Miracle-dz/UniFAR-FTUs huggingface dataset:
The roles of Kerr nonlinearity in a bosonic quantum neural network

The emerging technology of quantum neural networks (QNNs) offers a quantum advantage over classical artificial neural networks (ANNs) in terms of speed or efficiency of information processing tasks. It is well established that nonlinear mapping between input and output is an indispensable feature of classical ANNs, while in a QNN the roles of nonlinearity are not yet fully understood. As one tends to think of QNNs as physical systems, it is natural to think of nonlinear mapping originating from a physical nonlinearity of the system, such as Kerr nonlinearity. Here we investigate the effect of Kerr nonlinearity on a bosonic QNN in the context of both classical (simulating an XOR gate) and quantum (generating Schrödinger cat states) tasks. Aside offering a mechanism of nonlinear input-output mapping, Kerr nonlinearity reduces the effect of noise or losses, which are particularly important to consider in the quantum setting. We note that nonlinear mapping may also be introduced through a nonlinear input-output encoding rather than a physical nonlinearity: for example, an output intensity is already a nonlinear function of input amplitude. While in such cases Kerr nonlinearity is not strictly necessary, it still increases the performance in the face of noise or losses.
What Determines Active Region Coronal Plasma Composition?

The chemical composition of the solar corona is different from that of the solar photosphere, with the strongest variation being observed in active regions (ARs). Using data from the Extreme Ultraviolet (EUV) Imaging Spectrometer (EIS) on Hinode, we present a survey of coronal elemental composition as expressed in the first ionization potential (FIP) bias in 28 ARs of different ages and magnetic flux content, which are at different stages in their evolution. We find no correlation between the FIP bias of an AR and its total unsigned magnetic flux or age. However, there is a weak dependence of FIP bias on the evolutionary stage, decreasing from 1.9 to 2.2 in ARs with spots to 1.5–1.6 in ARs that are at more advanced stages of the decay phase. FIP bias shows an increasing trend with average magnetic flux density up to 200 G, but this trend does not continue at higher values. The FIP bias distribution within ARs has a spread between 0.4 and 1. The largest spread is observed in very dispersed ARs. We attribute this to a range of physical processes taking place in these ARs, including processes associated with filament channel formation. These findings indicate that, while some general trends can be observed, the processes influencing the composition of an AR are complex and specific to its evolution, magnetic configuration, or environment. The spread of FIP bias values in ARs shows a broad match with that previously observed in situ in the slow solar wind.
A Verification Framework for Certifying Learning-Based Safety-Critical Aviation Systems

We present a safety verification framework for design-time and run-time assurance of learning-based components in aviation systems. Our proposed framework integrates two novel methodologies. From the design-time assurance perspective, we propose offline mixed-fidelity verification tools that incorporate knowledge from different levels of granularity in simulated environments. From the run-time assurance perspective, we propose reachability- and statistics-based online monitoring and safety guards for a learning-based decision-making model to complement the offline verification methods. This framework is designed to be loosely coupled among modules, allowing the individual modules to be developed using independent methodologies and techniques, under varying circumstances and with different tool access. The proposed framework offers feasible solutions for meeting system safety requirements at different stages throughout the system development and deployment cycle, enabling the continuous learning and assessment of the system product.
PinnerFormer: Sequence Modeling for User Representation at Pinterest

Sequential models have become increasingly popular in powering personalized recommendation systems over the past several years. These approaches traditionally model a user's actions on a website as a sequence to predict the user's next action. While theoretically simplistic, these models are quite challenging to deploy in production, commonly requiring streaming infrastructure to reflect the latest user activity and potentially managing mutable data for encoding a user's hidden state. Here we introduce PinnerFormer, a user representation trained to predict a user's future long-term engagement using a sequential model of a user's recent actions. Unlike prior approaches, we adapt our modeling to a batch infrastructure via our new dense all-action loss, modeling long-term future actions instead of next action prediction. We show that by doing so, we significantly close the gap between batch user embeddings that are generated once a day and realtime user embeddings generated whenever a user takes an action. We describe our design decisions via extensive offline experimentation and ablations and validate the efficacy of our approach in A/B experiments showing substantial improvements in Pinterest's user retention and engagement when comparing PinnerFormer against our previous user representation. PinnerFormer is deployed in production as of Fall 2021.
Eﬀective local elastic moduli of a strongly disordered medium

