Miracle-dz/UniFAR-FTUs huggingface dataset:
Literature-Augmented Clinical Outcome Prediction

We present BEEP (Biomedical Evidence-Enhanced Predictions), a novel approach for clinical outcome prediction that retrieves patient-specific medical literature and incorporates it into predictive models. Based on each individual patient's clinical notes, we train language models (LMs) to find relevant papers and fuse them with information from notes to predict outcomes such as in-hospital mortality. We develop methods to retrieve literature based on noisy, information-dense patient notes, and to augment existing outcome prediction models with retrieved papers in a manner that maximizes predictive accuracy. Our approach boosts predictive performance on three important clinical tasks in comparison to strong recent LM baselines, increasing F1 by up to 5 points and precision@Top-K by a large margin of over 25%.
Single Image Object Counting and Localizing using Active-Learning

The need to count and localize repeating objects in an image arises in different scenarios, such as biological microscopy studies, production-lines inspection, and surveillance recordings analysis. The use of supervised Convolutional Neural Networks (CNNs) achieves accurate object detection when trained over large class-specific datasets. The labeling effort in this approach does not pay-off when the counting is required over few images of a unique object class.We present a new method for counting and localizing repeating objects in single-image scenarios, assuming no pre-trained classifier is available. Our method trains a CNN over a small set of labels carefully collected from the input image in few active-learning iterations. At each iteration, the latent space of the network is analyzed to extract a minimal number of user-queries that strives to both sample the in-class manifold as thoroughly as possible as well as avoid redundant labels.Compared with existing user-assisted counting methods, our active-learning iterations achieve state-of-the-art performance in terms of counting and localizing accuracy, number of user mouse clicks, and running-time. This evaluation was performed through a large user study over a wide range of image classes with diverse conditions of illumination and occlusions.
Global manufacturing and innovation: a supply chain perspective

Unilever is a global manufacturer of home, personal care, and food products, and like many multinational companies today, is looking for the best ways to expand its markets and meet the rising challenges of managing its crucial supply chain. The company's current strategy is to focus its research and development and marketing on the leading brands, that is, those that are most in demand from consumers. To better optimise this strategy, the company recently identified its top 400 brands - the ones that were producing 90% of its income. It then seized the opportunity to reduce the number of brands from 1,600 down to these 400 and, through extensive knowledge of trends identified today, will continue to develop the brands to meet the needs of consumers' future needs. The company wants to put more eggs in fewer baskets, and expend its efforts and resources into growing the big, profitable brands, in order to create "fuel for growth".
Entropy optimized semi-supervised decomposed vector-quantized variational autoencoder model based on transfer learning for multiclass text classification and generation

Semisupervised text classification has become a major focus of research over the past few years. Hitherto, most of the research has been based on supervised learning, but its main drawback is the unavailability of labeled data samples in practical applications. It is still a key challenge to train the deep generative models and learn comprehensive representations without supervision. Even though continuous latent variables are employed primarily in deep latent variable models, discrete latent variables, with their enhanced understandability and better compressed representations, are effectively used by researchers. In this paper, we propose a semisupervised discrete latent variable model for multi-class text classification and text generation. The proposed model employs the concept of transfer learning for training a quantized transformer model, which is able to learn competently using fewer labeled instances. The model applies decomposed vector quantization technique to overcome problems like posterior collapse and index collapse. Shannon entropy is used for the decomposed sub-encoders, on which a variable DropConnect is applied, to retain maximum information. Moreover, gradients of the Loss function are adaptively modified during backpropagation from decoder to encoder to enhance the performance of the model. Three conventional datasets of diversified range have been used for validating the proposed model on a variable number of labeled instances. Experimental results indicate that the proposed model has surpassed the state-of-the-art models remarkably.
Reinforcement Learning with Feedback from Multiple Humans with Diverse Skills

A promising approach to improve the robustness and exploration in Reinforcement Learning is collecting human feedback and that way incorporating prior knowledge of the target environment. It is, however, often too expensive to obtain enough feedback of good quality. To mitigate the issue, we aim to rely on a group of multiple experts (and non-experts) with different skill levels to generate enough feedback. Such feedback can therefore be inconsistent and infrequent. In this paper, we build upon prior work -- Advise, a Bayesian approach attempting to maximise the information gained from human feedback -- extending the algorithm to accept feedback from this larger group of humans, the trainers, while also estimating each trainer's reliability. We show how aggregating feedback from multiple trainers improves the total feedback's accuracy and make the collection process easier in two ways. Firstly, this approach addresses the case of some of the trainers being adversarial. Secondly, having access to the information about each trainer reliability provides a second layer of robustness and offers valuable information for people managing the whole system to improve the overall trust in the system. It offers an actionable tool for improving the feedback collection process or modifying the reward function design if needed. We empirically show that our approach can accurately learn the reliability of each trainer correctly and use it to maximise the information gained from the multiple trainers' feedback, even if some of the sources are adversarial.
A Survey on Neural-symbolic Systems

