing statistical guarantees and being insensitive to task-irrelevant distribution shifts.
Feasibility of a serious game coupled with a contact-based session led by lived experience workers for depression prevention in high-school students

Background Stigma and limited mental health literacy impede adolescents getting the help they need for depressive symptoms. A serious game coupled with a classroom session led by lived experience workers (LEWs) might help to overcome these barriers. The school-based Strong Teens and Resilient Minds (STORM) preventive program employed this strategy and offered a serious game, Moving Stories. The current study was carried out to assess inhibiting and promoting factors for scaling up Moving Stories once its effectiveness has been ascertained. Methods Moving Stories was offered in three steps: (1) introductory classroom session, (2) students playing the game for five days, (3) debriefing classroom session led by lived experience worker. Data was collected on the number of participating students, costs of offering Moving Stories, and was further based on the notes of the debriefing sessions to check if mental health first aid (MHFA) strategies were addressed. Results Moving Stories was offered in seven high-schools. Coverage was moderate with 982 participating students out of 1880 (52%). Most participating students (83%) played the Moving Stories app three out of the five days. Qualitative data showed that the MHFAs were discussed in all debriefing sessions. Students showed great interest in lived experience workers’ stories and shared their own experiences with depression. Conclusions Bringing Moving Stories to scale in the high-school setting appears feasible, but will remain logistically somewhat challenging. Future implementation and scale-up of Moving Stories could benefit from improved selection and training of LEWs that played such an important role in grabbing the full attention of students and were able to launch frank discussions about depressive disorder and stigma in classrooms. Trial registration The study is registered in the Dutch Trial Register: Trial NL6444 (NTR6622: https://www.trialregister.nl/trial/6444).
Kitchens: The Culture of Restaurant Work

"Kitchens" takes us into the robust, overheated, backstage world of the contemporary restaurant. In this rich, often surprising portrait of the real lives of kitchen workers, Gary Alan Fine brings their experiences, challenges, and satisfactions to colourful life. He provides a riveting exploration of how restaurants actually work, both in-dividually and as part of a larger culinary culture. Working conditions, time constraints, market forces, and aesthetic goals all figure into the food served to customers - who often don't know quite what they're getting. The kitchen is a place of constant compromise, of quirks, approximations, dirty tricks, surprises, and short cuts, as Fine demonstrates in his deft, readable narrative. He brings to life the complicated relationships among kitchen workers - servers, dishwashers, pantry workers, managers, restaurant critics, and customers - and reveals the effects of organisational structure on individual relations.
Sickle cell disease patients in two London trusts: Genotyping including RH variants

All SCD patients need extended RBC antigen typing (by serology or genotyping) for provision of extended RH, K matched blood and to guide RBC selection in those with complex transfusion requirements. Genotyping can also identify RH variants which can cause sensitisation even when extended RH phenotypically matched blood is provided and alloantibodies associated with RH variants can cause HTRs.
How rich are false memories in a naturalistic context in healthy aging?

ABSTRACT The recall of factual and contextual information is a core characteristic of episodic memory sensitive to aging effects. The innovative aim of the present study was to assess in a naturalistic context the quantity and quality of correct and false free recalls among younger and older adults considering feature binding (What-Where-When-Details) and recollection (Remembering vs. Knowing). Thanks to virtual reality, we designed a multimodal environment simulating a lively town in which we implemented a variant of a DRM task rich in sets of semantically related items (e.g., fruits on a market stall). We asked 30 young and 30 older participants to navigate in the virtual environment, paying attention to the items, and then recall as many items and as much contextual information as possible and indicate the presence of recollection. As expected, older adults produced fewer correct recall but more intrusions than younger adults, and their correct recall was more deficient in binding and recollection. In both age groups, false recall was associated with the correct context inferred from a related set of items. However, the intrusions produced by older adults were highly recollected compared to those of the younger adults, and they were associated with false item-related contextual information.
Literature searching methods or guidance and their application to public health topics: A narrative review

Abstract Background Information specialists conducting searches for systematic reviews need to consider key questions around which and how many sources to search. This is particularly important for public health topics where evidence may be found in diverse sources. Objectives The objective of this review is to give an overview of recent studies on information retrieval guidance and methods that could be applied to public health evidence and used to guide future searches. Methods A literature search was performed in core databases and supplemented by browsing health information journals and citation searching. Results were sifted and reviewed. Results Seventy‐two papers were found and grouped into themes covering sources and search techniques. Public health topics were poorly covered in this literature. Discussion Many researchers follow the recommendations to search multiple databases. The review topic influences decisions about sources. Additional sources covering grey literature eliminate bias but are time‐consuming and difficult to search systematically. Public health searching is complex, often requiring searches in multidisciplinary sources and using additional methods. Conclusions Search planning is advisable to enable decisions about which and how many sources to search. This could improve with more work on modelling search scenarios, particularly in public health topics, to examine where publications were found and guide future research.
Impaired attentional bias toward one's own face in autism spectrum disorder: ERP evidence

