Miracle-dz/UniFAR-FTUs huggingface dataset:
‘It's a double whammy’: A qualitative study of illness uncertainty in individuals with Parkinson's disease in the context of COVID-19

Objectives The purpose of this study was to explore the experiences of individuals with Parkinson's through the theoretical lens of illness uncertainty during the first UK full lockdown period (March–June 2020) put in place due outbreak of the COVID-19 pandemic. Methods Individual semi-structured interviews were carried out via telephone in May 2020 with 10 individuals with Parkinson's (six men and four women) recruited from Parkinson's UK. Interviews were recorded and transcribed verbatim, and thematic analysis was adopted to analyse the resulting data. Results Four overarching themes emerged from the interview data: (1) COVID-19 amplifying existing fears and difficulties around the uncertainty of Parkinson's; (2) practical and psychological efforts to manage uncertainty; (3) benefit-finding as a way of acknowledging the positives of lockdown; (4) risk and future management in the context of uncertainty. Discussion Participants reported a range of implicit and explicit strategies to cope with the ‘double whammy’ of uncertainty caused by having Parkinson's during a global pandemic. While these were generally successful in maintaining well-being, it is important that such successful accounts are used to help inform novel strategies and interventions targeting individuals who might need additional support.
Targeted Ablation of Primary Cilia in Differentiated Dopaminergic Neurons Reduces Striatal Dopamine and Responsiveness to Metabolic Stress

Primary cilia (PC) are microtubule-based protrusions of the cell membrane transducing molecular signals during brain development. Here, we report that PC are required for maintenance of Substantia nigra (SN) dopaminergic (DA) neurons highly vulnerable in Parkinson’s disease (PD). Targeted blockage of ciliogenesis in differentiated DA neurons impaired striato-nigral integrity in adult mice. The relative number of SN DA neurons displaying a typical auto-inhibition of spontaneous activity in response to dopamine was elevated under control metabolic conditions, but not under metabolic stress. Strikingly, in the absence of PC, the remaining SN DA neurons were less vulnerable to the PD neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridin (MPTP). Our data indicate conserved PC-dependent neuroadaptive responses to DA lesions in the striatum. Moreover, PC control the integrity and dopamine response of a subtype of SN DA neurons. These results reinforce the critical role of PC as sensors of metabolic stress in PD and other disorders of the dopamine system.
Relationship between Body and Otolith Morphological Characteristics of Sabre Squirrelfish (Sargocentron spiniferum) from the Southern Red Sea: Difference between Right and Left Otoliths

Otolith morphology analysis is one of the main tools used for fish or fish stock identification. Moreover, otolith shape can also be used in animal dietary studies (stomach content) for the identification of prey fishes and their size according to the relationship between fish and otolith sizes. In the present study, the relationship between fish length and otolith morphological dimensions was investigated for the sabre squirrelfish, Sargocentron spiniferum (Forsskål, 1775) (family: Holocentridae). Samples of 185 fish were collected from the coast of the Red Sea, Egypt. To analyze the relationship between fish and otolith, otolith morphometric measurements (length, width, area, perimeter, weight, sulcus, and ostium) and shape factors (aspect ratio, compactness, form factor, rectangularity, roundness, ellipticity, squareness) describing the outline shape were extracted using image analysis. Generalized linear models were applied for the relationship between body length and each otolith morphology feature. From the relationships between the total length of fish and fourteen morphology features, only otolith length, caudal length, and squareness were significantly correlated with fish size. Our results provide more information for the relationship between fish length and otolith morphometric features.
A Qualitative Analysis of Female Breadwinner Representations in the Media by

Violating social norms often elicits a negative public response aimed at punishing norm violators and dissuading others from following their lead. One way to understand reactions to norm violations is through the moral panic framework. This framework identifies the reactions that an emergent norm-violating social group is likely to experience. These reactions are characterized as overexaggerated, often blaming the group for the consequences of their norm violation and suggesting that the public should fear the group. I hypothesized that social attitudes towards female breadwinner relationships (FBRs), which violate heteronormative gender norms, would reflect these, and other, moral panic characteristics. However, I expected that these characteristics would be less extreme, and thus I proposed that social reactions to FBRs would reflect a kind of moral anxiety. To test this hypothesis, I conducted a qualitative thematic analysis of 94 magazine and newspaper articles concerning FBRs, and five themes emerged. First, the articles emphasized the social change that these relationships represent. Second, gender role expectations were often discussed, with many more articles reinforcing traditional gender role expectations than non-traditional gender role expectations. Third, most articles described costs of FBRs while only a minority described benefits, and most costs concerned the couple as a whole instead of either partner individually. Fourth, societal reactions to this change were often described, and most reactions were negative. And fifth, many articles offered tips for FBRs, often focused on overcoming the costs associated with such bonds. This pattern of reactions to FBRs ultimately demonstrated many of the characteristics of moral anxiety. Overall, these media articles portrayed FBRs as undesirable relationships involving stressed women and emasculated men: a stigmatizing portrayal that could dissuade others from pursuing such relationships.
AutoPilot: Automating SoC Design Space Exploration for SWaP Constrained Autonomous UAVs

