Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Selection Bias and Group Importance: Differentiable Reparameterization for the Hypergeometric Distribution

Partitioning a set of elements into a given number of groups of a priori unknown sizes is a critical task in many applications. It can be characterized by a hypergeo-metric distribution, which describes biased sampling without replacement based on the relative importance between classes of samples. Due to hard constraints, this discrete distribution is not differentiable in its standard formulation, prohibiting its use in modern machine learning frameworks. Hence, previous works mostly fall back on suboptimal heuristics or simpliﬁed assumptions. In this work, we propose a differentiable reparameterization trick for the multivariate noncentral hypergeometric distribution. We introduce reparameterizable gradients to enable learning of the importance or the selection bias between groups. We highlight the applicability and usability of the proposed formulation in two different experiments: weakly-supervised learning and clustering.
Design of a low‐profile H‐plane siw horn antenna with modified flare walls

A low‐profile H‐plane horn antenna with modified flare walls based on the substrate integrated waveguide (SIW) technology is presented. To deal with the common problem of impedance mismatch in SIW horns built on thin substrates, modified flare walls consisting of a rolled edge section and a curved throat section are employed. In addition, the arc‐shaped grated transition, which can also lower the reflection coefficient, is introduced after the horn aperture. The antenna is implemented on a PTFE substrate. Measured |S11| of the proposed antenna is below −10 dB from 12.0 to 15.5 GHz. Therefore, the proposed design can achieve an enhanced bandwidth of 25.5% with a substrate thickness of only 0.092 λ0, showing good impedance matching performance. © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:3012–3015, 2016
Characterization of Glucokinases from Pathogenic Free-Living Amoebae

Infection with pathogenic free-living amoebae, including Naegleria fowleri, Acanthamoeba spp. , and Balamuthia mandrillaris, can lead to life-threatening illnesses, primarily because of catastrophic central nervous system involvement. Efficacious treatment options for these infections are lacking, and the mortality rate due to infection is high. ABSTRACT Infection with pathogenic free-living amoebae, including Naegleria fowleri, Acanthamoeba spp. , and Balamuthia mandrillaris, can lead to life-threatening illnesses, primarily because of catastrophic central nervous system involvement. Efficacious treatment options for these infections are lacking, and the mortality rate due to infection is high. Previously, we evaluated the N. fowleri glucokinase (NfGlck) as a potential target for therapeutic intervention, as glucose metabolism is critical for in vitro viability. Here, we extended these studies to the glucokinases from two other pathogenic free-living amoebae, including Acanthamoeba castellanii (AcGlck) and B. mandrillaris (BmGlck). While these enzymes are similar (49.3% identical at the amino acid level), they have distinct kinetic properties that distinguish them from each other. For ATP, AcGlck and BmGlck have apparent Km values of 472.5 and 41.0 μM, while Homo sapiens Glck (HsGlck) has a value of 310 μM. Both parasite enzymes also have a higher apparent affinity for glucose than the human counterpart, with apparent Km values of 45.9 μM (AcGlck) and 124 μM (BmGlck) compared to ~8 mM for HsGlck. Additionally, AcGlck and BmGlck differ from each other and other Glcks in their sensitivity to small molecule inhibitors, suggesting that inhibitors with pan-amoebic activity could be challenging to generate.
A mapping review of adolescent identity after TBI: what clinicians need to know

ABSTRACT Adolescence is a critical period for developing a sense of identity, an iterative process that relies on the development of skills such as self-reflection and self-appraisal. Outcomes of identity development include personal ethics, knowledge of one's strengths and challenges, and, ultimately, independence. Traumatic brain injury (TBI) affects these outcomes in adults, when identity has been established and may need to be redefined; but what happens when an injury occurs while identity is being formed? To answer this question, we used mapping review methodology to explore TBI effects on adolescent identity formation, mapping the evidence onto a biopsychosocial framework for rehabilitation. We reviewed studies on identity in adolescents with mild to severe TBI ages 13–18 years, published from inception to 2021, with a focus on outpatient rehabilitation settings. Key findings of the mapping review noted adolescents post-TBI are likely to: (1) question their identity considering dissonance between the current self and their pre-injury self; and (2) seek to establish new, adaptive meanings and identities. All studies drew conclusions regarding identity and participation of adolescents post-TBI. Results provided insight into the importance of considering individualized rehabilitation interventions for adolescents, given their unique developmental path towards identity formation.
On Elimination Strategies for Bandit Fixed-Confidence Identification

