Miracle-dz/UniFAR-FTUs huggingface dataset:
Involvement of the Choroid Plexus in the Pathogenesis of Niemann-Pick Disease Type C

Niemann-Pick type C (NPC) disease, sometimes called childhood Alzheimer’s, is a rare neurovisceral lipid storage disease with progressive neurodegeneration leading to premature death. The disease is caused by loss-of-function mutations in the Npc1 or Npc2 gene which both result into lipid accumulation in the late endosomes and lysosomes. Since the disease presents with a broad heterogenous clinical spectrum, the involved disease mechanisms are still incompletely understood and this hampers finding an effective treatment. As NPC patients, who carry NPC1 mutations, have shown to share several pathological features with Alzheimer’s disease (AD) and we and others have previously shown that AD is associated with a dysfunctionality of the blood-cerebrospinal fluid (CSF) barrier located at choroid plexus, we investigated the functionality of this latter barrier in NPC1 pathology. Using NPC1–/– mice, we show that despite an increase in inflammatory gene expression in choroid plexus epithelial (CPE) cells, the blood-CSF barrier integrity is not dramatically affected. Interestingly, we did observe a massive increase in autophagosomes in CPE cells and enlarged extracellular vesicles (EVs) in CSF upon NPC1 pathology. Additionally, we revealed that these EVs exert toxic effects on brain tissue, in vitro as well as in vivo. Moreover, we observed that EVs derived from the supernatant of NPC1–/– choroid plexus explants are able to induce typical brain pathology characteristics of NPC1–/–, more specifically microgliosis and astrogliosis. Taken together, our data reveal for the first time that the choroid plexus and CSF EVs might play a role in the brain-related pathogenesis of NPC1.
Cross-Talks Between Macro- and Micronutrient Uptake and Signaling in Plants

In nature, land plants as sessile organisms are faced with multiple nutrient stresses that often occur simultaneously in soil. Nitrogen (N), phosphorus (P), sulfur (S), zinc (Zn), and iron (Fe) are five of the essential nutrients that affect plant growth and health. Although these minerals are relatively inaccessible to plants due to their low solubility and relative immobilization, plants have adopted coping mechanisms for survival under multiple nutrient stress conditions. The double interactions between N, Pi, S, Zn, and Fe have long been recognized in plants at the physiological level. However, the molecular mechanisms and signaling pathways underlying these cross-talks in plants remain poorly understood. This review preliminarily examined recent progress and current knowledge of the biochemical and physiological interactions between macro- and micro-mineral nutrients in plants and aimed to focus on the cross-talks between N, Pi, S, Zn, and Fe uptake and homeostasis in plants. More importantly, we further reviewed current studies on the molecular mechanisms underlying the cross-talks between N, Pi, S, Zn, and Fe homeostasis to better understand how these nutrient interactions affect the mineral uptake and signaling in plants. This review serves as a basis for further studies on multiple nutrient stress signaling in plants. Overall, the development of an integrative study of multiple nutrient signaling cross-talks in plants will be of important biological significance and crucial to sustainable agriculture.
Accuracy of Flash Glucose Monitoring During Postprandial Rest and Different Walking Conditions in Overweight or Obese Young Adults

