Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantification under prior probability shift: the ratio estimator and its extensions

The quantification problem consists of determining the prevalence of a given label in a target population. However, one often has access to the labels in a sample from the training population but not in the target population. A common assumption in this situation is that of prior probability shift, that is, once the labels are known, the distribution of the features is the same in the training and target populations. In this paper, we derive a new lower bound for the risk of the quantification problem under the prior shift assumption. Complementing this lower bound, we present a new approximately minimax class of estimators, ratio estimators, which generalize several previous proposals in the literature. Using a weaker version of the prior shift assumption, which can be tested, we show that ratio estimators can be used to build confidence intervals for the quantification problem. We also extend the ratio estimator so that it can: (i) incorporate labels from the target population, when they are available and (ii) estimate how the prevalence of positive labels varies according to a function of certain covariates.
Poorer Search Efficiency in Healthy Young Adults With High Schizotypal Personality Traits

Perceptual organization (PO) difficulties have repeatedly been reported in people with schizophrenia, and in healthy individuals with high levels of schizotypy traits, who are at increased risk for schizophrenia. In particular, poor performance on the Embedded Figures Test (EFT) has been interpreted as an atypically strong preference for global over local processing, even though these processes cannot be clearly disambiguated on this test. Here we use two separate versions of the Radial Frequency Search Task (RFST), a new index of PO abilities, to selectively investigate global and local processing of shape information in trait schizotypy. Schizotypy traits were assessed using the Wisconsin Schizotypy Scales-Brief. Individuals selected for high and low levels of positive schizotypy [assessed with the Wisconsin Schizotypy Scales-Brief Perceptual Aberration (PAb) scale] completed the EFT, along with the Global RFST and the Local RFST, all of which require participants to find a target shape amongst distractor elements. The High PAb group (n = 83) were less efficient (i.e., reactions times slowed more as the set size increased) than the Low PAb group (n = 146) on the Global RFST (significant group differences for Target Absent conditions only), but not the Local RFST. High and Low PAb groups also differed on other schizotypy traits, so the specificity of the results to positive schizotypy cannot be assured. Unexpectedly, no group differences were observed on the EFT; however, there was a small, but significant, positive correlation between RFST search efficiency and EFT performance, indicating that they shared some common processes. Overall, the pattern of results suggests that global (but not local) processing difficulties may be contributing to the poorer perceptual organization observed in groups with high levels of schizotypy traits. In addition, the confinement of this result to the Target Absent condition suggests that the underlying mechanism involves differences in decisional processes on the RFST between high and low schizotypy groups. The RFST shows promise as a useful tool for measuring specific perceptual organization abilities in non-clinical, and potentially clinical, samples.
A Constrained Randomized Shortest-Paths Framework for Optimal Exploration

The present work extends the randomized shortest-paths framework (RSP), interpolating between shortest-path and random-walk routing in a network, in three directions. First, it shows how to deal with equality constraints on a subset of transition probabilities and develops a generic algorithm for solving this constrained RSP problem using Lagrangian duality. Second, it derives a surprisingly simple iterative procedure to compute the optimal, randomized, routing policy generalizing the previously developed "soft" Bellman-Ford algorithm. The resulting algorithm allows balancing exploitation and exploration in an optimal way by interpolating between a pure random behavior and the deterministic, optimal, policy (least-cost paths) while satisfying the constraints. Finally, the two algorithms are applied to Markov decision problems by considering the process as a constrained RSP on a bipartite state-action graph. In this context, the derived "soft" value iteration algorithm appears to be closely related to dynamic policy programming as well as Kullback-Leibler and path integral control, and similar to a recently introduced reinforcement learning exploration strategy. This shows that this strategy is optimal in the RSP sense - it minimizes expected path cost subject to relative entropy constraint. Simulation results on illustrative examples show that the model behaves as expected.
Visual Outcomes and Higher Order Aberrations Following LASIK on Eyes with Low Myopia and Astigmatism

