Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Monte Carlo dropout and bootstrap aggregation for uncertainty estimation in radiation therapy dose prediction with deep learning neural networks

Recently, artificial intelligence technologies and algorithms have become a major focus for advancements in treatment planning for radiation therapy. As these are starting to become incorporated into the clinical workflow, a major concern from clinicians is not whether the model is accurate, but whether the model can express to a human operator when it does not know if its answer is correct. We propose to use Monte Carlo dropout (MCDO) and the bootstrap aggregation (bagging) technique on deep learning models to produce uncertainty estimations for radiation therapy dose prediction. We show that both models are capable of generating a reasonable uncertainty map, and, with our proposed scaling technique, creating interpretable uncertainties and bounds on the prediction and any relevant metrics. Performance-wise, bagging provides statistically significant reduced loss value and errors in most of the metrics investigated in this study. The addition of bagging was able to further reduce errors by another 0.34% for Dmean and 0.19% for Dmax, on average, when compared to the baseline framework. Overall, the bagging framework provided significantly lower MAE of 2.62, as opposed to the baseline framework's MAE of 2.87. The usefulness of bagging, from solely a performance standpoint, does highly depend on the problem and the acceptable predictive error, and its high upfront computational cost during training should be factored in to deciding whether it is advantageous to use it. In terms of deployment with uncertainty estimations turned on, both frameworks offer the same performance time of about 12 seconds. As an ensemble-based metaheuristic, bagging can be used with existing machine learning architectures to improve stability and performance, and MCDO can be applied to any deep learning models that have dropout as part of their architecture.
Toward a Generalization Metric for Deep Generative Models

Measuring the generalization capacity of Deep Generative Models (DGMs) is difficult because of the curse of dimensionality. Evaluation metrics for DGMs such as Inception Score, Fr\'echet Inception Distance, Precision-Recall, and Neural Net Divergence try to estimate the distance between the generated distribution and the target distribution using a polynomial number of samples. These metrics are the target of researchers when designing new models. Despite the claims, it is still unclear how well can they measure the generalization capacity of a generative model. In this paper, we investigate the capacity of these metrics in measuring the generalization capacity. We introduce a framework for comparing the robustness of evaluation metrics. We show that better scores in these metrics do not imply better generalization. They can be fooled easily by a generator that memorizes a small subset of the training set. We propose a fix to the NND metric to make it more robust to noise in the generated data. Toward building a robust metric for generalization, we propose to apply the Minimum Description Length principle to the problem of evaluating DGMs. We develop an efficient method for estimating the complexity of Generative Latent Variable Models (GLVMs). Experimental results show that our metric can effectively detect training set memorization and distinguish GLVMs of different generalization capacities.
Modification de la surface luminale artérielle par films de polyelectrolytes en vue d’obtenir un substitut vasculaire non thrombogénique

L'augmentation de la frequence des pathologies cardiovasculaires ainsi que les limites des therapies disponibles, conduiront, ces prochaines decennies, a des besoins importants en substituts vasculaires, surtout de faibles calibres. L'objectif de ce travail est d'elaborer de nouvelles matrices naturelles vasculaires, repondant aux qualites necessaires pour le remplacement de vaisseaux endommages. Nous avons choisi de valoriser des arteres ombilicales humaines cryoconservees et desendothelialisees en les fonctionnalisant avec un film de polyelectrolytes. Ces films sont construits par depots alternes d'hydrochlorure de polyallylamine (polycation) et de poly (sodium-4-styrene sulfonate) (polyanion). L'evaluation des proprietes biomecaniques de la paroi du greffon apres depot du film montre la restauration de son elasticite perdue au cours de la congelation. Pour le developpement d'une surface luminale non thrombogene deux approches ont ete envisagees : 1)- Favoriser la formation d'un neo-endothelium vasculaire sur un film qui se termine par une couche positive, 2)- Empecher toute adhesion cellulaire sur la surface luminale chargee negativement. La caracterisation in vitro des arteres endothelialisees a montre que le film permettait le developpement d'un endothelium avec une morphologie proche de l'artere native. Nous observons egalement une meilleure resistance des cellules au cisaillement. L'implantation chez le lapin d'arteres non endothelialisees a montre une bonne fonctionnalite (permeabilite, resistance, structure.) des arteres apres trois mois d'implantation. Ces resultats encourageants permettent d'envisager une evaluation des substituts fonctionnalises endothelialises ou decellularises chez le gros animal.
Adversarial Learning on the Latent Space for Diverse Dialog Generation