Aims: To investigate the accuracy of FreeStyle LibreTM flash glucose monitoring (FGM) relevant to plasma glucose (PG) measurements during postprandial rest and different walking conditions in overweight/obese young adults. Methods: Data of 40 overweight/obese participants from two randomized crossover studies were pooled into four trials: (1) sitting (SIT, n = 40); (2) walking continuously for 30 min initiated 20 min before individual postprandial glucose peak (PPGP) (20iP + CONT, n = 40); (3) walking continuously for 30 min initiated at PPGP (iP + CONT, n = 20); and (4) accumulated walking for 30 min initiated 20 min before PPGP (20iP + ACCU, n = 20). Paired FGM and PG were measured 4 h following breakfast. Results: The overall mean absolute relative difference (MARD) between PG and FGM readings was 16.4 ± 8.6% for SIT, 16.2 ± 4.7% for 20iP + CONT, 16.7 ± 12.2% for iP + CONT, and 19.1 ± 6.8% for 20iP + ACCU. The Bland–Altman analysis showed a bias of −1.03 mmol⋅L–1 in SIT, −0.89 mmol⋅L–1 in 20iP + CONT, −0.82 mmol⋅L–1 in iP + CONT, and −1.23 mmol⋅L–1 in 20iP + ACCU. The Clarke error grid analysis showed that 99.6–100% of the values in all trials fell within zones A and B. Conclusion: Although FGM readings underestimated PG, the FGM accuracy was overall clinically acceptable during postprandial rest and walking in overweight/obese young adults.
Industrial experiences with the evolution of a DSL

At Philips IGT, we develop and produce interventional X-ray systems. For a controller in these systems, we have an approximately five years old domain specific language. Like general programming languages, domains specific languages also evolve. These languages co-evolve together with their domain. The language used at IGT was initially created for one system instance. Because of our positive experiences with the language, we want to evolve the language to support a family of systems. In this paper, we report on our experiences with the modifications we made to the original language. We made these changes preserving the behavior of the existing system instance. To prevent confidentiality issues, we use a Lego robot in our examples.
Intra- and Inter-Contrastive Learning for Micro-expression Action Unit Detection

Encoding facial expressions via Action Units (AUs) has been found effective for resolving the ambiguity issue among different expressions. In the literature, AU detection has extensive researches in macro-expressions. However, there is limited research about AU analysis for micro-expressions (MEs). Micro-expression Action Unit (MEAU) detection becomes a challenging problem because of the subtle facial motion. To alleviate this problem, in this paper, we study the contrastive learning for modeling subtle AUs and propose a novel MEAU detection method by learning the intra- and inter-contrastive information among MEs. Through the intra-contrastive learning module, the difference between the onset and apex frames is enlarged and utilized to obtain the discriminative representation for low-intensity AU detection. In addition, considering the subtle difference between MEAUs, the inter-contrastive learning is designed to automatically explore and enlarge the difference between different AUs to enhance the MEAU detection robustness. Intensive experiments on two widely used ME databases have demonstrated the effectiveness and generalization ability of our proposed method.
Multimodal Sentiment Analysis based on Recurrent Neural Network and Multimodal Attention

Automatic estimation of emotional state has a wide application in human-computer interaction. In this paper, we present our solutions for the MuSe-Stress and MuSe-Physio sub-challenge of Multimodal Sentiment Analysis (MuSe 2021). The goal of these two sub-challenges is to perform continuous emotion predictions from people in stressed dispositions. To this end, we first extract both handcrafted features and deep representations from multiple modalities. Then, we explore the Long Short-Term Memory network and Transformer Encoder with Multimodal Multi-head Attention to model the complex temporal dependencies in the sequence. Finally, we adopt the early fusion, late fusion and model fusion to boost the model's performance by exploiting complementary information from different modalities. Our method achieves CCC of 0.6648, 0.3054 and 0.5781 for valence, arousal and arousal plus EDA (anno12_EDA). The results of valence and anno12_EDA outperform the baseline system with corresponding CCC of 0.5614 and 0.4908, and both rank Top3 in these challenges.
A TMR Angle Sensor for Gesture Acquisition and Disambiguation on the Electric Guitar

This paper presents a novel approach to the acquisition of musical gestures on guitar based on Tunneling Magnetoresistance (TMR) sensing. With this minimally invasive setup, tracking of the horizontal displacement of the strings is used to capture gestures related to left and right-hand techniques. A pitch-based calibration is suggested to map the sensed displacement to pitch shifts so that the acquired signals can be directly used to estimate pitch produced by string bending in real-time. Some of the performer’s gestures, despite corresponding to different physical interactions, might produce a similar sonic output, as is the case of upward and downward string bends on the guitar. The proposed technology can be used to disambiguate between these gestures whether that is for automatic transcription purposes or for crafting instrument augmentations that build upon the performer’s existing expertise.
Program analysis via efficient symbolic abstraction