Converging lines of evidence seem to indicate reduced self‐referential processing in individuals with autism spectrum disorder (ASD). However, processing of one's own face has rarely been investigated in the context of ASD. Thus, the aim of the present study was to elucidate the role of attentional biases in the processing of self‐ and other faces in ASD. To achieve this goal we presented participants with images of their own face, the face of a close‐other, and famous and unknown faces in a Stroop‐like paradigm. Participants (22 with ASD, 22 typically developing [TD]) were instructed to indicate the color of presented faces while EEG was recorded. Our event‐related potential results clearly showed that self‐face was associated with larger P3 amplitudes than all other faces in the TD group, thus indicating a strong attentional bias toward one's own face. In the ASD group, P3 to the self‐face and the close‐other's face did not differ, suggesting similar attentional biases in both cases. In line with these P3 findings, nonparametric cluster‐based permutation tests showed an analogous pattern of results: significant clusters for the self‐face compared with all other faces in the TD group, and no significant cluster in the ASD group. Overall, our findings revealed impaired attentional bias to one's own face and diminished self versus other differentiation in individuals with ASD. The similar neural underpinnings of the self‐face and other faces supports previous findings indicating reduced self‐prioritization among individuals with ASD.
Full breastfeeding protection against common enteric bacteria and viruses: results from the MAL-ED cohort study

ABSTRACT Background Breastfeeding is known to reduce the risk of enteropathogen infections, but protection from specific enteropathogens is not well characterized. Objective The aim was to estimate the association between full breastfeeding (days fed breast milk exclusively or with nonnutritive liquids) and enteropathogen detection. Methods A total of 2145 newborns were enrolled at 8 sites, of whom 1712 had breastfeeding and key enteropathogen data through 6 mo. We focused on 11 enteropathogens: adenovirus 40/41, norovirus, sapovirus, astrovirus, and rotavirus, enterotoxigenic Escherichia coli (ETEC), Campylobacter spp. , and typical enteropathogenic E. coli as well as entero-aggregative E. coli, Shigella and Cryptosporidium. Logistic regression was used to estimate the risk of enteropathogen detection in stools and survival analysis was used to estimate the timing of first detection of an enteropathogen. Results Infants with 10% more days of full breastfeeding within the preceding 30 d of a stool sample were less likely to have the 3 E. coli and Campylobacter spp. detected in their stool (mean odds: 0.92–0.99) but equally likely (0.99–1.02) to have the viral pathogens detected in their stool. A 10% longer period of full breastfeeding from birth was associated with later first detection of the 3 E. coli, Campylobacter, adenovirus, astrovirus, and rotavirus (mean HRs of 0.52–0.75). The hazards declined and point estimates were not statistically significant at 3 mo. Conclusions In this large multicenter cohort study, full breastfeeding was associated with lower likelihood of detecting 4 important enteric pathogens in the first 6 mo of life. These results also show that full breastfeeding is related to delays in the first detection of some bacterial and viral pathogens in the stool. As several of these pathogens are risk factors for poor growth during childhood, this work underscores the importance of exclusive or full breastfeeding during the first 6 mo of life to optimize early health.
Impact of COVID-19 Vaccination on Glycemia in Individuals With Type 1 and Type 2 Diabetes: Substudy of the COVAC-DM Study.

Vaccination and potentially related side effects might impact glucose management in people with diabetes. In this study, we investigated effects of COVID-19 vaccination on glycemia assessed by continuous glucose monitoring (CGM) in people with type 1 and type 2 diabetes. 74 participants of the ongoing multicenter prospective COVAC-DM-study, investigating the immune response to COVID-19 vaccines in people with diabetes, were willing to participate in this CGM sub-study. Time spent in glycemic ranges (time in range [TIR] 70-180 mg/dL; time below range [TBR] <70 mg/dL and time above range [TAR] >180 mg/dL) was assessed daily from two days prior to three days after the first COVID-19 vaccination. Participants were asked to document side effects in response to vaccination, insulin injections, and carbohydrate intake. 58 participants with type 1 (27 female, mean age 39.5 years, HbA1c 57 ± 12 mmol/mol) and 16 with type 2 diabetes (9 females, mean age 60.6 years, HbA1c 63 ± 11mmol/mol) were included in this study. The mean TIR did not change on the day of the vaccination and the following 3 days (p>0.05). In people with type 1 diabetes, the TIR (p=0.033) and the TAR (p= 0.043) deteriorated on days with side effects as compared to those without. Side effects occurring after COVID-19 vaccination significantly reduce the TIR and increase the TAR in people with type 1 diabetes, while no impact was observed in people with type 2 diabetes.
A pilot study of the association between leukoaraiosis and cerebral atherosclerosis using synthetic magnetic resonance imaging