Background: Laser-Assisted in situ Keratomileusis (LASIK) can induce corneal aberrations that can impact vision and patient satisfaction. Recent developments in laser technologies have helped minimise these aberrations. Objective: To assess the quality of vision and change in Higher-Order Aberrations (HOAs) following wavefront-optimized LASIK in low-myopic astigmatic patients. Methods: LASIK was performed on a total of 76 eyes in patients with myopia <4.0 D and cylinder <2.0 D using the WaveLight® EX500 excimer and FS200 femtosecond laser platform. Visual acuity, contrast sensitivity and HOAs were measured at 1 and 3 months postoperatively and compared to preoperative values. Subjective quality of vision was assessed pre- and postoperatively using a VF14 questionnaire. Results: Mean postoperative Spherical Equivalent (SE) was -0.09 ± 0.26 µm with 95% of patients within ± 0.5 D of attempted SE. Postoperative uncorrected distance visual acuity was 20/20 or better for 96% of patients. Contrast sensitivity increased against horizontal and vertical gratings at all spatial frequencies except for vertical gratings at 18 cycles/degree. Spherical aberration and total HOA increased by 0.085 µm and 0.13 µm respectively. The mean VF14 score increased from 89.2 ± 16.7% to 99 ± 1.4% postoperatively. Conclusion: LASIK performed using the WaveLight® EX500 excimer and WaveLight® FS200 laser platform provided improved contrast sensitivity and visual acuity with minimal introduction of HOAs, making it a suitable platform for low myopic astigmatic patients.
Prioritizing Browser Environments for Web Application Test Execution

When testing client-side web applications, it is important to consider different web-browser environments. Different properties of these environments such as web-browser types and underlying platforms may cause a web application to exhibit different types of failures. As web applications evolve, they must be regression tested across these different environments. Because there are many environments to consider this process can be expensive, resulting in delayed feedback about failures in applications. In this work, we propose six techniques for providing a developer with faster feedback on failures when regression testing web applications across different web-browser environments. Our techniques draw on methods used in test case prioritization; however, in our case we prioritize web-browser environments, based on information on recent and frequent failures. We evaluated our approach using four non-trivial and popular open-source web applications. Our results show that our techniques outperform two baseline methods, namely, no ordering and random ordering, in terms of the cost-effectiveness. The improvement rates ranged from -12.24% to 39.05% for no ordering, and from -0.04% to 45.85% for random ordering.
AROMA

Human activity recognition (HAR) is a promising research issue in ubiquitous and wearable computing. However, there are some problems existing in traditional methods: 1) They treat HAR as a single label classification task, and ignore the information from other related tasks, which is helpful for the original task. 2) They need to predesign features artificially, which are heuristic and not tightly related to HAR task. To address these problems, we propose AROMA (human activity recognition using deep multi-task learning). Human activities can be divided into simple and complex activities. They are closely linked. Simple and complex activity recognitions are two related tasks in AROMA. For simple activity recognition task, AROMA utilizes a convolutional neural network (CNN) to extract deep features, which are task dependent and non-handcrafted. For complex activity recognition task, AROMA applies a long short-term memory (LSTM) network to learn the temporal context of activity data. In addition, there is a shared structure between the two tasks, and the object functions of these two tasks are optimized jointly. We evaluate AROMA on two public datasets, and the experimental results show that AROMA is able to yield a competitive performance in both simple and complex activity recognitions.
Capacity of Two-Way Channels With Symmetry Properties

In this paper, we make use of channel symmetry properties to determine the capacity region of three types of two-way networks: 1) two-user memoryless two-way channels (TWCs); 2) two-user TWCs with memory; and 3) three-user multiaccess/degraded broadcast (MA/DB) TWCs. For each network, symmetry conditions under which a Shannon-type random coding inner bound (under independent non-adaptive inputs) is tight are given. For two-user memoryless TWCs, prior results are substantially generalized by viewing a TWC as two interacting state-dependent one-way channels. The capacity of symmetric TWCs with memory, whose outputs are functions of the inputs and independent stationary and ergodic noise processes, is also obtained. Moreover, various channel symmetry properties under which the Shannon-type inner bound is tight are identified for three-user MA/DB TWCs. The results not only enlarge the class of symmetric TWCs whose capacity region can be exactly determined but also imply that interactive adaptive coding, not improving capacity, is unnecessary for such channels.
Study of community-living Alzheimer’s patients’ adherence to the Mediterranean diet and risks of malnutrition at different disease stages