Elimination algorithms for bandit identification, which prune the plausible correct answers sequentially until only one remains, are computationally convenient since they reduce the problem size over time. However, existing elimination strategies are often not fully adaptive (they update their sampling rule infrequently) and are not easy to extend to combinatorial settings, where the set of answers is exponentially large in the problem dimension. On the other hand, most existing fully-adaptive strategies to tackle general identification problems are computationally demanding since they repeatedly test the correctness of every answer, without ever reducing the problem size. We show that adaptive methods can be modified to use elimination in both their stopping and sampling rules, hence obtaining the best of these two worlds: the algorithms (1) remain fully adaptive, (2) suffer a sample complexity that is never worse of their non-elimination counterpart, and (3) provably eliminate certain wrong answers early. We confirm these benefits experimentally, where elimination improves significantly the computational complexity of adaptive methods on common tasks like best-arm identification in linear bandits.
LexiconNet: An End-to-End Handwritten Paragraph Text Recognition System

In this study, we have presented an eﬃcient procedure using two state-of-the-art approaches from literature of handwritten text recognition as Vertical Attention Network and Word Beam Search. The attention module is responsible for internal line segmentation that consequently processes a page in a line-by-line manner. At the decoding step, we have added a connectionist temporal classiﬁcation-based word beam search decoder as a post-processing step. In this study, an end-to-end paragraph recognition system is presented with a lexicon decoder as a post-processing step. We named our model as Lexi-conNet. Our procedure reports state-of-the-art results on standard datasets. The reported character error rate is 3.24% on the IAM dataset with 27.19% improvement, 1.13% on RIMES with 40.83% improvement and 2.43% on the READ-16 dataset with 32.31% improvement from existing literature and the word error rate is 8.29% on IAM dataset with 43.02% improvement, 2.94% on RIMES dataset with 56.25% improvement and 7.35% on READ-2016 dataset with 47.27% improvement from the existing results. The character error rate and word error rate reported in this work surpass the results reported in the literature.
An analogical model for the stationary black holes by the flow field of space-time fluid around a 3-dimensional point sink

Recent researches suggest an analogy between the theory of general relativity (GR) and ﬂuid dynamics. As a result of this analogy, the Navier-Stokes equations and Einstein ﬁeld equations are the same, and it is possible to study the properties of space-time by using ﬂuid mechanics. In this paper, we present a new model to describe gravitational phenomena by an inviscid and compressible ﬂuid called space-time ﬂuid (STF). The analogy method is used to obtain the gravity ﬁeld of both static and rotating masses from the ﬂow ﬁeld of STF around static and rotating point sink. In addition, event horizons and the ergosphere of stationary black holes are deﬁned based on our STF model. Then, we compare hydrodynamic forces exerted on a test particle with gravitational forces in the gravitoelectromagnetic approximation of the GR. As a natural consequence, it is shown that inertial and gravitational masses are equivalent in this analogy. Finally, using the aspect of ﬂuid dynamics, the Mach’s principle, weak equivalence principle, and information discontinuity on the event horizon are discussed.
How Useful are Gradients for OOD Detection Really?

One critical challenge in deploying highly performant machine learning models in real-life applications is out of distribution (OOD) detection. Given a predictive model which is accurate on in distribution (ID) data, an OOD detection system will further equip the model with the option to defer prediction when the input is novel and the model has little conﬁdence in prediction. There has been some recent interest in utilizing the gradient information in pre-trained models for OOD detection. While these methods have shown competitive performance, there are misconceptions about the true mechanism underlying them, which conﬂate their performance with the necessity of gradients. In this work, we provide an in-depth analysis and comparison of gradient based methods and elucidate the key components that warrant their OOD detection performance. We further propose a general, non-gradient based method of OOD detection which improves over previous baselines in both performance and computational efﬁciency.
WALNUT: A Benchmark on Semi-weakly Supervised Learning for Natural Language Understanding

Building machine learning models for natural language understanding (NLU) tasks relies heavily on labeled data. Weak supervision has been proven valuable when large amount of labeled data is unavailable or expensive to obtain. Existing works studying weak supervision for NLU either mostly focus on a specific task or simulate weak supervision signals from ground-truth labels. It is thus hard to compare different approaches and evaluate the benefit of weak supervision without access to a unified and systematic benchmark with diverse tasks and real-world weak labeling rules. In this paper, we propose such a benchmark, named WALNUT, to advocate and facilitate research on weak supervision for NLU. WALNUT consists of NLU tasks with different types, including document-level and token-level prediction tasks. WALNUT is the first semi-weakly supervised learning benchmark for NLU, where each task contains weak labels generated by multiple real-world weak sources, together with a small set of clean labels. We conduct baseline evaluations on WALNUT to systematically evaluate the effectiveness of various weak supervision methods and model architectures. Our results demonstrate the benefit of weak supervision for low-resource NLU tasks and highlight interesting patterns across tasks. We expect WALNUT to stimulate further research on methodologies to leverage weak supervision more effectively. The benchmark and code for baselines are available at aka.ms/walnut_benchmark.
Targeting the human βc receptor inhibits inflammatory myeloid cells and lung injury caused by acute cigarette smoke exposure