Building domain-specific accelerators for autonomous unmanned aerial vehicles (UAVs) is challenging due to a lack of systematic methodology for designing onboard compute. Balancing a computing system for a UAV requires considering both the cyber (e.g., sensor rate, compute performance) and physical (e.g., payload weight) characteristics that affect overall performance. Iterating over the many component choices results in a combinatorial explosion of the number of possible combinations: from 10s of thousands to billions, depending on implementation details. Manually selecting combinations of these components is tedious and expensive. To navigate the {cyber-physical design space} efficiently, we introduce \emph{AutoPilot}, a framework that automates full-system UAV co-design. AutoPilot uses Bayesian optimization to navigate a large design space and automatically select a combination of autonomy algorithm and hardware accelerator while considering the cross-product effect of other cyber and physical UAV components. We show that the AutoPilot methodology consistently outperforms general-purpose hardware selections like Xavier NX and Jetson TX2, as well as dedicated hardware accelerators built for autonomous UAVs, across a range of representative scenarios (three different UAV types and three deployment environments). Designs generated by AutoPilot increase the number of missions on average by up to 2.25x, 1.62x, and 1.43x for nano, micro, and mini-UAVs respectively over baselines. Our work demonstrates the need for holistic full-UAV co-design to achieve maximum overall UAV performance and the need for automated flows to simplify the design process for autonomous cyber-physical systems.
Robust Designs for Prospective Randomized Trials Surveying Sensitive Topics.

We consider the problem of designing a prospective randomized trial in which the outcome data will be self-reported, and will involve sensitive topics. Our interest is in how a researcher can adequately power her study when some respondents misreport the binary outcome of interest. To correct the power calculations, we first obtain expressions for the bias and variance induced by misreporting. We model the problem by assuming each individual in our study is a member of one "reporting class": aTrue-reporter, False-reporter, Never-reporter, or Always-reporter. We show that the joint distribution of reporting classes and "response classes" (characterizing individuals' response to the treatment) will exactly define the error terms for our causalestimate. We propose a novel procedure for determining adequate sample sizes under the worst-case power corresponding to a given level of misreporting. Our problem is motivated by prior experience implementing a randomized controlled trial of a sexual violence prevention program among adolescent girls in Kenya.
Nuclear magnetic resonance measurements in dynamically controlled field pulse.

We present the architecture of the versatile nuclear magnetic resonance (NMR) spectrometer with software-defined radio technology and its application to the dynamically controlled pulsed magnetic fields. The pulse-field technology is the only solution to access magnetic fields greater than 50 T, but the NMR experiment in the pulsed magnetic field was difficult because of the continuously changing field strength. The dynamically controlled field pulse allows us to perform NMR experiment in a quasi-steady field condition by creating a constant magnetic field for a short time around the peak of the field pulse. We confirmed the reproducibility of the field pulses using the NMR spectroscopy as a high precision magnetometer. With the highly reproducible field strength, we succeeded in measuring the nuclear spin-lattice relaxation rate 1/T1, which had never been measured by the pulse-field NMR experiment without dynamic field control. We also implement the NMR spectrum measurement with both the frequency-sweep and field-sweep modes and discuss the appropriate choices of these modes depending on the magnetic properties of the sample to be measured. This development, with further improvement at a long-duration field pulse, will innovate the microscopic measurement in extremely high magnetic fields.
Spin-to-Charge Conversion with Electrode Confinement in Diamond

The nitrogen-vacancy (NV) center in diamond has a wide range of potential applications in quantum metrology, communications and computation. The key to its use lies in how large the optical spin contrast is and the associated fidelity of spin state readout. In this paper we propose a new mechanism for improving contrast with a spin-to-charge protocol that relies on the use of an external electrode and cryogenic temperatures to discretize the diamond conduction band for spin-selective resonant photoionization. We use effective mass theory to calculate the discrete eigenenergies in this new system and use them to formulate a new spin-to-charge protocol that involves resonant photoionization out the NV ground state into the diamond conduction band. The major sources of broadening are also addressed which guide the design of the experiment. With this mechanism we theorise an optical spin contrast that and an associated spin readout fidelity of 85%. This significant improvement can be applied to a number of cryogenic quantum technologies.
Knowledge Distillation from Ensemble of Offsets for Head Pose Estimation