Generating relevant responses in a dialog is challenging, and requires not only proper modeling of context in the conversation, but also being able to generate fluent sentences during inference. In this paper, we propose a two-step framework based on generative adversarial nets for generating conditioned responses. Our model first learns a meaningful representation of sentences by autoencoding, and then learns to map an input query to the response representation, which is in turn decoded as a response sentence. Both quantitative and qualitative evaluations show that our model generates more fluent, relevant, and diverse responses than existing state-of-the-art methods.
LETTER TO THE EDITOR: Electroabsorptive behaviour of semiconductor quantum dots in glass

The electroabsorptive properties of semiconductor quantum dots in glass are observed for macroscopic field strengths up to 8*105 V cm-1. The quadratic dependence of the induced absorption change on the applied field turns over to a linear dependence at the highest fields, and a tentative explanation for this behaviour is suggested.
Fast Database Joins and PSI for Secret Shared Data

We present a scalable protocol for database joins on secret shared data in the honest-majority three-party setting. The key features of our protocol are a rich set of SQL-like join/select queries and the ability to compose join operations together due to the inputs and outputs being generically secret shared between the parties. Provided that all joins operate on unique primary keys, no information is revealed to any party during the protocol. In particular, not even the sizes of intermediate joins are revealed. All of our protocols are constant-round and achieve O(n) communication and computation overhead for joining two tables of n rows. These properties make our protocol ideal for outsourced secure computation. In this setting several non-colluding servers are setup and the input data is shared among them. These servers then perform the relevant secret shared computation and output the result. This model has recently been gaining traction in industry, e.g. Facebook's Crypten, Cape Privacy's TFEncrypted, Mozilla Telemetry. We additionally implement two applications on top of our framework. The first application detects voter registration errors within and between agencies of 50 US states, in a privacy-preserving manner. The second application allows several organizations to compare network security logs to more accurately identify common security threats, e.g. the IP addresses of a bot net. In both cases, the practicality of these applications depends on efficiently performing joins on millions of secret shared records. For example, our three party protocol can perform a join on two sets of 1 million records in 4.9 seconds or, alternatively, compute the cardinality of this join in just 3.1 seconds.
"Pls Uninstall": On the Interplay of the COVID-19 Pandemic and Toxic Player Behavior in Competitive Gaming

Win or lose---the beauty of competitive games lies in the challenge, accompanied by the rush of adrenaline. Unfortunately, another loyal companion is toxic player behavior, which can ruin a game even before the winner is decided. Yet what happens when opponents suddenly face a much more dangerous, common enemy in the form of a pandemic outbreak? Does such a situation act as common ground, uniting the players and increasing their social awareness? Or do players abuse the game (and other players) even more to release their frustration caused by the pandemic? The results of our ongoing work support the latter hypothesis: in most competitive games, players perceive a notable increase of toxicity, which is an alarming sign and a clear call for more detailed explorations.
Curioscape: A Curiosity-driven Escape Room Board Game

Are you frustrated when a board game has too many rules? Do you want to jump straight into the game and just play? We created Curioscape, an escape room board game that focuses on the idea of whether eliminating a rule book is possible in a board game context. This means players can start the game without having to learn rules or understand how the game works. This paper describes Curioscape's conception to release, along with the exploration of replicating escape rooms in a smaller space and investigates if we can use curiosity to create meaningful game design choices.
Nocturnal Bees Feed on Diurnal Leftovers and Pay the Price of Day – Night Lifestyle Transition

Bees exemplify flights under bright sunlight. A few species across bee families have evolved nocturnality, displaying remarkable adaptations to overcome limitations of their daylight-suited apposition eyes. Phase inversion to nocturnality in a minority of bees that co-exist with diurnal bees provides a unique opportunity to study ecological benefits that mediate total temporal niche shifts. While floral traits and sensory modalities associated with the evolution of classical nocturnal pollination syndromes, e.g. by bats and moths, are well-studied, nocturnality in bees represents a poorly understood, recently invaded, extreme niche. To test the competitive release hypothesis, we examine how nocturnality shapes foraging by comparing pollen loads, nest pollen, and flower visitation of sympatric nocturnal and diurnal carpenter bees. We predicted that nocturnal bees primarily use night-blooming flowers, show little/no resource overlap with diurnal species and competitive release favors night-time pollen collection for provisioning. Contrarily, we found substantial resource overlap between nocturnal and diurnal bees. Flower opening times, floral longevity and plant abundance did not define nocturnal flower use. Smaller pollen loads on nocturnal foragers suggest subsistence on resource leftovers largely from diurnal flowers. Greater pollen types/diversity on nocturnal foragers indicate lower floral constancy compared to diurnal congenerics. Reduced activity during new moon compared to full moon suggests constraints to nocturnal foraging. Invasion and sustenance within the nocturnal niche is characterized by: (i) opportunistic foraging on residual resources as indicated by smaller pollen loads, extensive utilization of day-blooming flowers and substantial overlap with diurnal bees, (ii) generalization at two levels—between and within foraging trips as indicated by lower floral constancy, (iii) reduced foraging on darker nights, indicating visual constraints despite sensitive optics. This together with smaller populations and univoltine breeding in nocturnal compared to multivoltine diurnal counterparts suggest that nocturnality imposes substantial fitness costs. In conclusion, the evolution of nocturnality in bees is accompanied by resource generalization instead of specialization. Reduced floral constancy suggests differences in foraging strategies of nocturnal and diurnal bees which merits further investigation. The relative roles of competition, floral rewards and predators should be examined to fully understand the evolution and maintenance of nocturnality in bees.
Towards Attribution in Mobile Markets: Identifying Developer Account Polymorphism