Chronic obstructive pulmonary disease (COPD) is a devastating disease commonly caused by cigarette smoke (CS) exposure that drives tissue injury by persistently recruiting myeloid cells into the lungs. A significant portion of COPD patients also present with overlapping asthma pathology including eosinophilic inflammation. The βc cytokine family includes granulocyte monocyte‐colony‐stimulating factor, IL‐5 and IL‐3 that signal through their common receptor subunit βc to promote the expansion and survival of multiple myeloid cells including monocytes/macrophages, neutrophils and eosinophils.
Maternal immune activation accelerates puberty initiation and alters mechanical allodynia in male and female C57BL6/J mice

The mechanisms that link maternal immune activation (MIA) with the onset of neurodevelopmental disorders remain largely unclear. Accelerated puberty is also associated with a heightened risk for psychopathology in later life but there is a dearth of evidence on the impacts of maternal infection on pubertal timing. We examined the effects of MIA on reproductive development, mechanical allodynia, and sensorimotor gating in juvenile, adolescent, and adult male and female mice. Moreover, we investigated hypothalamic neural markers associated with the reproductive and stress axes. Finally, we tested the mitigating effects of environmental enrichment (EE), which has clinical relevancy in human rehabilitation settings. Our results show that administration of polyinosinic-polycytidylic acid (poly(I:C)) on gestational day 12.5 led to early preputial separation, vaginal openings, and age of first estrus in offspring. MIA exposure altered pain sensitivity across development and modestly altered prepulse inhibition. The downregulation of Nr3c1 and Oprk mRNA in the hypothalamus of juvenile mice suggests that MIA’s effects may be mediated through disruption of hypothalamic-pituitary-adrenal axis activity. In contrast, life-long housing with EE rescued many of these MIA-induced consequences. Overall, our findings suggest that accelerated puberty may be associated with the deleterious effects of infection during pregnancy and the onset of psychopathology.
The end of life of patients with COVID‐19 in intensive care unit and the stress level on their family members: A cross‐sectional study

Abstract Background During the Coronavirus disease 2019 (COVID‐19) pandemic, hospital visits were suspended and video calls were offered to connect patients with their family members, especially toward the end of life (EoL). Aim The primary aim was to describe EoL care for COVID‐19 patients dying in an intensive care unit (ICU). The secondary aim was to explore whether making video calls and allowing visits was associated with lower death‐related stress in family members. Design Single centre cross‐sectional study. The setting was the ICU of a COVID‐19 center in northern Italy, during the first year of the pandemic. Data on patients who died in the ICU were collected; death‐related stress on their family members was measured using the Impact of Event Scale‐Revised (IES‐R). The statistical association was tested by means of logistic regression. Results The study sample included 70 patients and 56 family members. All patients died with mechanical ventilation, hydration, nutrition, analgesia and sedation ongoing. Resuscitation procedures were performed in 5/70 patients (7.1%). Only 6/56 (10.7%) of the family members interviewed had visited their loved ones in the ICU and 28/56 (50%) had made a video call. EoL video calls were judged useful by 53/56 family members (94.6%) but all (56/56, 100%) wished they could have visited the patient. High‐stress levels were found in 38/56 family members (67.9%), regardless of whether they were allowed ICU access or made a video call. Compared with other degrees of kinship, patients' offspring were less likely to show a positive IES‐R score (odds ratio [OR] 0.22, 95% confidence interval [CI] 0.05 to 0.89). Conclusions During the first year of the COVID‐19 pandemic, patients died without their family members at the bedside while on life‐sustaining treatment. Stress levels were high in most family members, especially in patients' spouses. Video calls or ICU visits were judged favourably by family members but insufficient to alleviate death‐related stress. Relevance for Clinical Practice During a pandemic, ICU access by patients' family members should be considered, particularly as the time of death approaches. Although generally appreciated by family members, EoL video calls should be arranged together with other measures to alleviate death‐related stress, especially for the patient's spouse.
Comparison of Photographic Screening Methods for Diabetic Retinopathy – A Meta-analysis