—This paper proposes a method for estimating the head pose from a single image. This estimation uses a neural network (NN) obtained in two stages. In the ﬁrst stage, we trained the base NN, which has one regression head and four regression via classiﬁcation (RvC) heads. We build the ensemble of offsets using small offsets of face bounding boxes. In the second stage, we perform knowledge distillation (KD) from the ensemble of offsets of the base NN into the ﬁnal NN with one RvC head. On the main test protocol, the use of the offset ensemble improves the results of the base NN, and the KD improves the results from the offset ensemble. The KD improves the results by an average of 7.7% compared to the non-ensemble version. The proposed NN on the main test protocol improves the state-of-the-art result on AFLW2000 and approaches, with only a minimal gap, the state-of-the-art result on BIWI. Our NN uses only head pose data, but the previous state-of-the-art model also uses facial landmarks during training. We have made publicly available trained NNs and face bounding boxes for the 300W-LP, AFLW, AFLW2000, and BIWI datasets. KD-ResNet152 has the best results, and KD-ResNet18 has a better result on the AFLW2000 dataset than any previous method.
Efficient Online Estimation of Causal Effects by Deciding What to Observe

Researchers often face data fusion problems, where multiple data sources are available, each capturing a distinct subset of variables. While problem formulations typically take the data as given, in practice, data acquisition can be an ongoing process. In this paper, we aim to estimate any functional of a probabilistic model (e.g., a causal effect) as efficiently as possible, by deciding, at each time, which data source to query. We propose online moment selection (OMS), a framework in which structural assumptions are encoded as moment conditions. The optimal action at each step depends, in part, on the very moments that identify the functional of interest. Our algorithms balance exploration with choosing the best action as suggested by current estimates of the moments. We propose two selection strategies: (1) explore-then-commit (OMS-ETC) and (2) explore-then-greedy (OMS-ETG), proving that both achieve zero asymptotic regret as assessed by MSE. We instantiate our setup for average treatment effect estimation, where structural assumptions are given by a causal graph and data sources may include subsets of mediators, confounders, and instrumental variables.
Optimal Order Simple Regret for Gaussian Process Bandits

Consider the sequential optimization of a continuous, possibly non-convex, and expensive to evaluate objective function $f$. The problem can be cast as a Gaussian Process (GP) bandit where $f$ lives in a reproducing kernel Hilbert space (RKHS). The state of the art analysis of several learning algorithms shows a significant gap between the lower and upper bounds on the simple regret performance. When $N$ is the number of exploration trials and $\gamma_N$ is the maximal information gain, we prove an $\tilde{\mathcal{O}}(\sqrt{\gamma_N/N})$ bound on the simple regret performance of a pure exploration algorithm that is significantly tighter than the existing bounds. We show that this bound is order optimal up to logarithmic factors for the cases where a lower bound on regret is known. To establish these results, we prove novel and sharp confidence intervals for GP models applicable to RKHS elements which may be of broader interest.
Single Image Defocus Deblurring Using Kernel-Sharing Parallel Atrous Convolutions

This paper proposes a novel deep learning approach for single image defocus deblurring based on inverse kernels. In a defocused image, the blur shapes are similar among pixels although the blur sizes can spatially vary. To utilize the property with inverse kernels, we exploit the observation that when only the size of a defocus blur changes while keeping the shape, the shape of the corresponding inverse kernel remains the same and only the scale changes. Based on the observation, we propose a kernel-sharing parallel atrous convolutional (KPAC) block specifically designed by incorporating the property of inverse kernels for single image defocus deblurring. To effectively simulate the invariant shapes of inverse kernels with different scales, KPAC shares the same convolutional weights among multiple atrous convolution layers. To efficiently simulate the varying scales of inverse kernels, KPAC consists of only a few atrous convolution layers with different dilations and learns per-pixel scale attentions to aggregate the outputs of the layers. KPAC also utilizes the shape attention to combine the outputs of multiple convolution filters in each atrous convolution layer, to deal with defocus blur with a slightly varying shape. We demonstrate that our approach achieves state-of-the-art performance with a much smaller number of parameters than previous methods.
Straggler-Robust Distributed Optimization in Parameter-Server Networks