Malicious developers may succeed at publishing their apps in mobile markets, including the official ones. If reported, the apps will be taken down and the developer accounts possibly be banned. Unfortunately, such take-downs do not prevent the attackers to use other developer accounts to publish variations of their malicious apps. This work presents a novel approach for identifying developer accounts, and other indicators of compromise (IOCs) in mobile markets, that belong to the same operation, i.e., to the same owners. Given a set of seed IOCs, our approach explores app and version metadata to identify new IOCs that belong to the same operation. It outputs an attribution graph, which details the attribution inferences, so that they can be reviewed. We have implemented our approach into Retriever, a tool that supports multiple mobile markets including the official GooglePlay and AppleStore. We have evaluated Retriever on 17 rogueware and adware operations. In 94% of the operations, Retriever discovers at least one previously unknown developer account. Furthermore, Retriever reveals that operations that look dead still have active developer accounts.
Perinatal Resilience for the First 1,000 Days of Life. Concept Analysis and Delphi Survey

Background The ability to cope with challenges and stress in life is generally understood as resilience. Pregnancy and parenthood are challenging times. The concept of resilience is receiving increasing interest from researchers, clinicians, and policy staff because of its potential impact on health, well-being, and quality of life. Nevertheless, the concept is less studied during the perinatal period. Objectives The aim of this study is to understand the concept of perinatal resilience, including the underlying processes and more specifically for the first 1,000 days of life. Methods A concept analysis according to the Walker and Avant (2011) framework was used, to investigate the basic elements of the concept. Concurrently, a two-round Delphi survey involving researchers, clinicians, epidemiologists, mothers, and fathers (N = 21), was conducted to prioritize the terms associated with perinatal resilience. Data collection took place between January and April 2019. Results Through concept analysis and Delphi survey, five defining attributes for perinatal resilience were identified: social support, self-efficacy, self-esteem, sense of mastery and personality. The additional terms, rated important by the Delphi survey, were linked to the consequences of being resilient during the perinatal period for the individual and his/her family. Specifically, highlighted were the experiences of families in personal growth and achieving family balance, adaptation, or acceptance. Conclusion Based on the results of the concept analysis and Delphi survey, we describe perinatal resilience for the first 1,000 days as a circular process towards a greater well-being in the form of personal growth, family balance, adaptation or acceptance, when faced with stressors, challenges or adversity during the perinatal period. The presence of resiliency attributes such as social support, sense of mastery, self-efficacy, and self-esteem enhance the capacity to be resilient and probably prevent mental health problems.
Transgenic Crops for Biofortification

Global food security concerns impact greatly on the United Nation's Sustainable Development Goals, which are heavily focused on eradicating hunger by 2030. The Global Food Security Index of 2019 has reported that 88% of countries claim their is enough food supply in their countries, but it is a dreadful reality that every one in three countries is facing insufficient availability of food supply as per the index, meaning more than 10% of the population is malnourished. Since nutrition is one of the main factors in maintaining a healthy lifestyle and meeting the requirements of food security, several national nutrition surveys conducted in various countries have provided an avenue for governments to assess malnutrition problems across the population. For example, the National Nutrition Survey carried out in 2011 in Pakistan indicated that more than 50% of the population was food insecure based on the nutritional status of available food. This survey also highlighted the acute deficiency of micronutrients in the diet resulting in several disorders, especially among the female population. In view of these facts, efforts are being made globally to enhance the nutritional value of our agricultural products, especially staple crops, by using several biotechnological approaches.
Infinite Walking in Three Dimensions in Virtual Reality: A Shopping Mall Simulator Game