ABSTRACT Introduction Diabetic Retinopathy (DR) is a leading cause of irreversible visual impairment and blindness in both developed and developing countries. Although the merits of DR screening are well recognized, significant variations in screening practices including imaging modality still exists. Purpose To evaluate and compare the sensitivity and specificity of mydriatic and non-mydriatic photographic screening methods using 7-Field fundus photography or dilated fundus examination (DFE) by an ophthalmologist as reference standard. Methods A systematic review using PRISMA Guidelines was conducted by online search of MEDLINE, Web of Science, and other repositories of all available studies from 1990 until 2019. A total of 62 studies were included in the meta-analysis from a total of 406 suitable abstracts screened and 95 articles reviewed in full. Data were collected using a standardized extraction form independently, with all authors masked to others’ search results. Results For the detection of any DR (ADR), sensitivity ranged from 81% with single field to a maximum of 99% for 4–7 fields and wide-angle images. For detection of referable DR (RDR) sensitivity ranged from 76% for single field to 93% for wide-angle photography. Specificity was lowest at 91% for wide-angle images and greatest at 99% for three field photography. Study heterogeneity was noted to be significant, which was partly attributed to the range of DR classification between studies. Conclusions The sensitivity and specificity of DR screening are positively associated with number of photographic fields. Pooled estimates suggest non-mydriatic two-field photography may be sufficient for screening detection of ADR and RDR.
Dynamic-Memory Event-Based Asynchronous Attack Detection Filtering for a Class Of Nonlinear Cyber-Physical Systems

In this article, we endeavor to address the problem of asynchronous attack detection for a class of nonlinear cyber-physical system (CPS) under the dynamic-memory event-triggered transmission protocol (DMETP). Malicious attacks under consideration are composed of the parameter-dependent false data-injection (FDI) attacks and the multichannel deception attacks with the time delay. Meanwhile, we attempt to employ the T–S fuzzy singular Semi-Markov jump linear parameter-varying (SS-MJLPV) systems to describe the stochastic jump characteristics and nonlinear time-varying characteristics of CPSs. Then, the DMETP is proposed for the first time, which can not only relieve the bandwidth pressure but also obtain the key released packets at the crest or trough of the curve. By augmenting the states of the original system and the asynchronous attacks detection filter, the issue of attacks detection is converted into an auxiliary dissipative filtering for the CPS. Moreover, sufficient conditions for the existence of the attacks detection filter are obtained. Finally, the effectiveness of the proposed scheme is verified via the networked truck-trailer reversing system.
Calibration of Water Demand Multipliers in Water Distribution Systems Using Genetic Algorithms

AbstractHydraulic models have been widely used for design, analysis, and operation of water distribution systems. As with all hydraulic models, water demands are one of the main parameters that cause the most uncertainty to the model outputs. However, the calibration of the water demands is usually not feasible attributable to the limited quantity of available measurements in most real water networks. This paper presents an approach to calibration of the demand multiplier factors under an ill-posed condition where the number of measurements is less than the number of parameter variables. The problem is solved using a genetic algorithm (GA). The results show that not only is the GA able to match the calibrated values at measured locations, but by using multiple runs of the GA model, the flow rates and nodal heads at nonmeasured locations can be estimated. Three case studies are presented as an illustration of the problem. The first case study is a small network that demonstrates the calibration model. The .
Identification of resource needs in postgraduate studies

The desire to pursue higher education is constantly increasing. The government and the institutions of higher learning are striving to attract more students , especially at the graduate level by making every effort to provide quality education. Some scholars proposed that the strategic success of a service organization depends on its ability to consistently meet or exceed customer service expectations . Students as institutions’ customer, need information and support to cope in balancing the demands of the different environments . One of the major problems facing by the higher education nowadays is attrition and completion rates. To manage this issue, a constant effort should be made. The resources offered by the institution determine the ability of the institution to manage their students effectively. The main thrust of this paper is to provide the relevant input for resource development in institutional of higher education. It is hoped to assist the institution to identify and address implementation is s u es related to postgraduate studies. The major contribution of this review is the guideline for effective resource and in developing distinguished human capital.
Restoration of Vision and Retinal Responses After Adeno-Associated Virus–Mediated Optogenetic Therapy in Blind Dogs

Purpose Optogenetic gene therapy to render remaining retinal cells light-sensitive in end-stage retinal degeneration is a promising strategy for treatment of individuals blind because of a variety of different inherited retinal degenerations. The clinical trials currently in progress focus on delivery of optogenetic genes to ganglion cells. Delivery of optogenetic molecules to cells in the outer neural retina is predicted to be even more advantageous because it harnesses more of the retinal circuitry. However, this approach has not yet been tested in large animal models. For this reason, we evaluated the safety and efficacy of optogenetic therapy targeting remaining diseased cone photoreceptors in the Rcd1 dog model of retinitis pigmentosa. Methods Imaging and measures of retinal function and functional vision were carried out, as well as terminal studies evaluating multi-electrode array recordings and histology. Results Animals remained healthy and active throughout the study and showed improved retinal and visual function as assessed by electroretinography and visual-evoked potentials, improved navigational vision, and improved function of cone photoreceptors and the downstream retinal circuitry. Conclusions The findings demonstrate that an optogenetic approach targeting the outer retina in a blind large animal model can partially restore vision. Translational Relevance This work has translational relevance because the approach could potentially be extrapolated to treat humans who are totally blind because of retinal degenerative disease.