—In recent years, neural systems have demonstrated superior perceptual intelligence through highly effective learning, but their reasoning capabilities remain poor. In contrast, symbolic systems have exceptional cognitive intelligence through efﬁcient reasoning, but their learning capabilities are poor. In this case, an ideal intelligent system–a neural-symbolic system–with high perceptual and cognitive intelligence through powerful learning and reasoning capabilities gains a growing interest in the research community. Combining the fast computation ability of neural systems and the powerful expression ability of symbolic systems, neural-symbolic systems can perform effective learning and reasoning in multi-domain tasks, demonstrating concurrent perception and cognition capabilities in intelligent systems. This paper surveys the latest research in neural-symbolic systems along four dimensions: the necessity of combination, technical challenges, methods, and applications. This paper aims to help advance this emerging area of research by providing researchers with a holistic and comprehensive view, highlighting the state of art and identifying the opportunities.
Exploring strange origin of Dirac neutrino masses at hadron colliders

We consider the possibility that Dirac neutrino masses may be a manifestation of chiral symmetry breaking via non-perturbative QCD dynamics. The key role played by light quarks in this mechanism can naturally lead to signals that are accessible to hadron colliders. Bounds from charged meson decays imply a dominant effect from the strange quark condensate. We propose a model for Dirac neutrino mass generation with an extra Higgs doublet at the TeV scale and signiﬁcant coupling to strange quarks and leptons. Current data on D − ¯ D mixing constrain the allowed parameter space of the model, and a 100 TeV pp collider would either discover or largely exclude it. A distinct feature of this scenario is that measurements of the of charged Higgs leptonic branching ratios can distinguish between “normal” and “inverted” neutrino mass hierarchies, complementing future determinations at neutrino oscillation experiments.
Blockage Prediction Using Wireless Signatures: Deep Learning Enables Real-World Demonstration

Overcoming the link blockage challenges is essential for enhancing the reliability and latency of millimeter wave (mmWave) and sub-terahertz (sub-THz) communication networks. Previous approaches relied mainly on either (i) multiple-connectivity, which under-utilizes the network resources, or on (ii) the use of out-of-band and non-RF sensors to predict link blockages, which is associated with increased cost and system complexity. In this paper, we propose a novel solution that relies only on in-band mmWave wireless measurements to proactively predict future dynamic line of sight (LOS) link blockages. The proposed solution utilizes deep neural networks and special patterns of received signal power, that we call pre-blockage wireless signatures to infer future blockages. Specifically, the developed machine learning models attempt to predict: (i) If a future blockage will occur? (ii) When will this blockage happen? (iii) What is the type of the blockage? And (iv) what is the direction of the moving blockage? To evaluate our proposed approach, we build a large-scale real-world dataset comprising nearly 0.5 million data points (mmWave measurements) for both indoor and outdoor blockage scenarios. The results, using this dataset, show that the proposed approach can successfully predict the occurrence of future dynamic blockages with more than 85% accuracy. Further, for the outdoor scenario with highly-mobile vehicular blockages, the proposed model can predict the exact time of the future blockage with less than 100 ms error for blockages happening within the future 600 ms. These results, among others, highlight the promising gains of the proposed proactive blockage prediction solution which could potentially enhance the reliability and latency of future wireless networks.
N ov 2 02 1 Electronic Response Time around Tunnel Exit in Strong-Field Ionization

C. Chen, J. Y. Che, W. Y. Li, S. Wang, X. J. Xie, J. Y. Huang, Y. G. Peng, G. G. Xin, and Y. J. Chen 1.College of Physics and Information Technology, Shaan’xi Normal University, Xi’an, China 2.School of Mathematics and Science, Hebei GEO University, Shijiazhuang, China 3.College of Physics and Hebei Key Laboratory of Photophysics Research and Application, Hebei Normal University, Shijiazhuang, China 4.School of Physics, Northwest University, Xi’an, China (Dated: November 17, 2021)
Numerical analysis of secondary flow in a two-stage turbine

The three-dimensional viscous average passage flow in the Pratt and Whitney alternate design Space Shuttle Main Engine fuel turbine has been simulated. The effect of secondary flows generated by upstream blade rows on the performance of downstream blade rows is studied. The numerical results are compared to the design intent to validate improved models in the average passage equations. Analysis of the results centers on the primary spanwise mixing mechanism in this low aspect ratio turbine. A multigrid method has also been incorporated to improve the overall convergence rate of the numerical algorithm.
Explaining medical AI performance disparities across sites with confounder Shapley value analysis