In virtual environments, users can move almost infinitely while actually staying within a small area in real space. Several techniques such as Infinite Walking and Redirected Walking have already been developed for this purpose, but in order to overcome their respective limitations, several of them must be combined with visual effects and tricks to create the impression of a realistic environment and realistic movement. In this paper, our graduation class presents the prototype of a shopping mall simulator game realizing infinite motion in 3D on 639 m² within a 5 x 5 meter VR sensor range, even allowing the player to move freely on multiple floors of a beautifully designed shopping mall.
Effects of Space and Scenery on Virtual Pet-Assisted Activity

In recent years, 15% of the working population have suffered from mental disorders. Such conditions cause low productivity, absence of diseases, and unemployment. In addition, the treatment of mental disorders takes a long time, so routine precautions are essential. One of the ways to prevent mental disorders is to achieve healing through interaction with animals. However, this method has not been widely accepted because of the difficulties in handling animals. To solve these problem, we introduced a virtual pet through a virtual reality technology for three-dimensional stereoscopic visualization as an alternative to animals. We named the system that prevents mental disorders by interacting with the virtual pet as virtual pet-assisted activity (VAA) and verified that this system gained short-term psychological and physiological benefits. However, it was unclear what causes the system to have positive effects on users. This study investigated whether space factors affect the psychological and physiological aspects of the user and whether the scenery factor affected the psychological and physiological aspects of the user and its interactions with the virtual pet. The experiment was divided into two experiments for the space factor and scenery factor. As a result, it was not affected by the space factor but was affected by the scenery factor. The findings reveal that the open scenery is more effective than the closed scenery in the VAA system.
Alzheimer's Disease Classification With a Cascade Neural Network

Classification of Alzheimer's Disease (AD) has been becoming a hot issue along with the rapidly increasing number of patients. This task remains tremendously challenging due to the limited data and the difficulties in detecting mild cognitive impairment (MCI). Existing methods use gait [or EEG (electroencephalogram)] data only to tackle this task. Although the gait data acquisition procedure is cheap and simple, the methods relying on gait data often fail to detect the slight difference between MCI and AD. The methods that use EEG data can detect the difference more precisely, but collecting EEG data from both HC (health controls) and patients is very time-consuming. More critically, these methods often convert EEG records into the frequency domain and thus inevitably lose the spatial and temporal information, which is essential to capture the connectivity and synchronization among different brain regions. This paper proposes a cascade neural network with two steps to achieve a faster and more accurate AD classification by exploiting gait and EEG data simultaneously. In the first step, we propose attention-based spatial temporal graph convolutional networks to extract the features from the skeleton sequences (i.e., gait) captured by Kinect (a commonly used sensor) to distinguish between HC and patients. In the second step, we propose spatial temporal convolutional networks to fully exploit the spatial and temporal information of EEG data and classify the patients into MCI or AD eventually. We collect gait and EEG data from 35 cognitively health controls, 35 MCI, and 17 AD patients to evaluate our proposed method. Experimental results show that our method significantly outperforms other AD diagnosis methods (91.07 vs. 68.18%) in the three-way AD classification task (HC, MCI, and AD). Moreover, we empirically found that the lower body and right upper limb are more important for the early diagnosis of AD than other body parts. We believe this interesting finding can be helpful for clinical researches.
Control and Transfer of Chirality Within Well-Defined Tripodal Supramolecular Cages

The development of new strategies to turn achiral artificial hosts into highly desirable chiral receptors is a crucial challenge in order to advance the fields of asymmetric transformations and enantioselective sensing. Over the past few years, C3 symmetrical cages have emerged as interesting class of supramolecular hosts that have been reported as efficient scaffolds for chirality dynamics (such as generation, control, and transfer). On this basis, this mini review, which summarizes the existing examples of chirality control and propagation in tripodal supramolecular cages, aims at discussing the benefits and perspectives of this approach.
Bento: Bringing Network Function Virtualization to Tor

Tor is a powerful and important tool for providing anonymity and censorship resistance to users around the world. Yet it is surprisingly difficult to deploy new services in Tor---it is largely relegated to proxies and hidden services---or to nimbly react to new forms of attack. Conversely, "non-anonymous" Internet services are thriving like never before because of recent advances in programmable networks, such as Network Function Virtualization (NFV) which provides programmable in-network middleboxes. This work seeks to close this gap by introducing programmable middleboxes into the Tor network. In this architecture, users can install and run sophisticated "functions" on willing Tor routers,further improving anonymity, resilience to attack, performance of hidden services, and more. We present the design of an architecture, Bento, that protects middlebox nodes from the functions they run and protects the functions from the middleboxes they run on. Bento does not require modifications to Tor, and can run on the live Tor network. Additionally, we give an overview of how we can significantly extend the capabilities of Tor to meet users' anonymity needs and nimbly react to new threats.