Background Alzheimer’s disease (AD) is a neurodegenerative disease that is characterized by deficits in episodic memory. It is the most common form of dementia and affects 50–70% of patients with cognitive impairments over the age of 65. Elderly people are particularly susceptible to malnutrition and that risk is even higher in patients with AD. This study assessed the nutritional status of patients with AD at different stages of AD and explored how that status correlated with cognitive, functional and behavioural variables and caregiver overburden. The characteristics of the diet and the degree of adherence to the Mediterranean diet were also analysed. Methods This was a cross-sectional study that was representative of the general population and it was carried out in the Unit of Cognitive Disorders, Lleida, Spain. The participants were 111 subjects with AD who were aged 65 and over and still living at home. The subjects’ nutritional status was assessed using the Mini Nutritional Assessment (MNA) and Controlling Nutritional Status system. The monthly food intake was estimated using the short Food Frequency Questionnaire and adherence to the Mediterranean diet was evaluated using the Mediterranean Diet Score. The Mini Mental State Examination (MMSE), Global Deterioration Scale, Neuropsychiatric Inventory (NPI) and Zarit Burden Interview (ZBI) were also used. Results We found that 68% of the subjects presented with a risk of malnutrition and 19% were malnourished according to the MNA scale. Patients ate a higher quantity of meat and dairy products than recommended and fewer products from the remaining healthier food groups. Of the 111 subjects, 73% showed low adherence to the Mediterranean diet and 27% showed moderate adherence. There was a partial correlation between nutritional status and the MMSE when the data were adjusted for age and sex (r = 0, 318; p = 0.001) and inverse correlations were found for functional status (r = − 0.283; p = 0.004) and the NPI (r = − 0.409; p = 0.000) and ZBI scales (r = − 0.433; p = 0.000) when they were adjusted using the same variables. The ZBI scale (OR 1.08, 95% CI [1.01–1.15]) showed an increase in the risk of malnutrition in the multivariate analysis. Discussion Alterations in nutritional status were more common during the advanced stages of AD and were also associated with behavioural changes and caregiver overburden. Low adherence to traditional healthy diets in Mediterranean countries and food intake profiles should be considered when managing patients with AD. Other countries can use the results to examine diets in people with AD that are high in meat and dairy and low in healthy food groups like fruit and vegetables.
Measuring abstract reasoning in neural networks

Whether neural networks can learn abstract reasoning or whether they merely rely on superficial statistics is a topic of recent debate. Here, we propose a dataset and challenge designed to probe abstract reasoning, inspired by a well-known human IQ test. To succeed at this challenge, models must cope with various generalisation `regimes' in which the training and test data differ in clearly-defined ways. We show that popular models such as ResNets perform poorly, even when the training and test sets differ only minimally, and we present a novel architecture, with a structure designed to encourage reasoning, that does significantly better. When we vary the way in which the test questions and training data differ, we find that our model is notably proficient at certain forms of generalisation, but notably weak at others. We further show that the model's ability to generalise improves markedly if it is trained to predict symbolic explanations for its answers. Altogether, we introduce and explore ways to both measure and induce stronger abstract reasoning in neural networks. Our freely-available dataset should motivate further progress in this direction.
Manifold regularization with GANs for semi-supervised learning

Generative Adversarial Networks are powerful generative models that are able to model the manifold of natural images. We leverage this property to perform manifold regularization by approximating a variant of the Laplacian norm using a Monte Carlo approximation that is easily computed with the GAN. When incorporated into the semi-supervised feature-matching GAN we achieve state-of-the-art results for GAN-based semi-supervised learning on CIFAR-10 and SVHN benchmarks, with a method that is significantly easier to implement than competing methods. We also find that manifold regularization improves the quality of generated images, and is affected by the quality of the GAN used to approximate the regularizer.
BikeGPS: Accurate Localization of Shared Bikes in Street Canyons via Low-Level GPS Cooperation

The past few years have seen a surge of stationless bike sharing services in many modern cities. The service allows the bikes to be dropped off freely, and to be found through GPS localization. For maximum convenience, the bikes are often parked in close proximity to the buildings, where GPS may perform poorly, making bike search a challenging task. This paper proposes a novel approach to addressing this problem. Inspired by multi-antenna systems, our method tries to collect GPS signals from multiple distributed bikes, by organizing a group of bikes into a network, called the BikeGPS. Formed by pedestrian users who opportunistically measure interbike distance via radio sensing and step tracking, the generated network permits one to map all the nodes' satellite range measurements into a single lead node's view. By considering both signal and geometry properties of satellite raw measurements, and using an asynchronous coarse time navigation algorithm, the lead node can accurately derive the locations of all the network nodes. Real-world experiments show that BikeGPS significantly improves the localization performance, in terms of both accuracy and solution availability, compared with the naive GPS approach and a high-level cooperative localization method.
Effects of different primers on indirect orthodontic bonding: Shear bond strength, color change, and enamel roughness