Background Leukoaraiosis is a type of lesion characterized by tissue rarefaction or myelin pallor resulting from axons loss and gliosis. Synthetic magnetic resonance imaging (MRI) could yield quantitative T1, T2, proton density (PD) values of leukoaraiosis in addition to information on the volume of the lesion. Purpose To investigate the feasibility of quantifying leukoaraiosis using synthetic MRI and to explore the association between leukoaraiosis and cerebral small vascular diseases and cerebral atherosclerosis. Material and Methods Patients with acute ischemic stroke were enrolled in this study. All participants underwent a conventional T2-weighted image, brain volume, CUBE fluid attenuated inversion recovery, and synthetic MRI acquisition using a 3.0-T MR system. A time-of-flight magnetic resonance angiography was also obtained. We evaluated the T1, T2, PD values and leukoaraiosis volume. Results Analysis of the leukoaraiosis volume ratios demonstrated a positive association with T2 values, a negative association with T1 values, and no association with PD values. Leukoaraiosis volume ratios were independently correlated with age (P < 0.001), lacunes (P = 0.022), and cerebral microbleeds (P = 0.010). A statistical association was found between both age (P < 0.001) and lacunes (P = 0.047) and leukoaraiosis T2 values. Conclusion Synthetic MRI may enhance the evaluation of leukoaraiosis, in addition to providing information on its volume. Leukoaraiosis may represent a type of cerebral small vascular disease rather than cerebral atherosclerosis and may share the same pathological mechanism as lacunes and cerebral microbleeds.
Congenital deficiency reveals critical role of ISG15 in skin homeostasis

Ulcerating skin lesions are manifestations of human ISG15 deficiency, a type I interferonopathy. However, chronic inflammation may not be their exclusive cause. We describe two siblings with recurrent skin ulcers that healed with scar formation upon corticosteroid treatment. Both had a homozygous nonsense mutation in the ISG15 gene, leading to unstable ISG15 protein lacking the functional domain. We characterized ISG15–/– dermal fibroblasts, HaCaT keratinocytes, and human induced pluripotent stem cell–derived vascular endothelial cells. ISG15-deficient cells exhibited the expected hyperinflammatory phenotype, but also dysregulated expression of molecules critical for connective tissue and epidermis integrity, including reduced collagens and adhesion molecules, but increased matrix metalloproteinases. ISG15–/– fibroblasts exhibited elevated ROS levels and reduced ROS scavenger expression. As opposed to hyperinflammation, defective collagen and integrin synthesis was not rescued by conjugation-deficient ISG15. Cell migration was retarded in ISG15–/– fibroblasts and HaCaT keratinocytes, but normalized under ruxolitinib treatment. Desmosome density was reduced in an ISG15–/– 3D epidermis model. Additionally, there were loose architecture and reduced collagen and desmoglein expression, which could be reversed by treatment with ruxolitinib/doxycycline/TGF-β1. These results reveal critical roles of ISG15 in maintaining cell migration and epidermis and connective tissue homeostasis, whereby the latter likely requires its conjugation to yet unidentified targets.
Dynamic computation of value signals via a common neural network in multi-attribute decision-making

Abstract Studies in decision neuroscience have identified robust neural representations for the value of choice options. However, overall values often depend on multiple attributes, and it is not well understood how the brain evaluates different attributes and integrates them to combined values. In particular, it is not clear whether attribute values are computed in distinct attribute-specific regions or within the general valuation network known to process overall values. Here, we used a functional magnetic resonance imaging choice task in which abstract stimuli had to be evaluated based on variations of the attributes color and motion. The behavioral data showed that participants responded faster when overall values were high and attribute value differences were low. On the neural level, we did not find that attribute values were systematically represented in areas V4 and V5, even though these regions are associated with attribute-specific processing of color and motion, respectively. Instead, attribute values were associated with activity in the posterior cingulate cortex, ventral striatum and posterior inferior temporal gyrus. Furthermore, overall values were represented in dorsolateral and ventromedial prefrontal cortex, and attribute value differences in dorsomedial prefrontal cortex, which suggests that these regions play a key role for the neural integration of attribute values.