Optimization in distributed networks plays a central role in almost all distributed machine learning problems. In principle, the use of distributed task allocation has reduced the computational time, allowing better response rates and higher data reliability. However, for these computational algorithms to run effectively in complex distributed systems, the algorithms ought to compensate for communication asynchrony, network node failures and delays known as stragglers. These issues can change the effective connection topology of the network, which may vary over time, thus hindering the optimization process. In this paper, we propose a new distributed unconstrained optimization algorithm for minimizing a convex function which is adaptable to a parameter server network. In particular, the network worker nodes solve their local optimization problems, allowing the computation of their local coded gradients, which will be sent to different server nodes. Then within this parameter server platform each server node aggregates its communicated local gradients, allowing convergence to the desired optimizer. This algorithm is robust to network s worker node failures, disconnection, or delaying nodes known as stragglers. One way to overcome the straggler problem is to allow coding over the network. We further extend this coding framework to enhance the convergence of the proposed algorithm under such varying network topologies. By using coding and utilizing evaluations of gradients of uniformly bounded delay we further enhance the proposed algorithm performance. Finally, we implement the proposed scheme in MATLAB and provide comparative results demonstrating the effectiveness of the proposed framework
Practical and Fast Momentum-Based Power Methods

The power method is a classical algorithm with broad applications in machine learning tasks, including streaming PCA, spectral clustering, and low-rank matrix approximation. The distilled purpose of the vanilla power method is to determine the largest eigenvalue (in absolute modulus) and its eigenvector of a matrix. A momentum-based scheme can be used to accelerate the power method, but achieving an optimal convergence rate with existing algorithms critically relies on additional spectral information that is unavailable at run-time, and sub-optimal initializations can result in divergence. In this paper, we provide a pair of novel momentum-based power methods, which we call the delayed momentum power method (DMPower) and a streaming variant, the delayed momentum streaming method (DMStream). Our methods leverage inexact deflation and are capable of achieving near-optimal convergence with far less restrictive hyperparameter requirements. We provide convergence analyses for both algorithms through the lens of perturbation theory. Further, we experimentally demonstrate that DMPower routinely outperforms the vanilla power method and that both algorithms match the convergence speed of an oracle running existing accelerated methods with perfect spectral knowledge.
Plug and Play, Model-Based Reinforcement Learning

Sample-efficient generalisation of reinforcement learning approaches have always been a challenge, especially, for complex scenes with many components. In this work, we introduce Plug and Play Markov Decision Processes, an object-based representation that allows zero-shot integration of new objects from known object classes. This is achieved by representing the global transition dynamics as a union of local transition functions, each with respect to one active object in the scene. Transition dynamics from an object class can be pre-learnt and thus would be ready to use in a new environment. Each active object is also endowed with its reward function. Since there is no central reward function, addition or removal of objects can be handled efficiently by only updating the reward functions of objects involved. A new transfer learning mechanism is also proposed to adapt reward function in such cases. Experiments show that our representation can achieve sample-efficiency in a variety of set-ups.
Combination of Transfer Learning, Recursive Learning and Ensemble Learning for Multi-Day Ahead COVID-19 Cases Prediction in India using Gated Recurrent Unit Networks

The current COVID-19 pandemic has put a huge challenge on the Indian health infrastructure. With more and more people getting affected during the second wave, the hospitals were over-burdened, running out of supplies and oxygen. In this scenario, prediction of the number of COVID-19 cases beforehand might have helped in the better utilization of limited resources and supplies. This manuscript deals with the prediction of new COVID-19 cases, new deaths and total active cases for multiple days in advance. The proposed method uses gated recurrent unit networks as the main predicting model. A study is conducted by building four models that are pre-trained on the data from four different countries (United States of America, Brazil, Spain and Bangladesh) and are fine-tuned or retrained on India's data. Since the four countries chosen have experienced different types of infection curves, the pre-training provides a transfer learning to the models incorporating diverse situations into account. Each of the four models then give a multiple days ahead predictions using recursive learning method for the Indian test data. The final prediction comes from an ensemble of the predictions of the combination of different models. This method with two countries, Spain and Brazil, is seen to achieve the best performance amongst all the combinations as well as compared to other traditional regression models.
Signal Injection Attacks against CCD Image Sensors

Since cameras have become a crucial part in many safety-critical systems and applications, such as autonomous vehicles and surveillance, a large body of academic and non-academic work has shown attacks against their main component --- the image sensor. However, these attacks are limited to coarse-grained and often suspicious injections because light is used as an attack vector. Furthermore, due to the nature of optical attacks, they require the line-of-sight between the adversary and the target camera. In this paper, we present a novel post-transducer signal injection attack against CCD image sensors, as they are used in professional, scientific, and even military settings. We show how electromagnetic emanation can be used to manipulate the image information captured by a CCD image sensor with the granularity down to the brightness of individual pixels. We study the feasibility of our attack and then demonstrate its effects in the scenario of automatic barcode scanning. Our results indicate that the injected distortion can disrupt automated vision-based intelligent systems.