Medical AI algorithms can often experience degraded performance when evaluated on previously unseen sites. Addressing cross-site performance disparities is key to ensuring that AI is equitable and effective when deployed on diverse patient populations. Multi-site evaluations are key to diagnosing such disparities as they can test algorithms across a broader range of potential biases such as patient demographics, equipment types, and technical parameters. However, such tests do not explain why the model performs worse. Our framework provides a method for quantifying the marginal and cumulative effect of each type of bias on the overall performance difference when a model is evaluated on external data. We demonstrate its usefulness in a case study of a deep learning model trained to detect the presence of pneumothorax, where our framework can help explain up to 60% of the discrepancy in performance across different sites with known biases like disease comorbidities and imaging parameters.
An adaptive dimension reduction algorithm for latent variables of variational autoencoder

—This paper proposes a new pattern of inactivating variables to address the dimension reduction issue of latent variables in VAE related models. By introducing π vector whose elements are 0 or 1, and further utilizing the element-wise multiplication between π and variables, we develop an adaptive dimension reduction algorithm to remove those latent variables inactivated by 0 component of π for the purpose of dimension reduction. We make some theoretical analysis about the convergence and local convexity of the algorithm output, i.e., the convergent dimension. These encouraging results are also supported by numerical experiments. Six frequently-used data sets, including two large-sized ones, are illustrated that the proposed algorithm is quite effective both in precision and in efﬁciency. A potential application of the algorithm is to generate VAE related models with more compact structure and higher precision, except for reducing dimension.
Products of Classes of Finite Structures

We study the preservation of certain properties under products of classes of finite structures. In particular, we examine indivisibility, definable self-similarity, the amalgamation property, and the disjoint n-amalgamation property. We explore how each of these properties interacts with the lexicographic product, full product, and free superposition of classes of structures. Additionally, we consider the classes of theories which admit configurations indexed by these products. In particular, we show that, under mild assumptions, the products considered in this paper do not yield new classes of theories.
Mathematical Models for Local Sensing Hashes

As data volumes continue to grow, searches in data are becoming increasingly time-consuming. Classical index structures for neighbor search are no longer sustainable due to the “curse of dimensionality”. Instead, approximated index structures offer a good opportunity to significantly accelerate the neighbor search for clustering and outlier detection and to have the lowest possible error rate in the results of the algorithms. Local sensing hashes is one of those. We indicate directions to mathematically model the properties of it.
Phase function estimation from a diffuse optical image via deep learning

Objective. The phase function is a key element of a light propagation model for Monte Carlo (MC) simulation, which is usually fitted with an analytic function with associated parameters. In recent years, machine learning methods were reported to estimate the parameters of the phase function of a particular form such as the Henyey–Greenstein phase function but, to our knowledge, no studies have been performed to determine the form of the phase function. Approach. Here we design a convolutional neural network (CNN) to estimate the phase function from a diffuse optical image without any explicit assumption on the form of the phase function. Specifically, we use a Gaussian mixture model (GMM) as an example to represent the phase function generally and learn the model parameters accurately. The GMM is selected because it provides the analytic expression of phase function to facilitate deflection angle sampling in MC simulation, and does not significantly increase the number of free parameters. Main Results. Our proposed method is validated on MC-simulated reflectance images of typical biological tissues using the Henyey–Greenstein phase function with different anisotropy factors. The mean squared error of the phase function is 0.01 and the relative error of the anisotropy factor is 3.28%. Significance. We propose the first data-driven CNN-based inverse MC model to estimate the form of scattering phase function. The effects of field of view and spatial resolution are analyzed and the findings provide guidelines for optimizing the experimental protocol in practical applications.
SPANN: Highly-efficient Billion-scale Approximate Nearest Neighbor Search

The in-memory algorithms for approximate nearest neighbor search (ANNS) have achieved great success for fast high-recall search, but are extremely expensive when handling very large scale database. Thus, there is an increasing request for the hybrid ANNS solutions with small memory and inexpensive solid-state drive (SSD). In this paper, we present a simple but efficient memory-disk hybrid indexing and search system, named SPANN, that follows the inverted index methodology. It stores the centroid points of the posting lists in the memory and the large posting lists in the disk. We guarantee both disk-access efficiency (low latency) and high recall by effectively reducing the disk-access number and retrieving high-quality posting lists. In the index-building stage, we adopt a hierarchical balanced clustering algorithm to balance the length of posting lists and augment the posting list by adding the points in the closure of the corresponding clusters. In the search stage, we use a query-aware scheme to dynamically prune the access of unnecessary posting lists. Experiment results demonstrate that SPANN is 2$\times$ faster than the state-of-the-art ANNS solution DiskANN to reach the same recall quality $90\%$ with same memory cost in three billion-scale datasets. It can reach $90\%$ recall@1 and recall@10 in just around one millisecond with only 32GB memory cost. Code is available at: {\footnotesize\color{blue}{\url{https://github.com/microsoft/SPTAG}}}.
Regular rhythmic and audio-visual stimulations enhance procedural learning of a perceptual-motor sequence in healthy adults: A pilot study