This paper concerns the scalability challenges of symbolic abstraction: given a formula ϕ in a logic L and an abstract domain A, find a most precise element in the abstract domain that over-approximates the meaning of ϕ. Symbolic abstraction is an important point in the space of abstract interpretation, as it allows for automatically synthesizing the best abstract transformers. However, current techniques for symbolic abstraction can have difficulty delivering on its practical strengths, due to performance issues. In this work, we introduce two algorithms for the symbolic abstraction of quantifier-free bit-vector formulas, which apply to the bit-vector interval domain and a certain kind of polyhedral domain, respectively. We implement and evaluate the proposed techniques on two machine code analysis clients, namely static memory corruption analysis and constrained random fuzzing. Using a suite of 57,933 queries from the clients, we compare our approach against a diverse group of state-of-the-art algorithms. The experiments show that our algorithms achieve a substantial speedup over existing techniques and illustrate significant precision advantages for the clients. Our work presents strong evidence that symbolic abstraction of numeric domains can be efficient and practical for large and realistic programs.
User Perceptions of Security and Privacy for Group Chat

Secure messaging tools are an integral part of modern society. To understand users’ security and privacy perceptions and requirements for secure group chat, we surveyed 996 respondents in the US and UK. Our results show that group chat presents important security and privacy challenges, some of which are not present in one-to-one chat. For example, users need to be able to manage and monitor group membership, establish trust for new group members, and filter content that they share in different chat contexts. We also find that respondents lack mechanisms for determining which tools are secure and instead rely on non-technical strategies for protecting their privacy—for example, self-filtering and carefully tracking group membership. To better understand how these results relate to existing tools, we conduct cognitive walkthroughs (a form of expert usability review) for five popular group chat tools. Our results demonstrate that while existing tools address some items identified in our surveys, this support is partial and is insufficient in many cases. As such, there is a need for improved group chat tools that better align with user perceptions and requirements. Based on these findings, we provide recommendations on improving the security and usability of secure group chat.
Educational Data Mining: Discovering Principal Factors for Better Academic Performance

The past decades have witnessed the vigorous development of new technologies in the educational field, among which Educational Data Mining (EDM) played an indispensable role in pedagogical improvement, enabling researchers to discover useful knowledge from education-oriented databases. By clustering student-related and parents-related variables into three categories: demographic and family background information (Demographic), self-perceived willingness for education (Willingness), perceived family interaction (Interaction) and utilizing various EDM methodologies such as linear regression, regression tree, random forest, and neural network, this study is the first attempt to conduct a comprehensive and quantitative investigation into the principal factors that influence Chinese junior high school students’ academic performance on a nationally representative survey, the China Education Panel Survey (CEPS) dataset. Additionally, this study further summarizes, explains, and compares different principal factors discovered by different EDM techniques, and proposes two practical strategies for mitigating China's educational inequality.
Formal verification of high-level synthesis

High-level synthesis (HLS), which refers to the automatic compilation of software into hardware, is rapidly gaining popularity. In a world increasingly reliant on application-specific hardware accelerators, HLS promises hardware designs of comparable performance and energy efficiency to those coded by hand in a hardware description language such as Verilog, while maintaining the convenience and the rich ecosystem of software development. However, current HLS tools cannot always guarantee that the hardware designs they produce are equivalent to the software they were given, thus undermining any reasoning conducted at the software level. Furthermore, there is mounting evidence that existing HLS tools are quite unreliable, sometimes generating wrong hardware or crashing when given valid inputs. To address this problem, we present the first HLS tool that is mechanically verified to preserve the behaviour of its input software. Our tool, called Vericert, extends the CompCert verified C compiler with a new hardware-oriented intermediate language and a Verilog back end, and has been proven correct in Coq. Vericert supports most C constructs, including all integer operations, function calls, local arrays, structs, unions, and general control-flow statements. An evaluation on the PolyBench/C benchmark suite indicates that Vericert generates hardware that is around an order of magnitude slower (only around 2× slower in the absence of division) and about the same size as hardware generated by an existing, optimising (but unverified) HLS tool.
Converting Manufacturing Companies into Data-Driven Enterprises: an Evaluation of the Transformation Model