Objective We aimed to perform in-vitro evaluation to compare 1) shear bond strength (SBS), adhesive remnant index (ARI), and color change between self-etched and acid-etched primers; 2) the SBS, ARI and color change between direct and indirect bonding; and 3) the enamel roughness (ER) between 12-blade bur and aluminum oxide polisher debonding methods. Methods Seventy bovine incisors were distributed in seven groups: control (no bonding), direct (DTBX), and 5 indirect bonding (ITBX, IZ350, ISONDHI, ISEP, and ITBXp). Transbond XT Primer was used in the DTBX, ITBX, and ITBXp groups, flow resin Z350 in the IZ350 group, Sondhi in the ISONDHI group, and SEP primer in the ISEP group. SBS, ARI, and ER were evaluated. The adhesive remnant was removed using a low-speed tungsten bur in all groups except the ITBXp, in which an aluminum oxide polisher was used. After coffee staining, color evaluations were performed using a spectrophotometer immediately after staining and prior to bonding. Results ISONDHI and ISEP showed significantly lower SBS (p < 0.01). DTBX had a greater number of teeth with all the adhesive on the enamel (70%), compared with the indirect bonding groups (0–30%). The ER in the ITBX and ITBXp groups was found to be greater because of both clean-up techniques used. Conclusions Direct and indirect bonding have similar results and all the primers used show satisfactory adhesion strength. Use of burs and polishers increases the ER, but polishers ensure greater integrity of the initial roughness. Resin tags do not change the color of the teeth.
A prospective “test‐and‐treat” demonstration project among people who inject drugs in Vietnam

Modelling suggests that early diagnosis and immediate antiretroviral therapy (ART) among key populations would have a substantial impact in reducing HIV transmission and mortality in Vietnam. An implementation research project of “test‐and‐treat” among people who inject drugs (PWID) was developed to inform effective roll‐out of such interventions.
List of Figures and Tables

1. Macaulay Land Use Research Institute, Craigiebuckler, Aberdeen, AB15 8QH 2. Penny Anderson Associates Ltd., Park Lea, 60 Park Road, Buxton, Derbyshire, SK17 6SN 3. Geography, School of Social and Environmental Sciences, University of Dundee, Perth Road, Dundee, DD1 4HN 4. University of Plymouth, A504 Portland Square, Drake Circus, Plymouth, Devon, PL4 8AA 5. Applied Vegetation Dynamics Laboratory, University of Liverpool, Liverpool, L69 3GP
Leveraging linked entities to estimate focus time of short texts

Time is a useful dimension to explore in text databases especially when historical and factual information is concerned. As documents generally refer to different events and time periods, understanding the focus time of key sentences, defined as the time the content refers to, is a crucial task to temporally annotate a document. In this paper, we leverage a bag of linked entities representation of sentences and temporal information from Wikipedia and DBpedia to implement a novel approach to focus time estimation. We evaluate our approach on sample datasets and compare it with a state of the art method, measuring improvements in MRR.
Efficient keyword spotting using time delay neural networks

This paper describes a novel method of live keyword spotting using a two-stage time delay neural network. The model is trained using transfer learning: initial training with phone targets from a large speech corpus is followed by training with keyword targets from a smaller data set. The accuracy of the system is evaluated on two separate tasks. The first is the freely available Google Speech Commands dataset. The second is an in-house task specifically developed for keyword spotting. The results show significant improvements in false accept and false reject rates in both clean and noisy environments when compared with previously known techniques. Furthermore, we investigate various techniques to reduce computation in terms of multiplications per second of audio. Compared to recently published work, the proposed system provides up to 89% savings on computational complexity.
A Security Index for Actuators Based on Perfect Undetectability: Properties and Approximation

A novel security index based on the definition of perfect undetectability is proposed. The index is a tool that can help a control system operator to localize the most vulnerable actuators in the network. In particular, the security index of actuator i represents the minimal number of sensors and actuators that needs to be compromised in addition to i, such that a perfectly undetectable attack is possible. A method for computing this index for small scale systems is derived, and difficulties with the index once the system is of large scale are outlined. An upper bound for the index that overcomes these difficulties is then proposed. The theoretical developments are illustrated on a numerical example.
Predicting Aging of Brain Metabolic Topography Using Variational Autoencoder

Predicting future brain topography can give insight into neural correlates of aging and neurodegeneration. Due to variability in aging process, it has been challenging to precisely estimate brain topographical change according to aging. Here, we predict age-related brain metabolic change by generating future brain 18F-Fluorodeoxyglucose PET. A cross-sectional PET dataset of cognitively normal subjects with different age was used to develop a generative model. The model generated PET images using age information and characteristic individual features. Predicted regional metabolic changes were correlated with the real changes obtained by follow-up data. This model was applied to produce a brain metabolism aging movie by generating PET at different ages. Normal population distribution of brain metabolic topography at each age was estimated as well. In addition, a generative model using APOE4 status as well as age as inputs revealed a significant effect of APOE4 status on age-related metabolic changes particularly in the calcarine, lingual cortex, hippocampus and amygdala. It suggested APOE4 could be a factor affecting individual variability in age-related metabolic degeneration in normal elderly. This predictive model may not only be extended to understanding cognitive aging process, but apply to development of a preclinical biomarker for various brain disorders.