Procedural learning is essential for the effortless execution of many everyday life activities. However, little is known about the conditions influencing the acquisition of procedural skills. The literature suggests that sensory environment may influence the acquisition of perceptual-motor sequences, as tested by a Serial Reaction Time Task. In the current study, we investigated the effects of auditory stimulations on procedural learning of a visuo-motor sequence. Given that the literature shows that regular rhythmic auditory rhythm and multisensory stimulations improve motor speed, we expected to improve procedural learning (reaction times and errors) with repeated practice with auditory stimulations presented either simultaneously with visual stimulations or with a regular tempo, compared to control conditions (e.g., with irregular tempo). Our results suggest that both congruent audio-visual stimulations and regular rhythmic auditory stimulations promote procedural perceptual-motor learning. On the contrary, auditory stimulations with irregular or very quick tempo alter learning. We discuss how regular rhythmic multisensory stimulations may improve procedural learning with respect of a multisensory rhythmic integration process.
Cultural and religious determinants of HIV transmission: A qualitative study with people living with HIV in Belu and Yogyakarta, Indonesia

As a part of a larger qualitative study to understand HIV-risk factors and impacts on people living with HIV (PLHIV) (52 women and 40 men) in Belu and Yogyakarta, Indonesia, this paper reports the influences of cultural practices and religious beliefs on sexual relationships and behaviours of participants as contributors for HIV transmission. This study was conducted from June to December 2020. Data collection was conducted using one-one-one in-depth interviews. Participants were recruited using the snowball sampling technique. Data analysis was guided by a qualitative data analysis framework. The findings showed that cultural practices in Belu related to the use of bride wealth, managing spousal disputes, marriage, and condom use, influenced spousal relationships and sexual behaviours or practices which contributed to HIV transmission. Javanese cultural practices and expectation of an ‘ideal wife’, Islamic religious beliefs about expected husband-wife relationships, forbidden premarital sex, and the participants’ individual interpretation of their religious beliefs about condom use spousal sexual relations, also influenced spousal sexual relations and behaviours, which supported HIV transmission among the participants. The findings indicate the need for HIV education programs that address cultural practices and religious beliefs for community members and population groups to enhance their understanding about HIV, condom use, and how cultural practices and religious beliefs play a role in HIV transmission. The findings also indicate the need for involvement of religious leaders in HIV education programs to bring insights to people and help them interpret their religious beliefs in health promoting ways. Future studies that explore different aspects of culture and religion which may contribute to HIV transmission are recommended.
Template-Based Expression and Reuse of Design Knowledge

Reuse of existing design knowledge is the key to realize rapid product design. Analysis of present knowledge capture, representation and reuse method as well as study of static and dynamic knowledge capture method, the design template as a carrier for knowledge expression and reuse method is proposed. A typical instance for small agricultural machinery product is given to demonstrate the feasibility of the design approach.
Escherichia coli O127 group 4 capsule proteins assemble at the outer membrane

Enteropathogenic Escherichia coli O127 is encapsulated by a protective layer of polysaccharide made of the same strain specific O-antigen as the serotype lipopolysaccharide. Seven genes encoding capsule export functions comprise the group 4 capsule (gfc) operon. Genes gfcE, etk and etp encode homologs of the group 1 capsule secretion system but the upstream gfcABCD genes encode unknown functions specific to group 4 capsule export. We have developed an expression system for the large-scale production of the outer membrane protein GfcD. Contrary to annotations, we find that GfcD is a non-acylated integral membrane protein. Circular dichroism spectroscopy, light-scattering data, and the HHomp server suggested that GfcD is a monomeric β-barrel with 26 β-strands and an internal globular domain. We identified a set of novel protein-protein interactions between GfcB, GfcC, and GfcD, both in vivo and in vitro, and quantified the binding properties with isothermal calorimetry and biolayer interferometry. GfcC and GfcB form a high-affinity heterodimer with a KD near 100 nM. This heterodimer binds to GfcD (KD = 28 μM) significantly better than either GfcB or GfcC alone. These gfc proteins may form a complex at the outer membrane for group 4 capsule secretion or for a yet unknown function.