Several approaches can be found in the literature to describe the transformation of companies into data-driven enterprises. A comparative framework was set up to objectively evaluate such transformation models As an additional result, a new model was derived, in which six steps were finally included: Research and Preparation, Digital Maturity Level Assessment, Target Definition, Gap Analysis, Identifying Measures and Implementation. This paper concentrates on the validation of the proposed model by an expert panel. Through the responses of 20 experts, potential strengths and weaknesses were revealed and analyzed using statistical methods. In summary, the expert attitude towards the proposed model was supportive throughout all evaluation dimensions. Thus, this work provides executives of manufacturing companies with a roadmap to plan and to perform a transformation into a data-driven enterprise. A direction for further research would be an adaptation of the proposed general model to the specific needs of manufacturing companies.
Symmetric distributed applications

A program is deterministic if multiple re-executions with the same inputs always lead to the same state. Even concurrent instances of a deterministic program should observe identical behavior---in real time---if assigned the same set of inputs. In this work, we propose real-time reproducibility for distributed programs. Multiple instances of the same interactive application can broadcast asynchronous inputs and yet conform to identical behavior. Collaborative networked applications, such as watch parties, document editing, and video games can benefit from this approach. We name this class of applications as symmetric distributed applications. Using a standard event-driven API to wait and emit events, programmers write code as if the application executes in a single machine. Our middleware intercepts event generation and synchronizes all instances in a consistent timeline so that receipt is identically reproducible. Not only distributed applications benefit from consistency and determinism but also development and testing can be done in a single instance with the same guarantees. In our experiments, the middleware can handle applications with 25 FPS, distributed in up to 25 nodes over the Internet, with an event latency below 350ms.
Attachment Recognition in School Age Children Based on Automatic Analysis of Facial Expressions and Nonverbal Vocal Behaviour

Attachment is a psychological construct that accounts for whether children are secure (the parents meet their physical and emotional needs) or insecure (the parents do not meet their physical and emotional needs). Unless identified and supported early enough, insecure children develop higher chances of experiencing issues such as antisocial behaviour or suicidal tendencies. For this reason, this article proposes a multimodal approach for attachment recognition in school age children (5-9 years old). In particular, the approach infers the attachment condition of a child from facial expressions and nonverbal vocal behaviour. The experiments involved 104 children that were recorded while undergoing the Manchester Child Attachment Story Test, an instrument that child psychiatrists use often to identify insecure children. The results show that attachment can be recognized with accuracy up to 71.2% (F1 score 62.4%).
PARA: Privacy Management and Control in Emerging IoT Ecosystems using Augmented Reality

The ubiquity of smart devices, combined with a lack of information about data garnered by them, make privacy a significant challenge for adopting smart devices. Ensuring users can safeguard their privacy without compromising the devices’ functionality requires effective yet intuitive ways to manage personal privacy preferences. Current solutions for privacy management are severely lacking as they are ineffective in making users aware of potential privacy risks or how to mitigate them and as they offer limited support for interaction. As our first contribution, we develop a novel AR privacy management interface (PARA) that uses AR visualization to contextualize data disclosure and improve user’s perceptions of privacy threats. Besides offering support for enhancing user’s privacy perceptions, our interface supports privacy control on compatible devices through privacy-enhancing technologies. As our second contribution, we systematically study factors affecting privacy perceptions and privacy control for two device classes (smart camera and smart speaker) through a user study with N = 32 participants. Our results show that PARA’s contextualization and visualization of privacy disclosure strongly affect the participants’ privacy perceptions. For privacy control, we demonstrate that our prototype improves the participant’s capability to identify risks and provides an effective and easy-to-use mechanism for controlling privacy disclosure, in contrast to existing state-of-the-art privacy management interfaces.
Kotlin coroutines: design and implementation

Asynchronous programming is having its “renaissance” moment in recent years. Created in the 1980s, it was in use for quite some time, but with the advent of multi-core processors, it has been sidestepped by multi-threaded programming, which was (for a long time) the de facto standard of performing concurrent computations. However, since the 2000s, more and more programming languages have begun to include the support for asynchronous programming, some built around asynchronicity from the start, others including it later in their evolution. In this paper, we explore the design and implementation of asynchronous programming in Kotlin, a multiplatform programming language from JetBrains, which uses coroutines for asynchronicity. Kotlin provides a compact built-in API for coroutine support, thus giving a lot of implementation freedom to the developer; this flexibility allows to transparently support different flavours of asynchronous programming within the same language. We overview existing approaches to asynchronous programming, zoom in and talk about coroutines in detail, and describe how they are used in Kotlin as the basis for asynchronous computations. Along the way, we show the flexibility of Kotlin coroutines, highlight several existing problems with asynchronicity, how they are fixed or worked-around in Kotlin, and also mention future directions asynchronous programming might explore.
The kingdoms of objects and values

THE purpose of the following paper is to consider whether there is a fundamental division of the [data] with which [programming] is concerned into two classes, [objects] and [values], or whether there is any method of overcoming this dualism. My own opinion is that the dualism is ultimate; on the other hand, many [colleagues] with whom, in the main, I am in close agreement, hold that it is not ultimate. (paraphrased after Bertrand Russell)
MonkeyDB: effectively testing correctness under weak isolation levels

Modern applications, such as social networking systems and e-commerce platforms are centered around using large-scale storage systems for storing and retrieving data. In the presence of concurrent accesses, these storage systems trade off isolation for performance. The weaker the isolation level, the more behaviors a storage system is allowed to exhibit and it is up to the developer to ensure that their application can tolerate those behaviors. However, these weak behaviors only occur rarely in practice and outside the control of the application, making it difficult for developers to test the robustness of their code against weak isolation levels. This paper presents MonkeyDB, a mock storage system for testing storage-backed applications. MonkeyDB supports a key-value interface as well as SQL queries under multiple isolation levels. It uses a logical specification of the isolation level to compute, on a read operation, the set of all possible return values. MonkeyDB then returns a value randomly from this set. We show that MonkeyDB provides good coverage of weak behaviors, which is complete in the limit. We test a variety of applications for assertions that fail only under weak isolation. MonkeyDB is able to break each of those assertions in a small number of attempts.
Conceptualizing Embodied Pedagogical Mediation (EPM): The Plávana Project, A Choreographer’s Toolkit.

In an age where circumstance makes the need and plausibility of digital cultural mediation a reality, it becomes necessary to consider the effects such mediation has on pedagogical process. It is our purpose through work with The Plavana Project, to create an artifact that can be used as a digital toolkit for practitioners of tradition, a study tool for its students, and an introduction to the novice for the history and cultural memory carried in its teachings. We hypothesize that through a dedicated focus on Sense of Embodiment (SoE) using the immersive qualities of consumer Virtual Reality (VR), we can place the participant in historically relevant settings and provide an authentic learning experience that is generally only available locally and synchronously. In this study, we apply the fundamentals of digital game design and development, the immersive qualities and philosophies of consumer VR, and the Embodied Pedagogical Mediation (EPM) of current teachers and scholars in the traditional Odissi dance form.