The local elastic properties of amorphous solids are investigated using the theory of correlated random matrices. A signiﬁcant increase in stiﬀness is shown in the interfacial region, which characteristic thickness depends on the strength of disorder. The studied interfacial eﬀect increases the inﬂuence of nanoparticles on the macroscopic stiﬀness of nanocomposites. The obtained thickness of the interfacial region has the same order as the length scale of the boson peak.
Image2Gif: Generating Continuous Realistic Animations with Warping NODEs

Generating smooth animations from a limited number of sequential observations has a number of applications in vision. For example, it can be used to increase number of frames per second, or generating a new trajectory only based on ﬁrst and last frames, e.g. a motion of face emotions. Despite the discrete observed data (frames), the problem of generating a new trajectory is a continues problem. In addition, to be perceptually realistic, the domain of an image should not alter drastically through the trajectory of changes. In this paper, we propose a new framework, Warping Neural ODE, for generating a smooth animation (video frame interpolation) in a continuous manner, given two (“farther apart”) frames, denoting the start and the end of the animation. The key feature of our framework is uti-lizing the continuous spatial transformation of the image based on the vector ﬁeld, derived from a system of differential equations. This allows us to achieve the smoothness and the realism of an animation with inﬁnitely small time steps between the frames. We show the application of our work in generating an animation given two frames, in different training settings, including Generative Adversarial Network (GAN) and with L 2 loss.
Entropic CLT for Order Statistics

It is well known that central order statistics exhibit a central limit behavior and converge to a Gaussian distribution as the sample size n grows. This paper strengthens this known result by establishing an entropic version of the central limit theorem (CLT) that ensures a stronger mode of convergence using the relative entropy. In particular, an order $O(1/\sqrt n )$ rate of convergence is established under mild conditions on the parent distribution of the sample generating the order statistics. To prove this result, ancillary results on order statistics are derived, which might be of independent interest.
The Future of Hybrid Meetings

Meetings are typically considered to be the fuel of an organization’s productivity—a place where employees discuss ideas and make collective decisions. However, it is no secret that meetings are also often perceived as wasteful vacuums, depleting employee morale and productivity, likely due to the fact that current technologies fall short in fully supporting physical or virtual meeting experience. In this position paper, we discuss the three key elements that make a meeting successful (i.e., execution, psychological safety, and physical comfort), and present new tools for hybrid meetings that incorporate those elements. As past research has focused on supporting meeting execution (the first element), we set the roadmap for future research on the two other elements: on psychological safety by articulating how new technologies could make meeting useful for all participants, ensure all participants give and receive appropriate levels of attention, and enable all participants to feel and make others feel comfortable; and on physical comfort by dwelling on how new technologies could make the meeting experience comfortable by integrating all human senses. We also discuss the potential danger of these technologies inadvertently becoming surveillance tools.
Probabilistic and Non-deterministic Event Data in Process Mining: Embedding Uncertainty in Process Analysis Techniques

Process mining is a subfield of process science that analyzes event data collected in databases called event logs. Recently, novel types of event data have become of interest due to the wide industrial application of process mining analyses. In this paper, we examine uncertain event data. Such data contain meta-attributes describing the amount of imprecision tied with attributes recorded in an event log. We provide examples of uncertain event data, present the state of the art in regard of uncertainty in process mining, and illustrate open challenges related to this research direction.
On some studies of Fraud Detection Pipeline and related issues from the scope of Ensemble Learning and Graph-based Learning

The UK anti-fraud charity Fraud Advisory Panel (FAP) in their review of 2016 estimates business costs of fraud at £144 billion, and its individual counterpart at £9.7 billion. Banking, insurance, manufacturing, and government are the most common industries affected by fraud activities. Designing an efficient fraud detection system could avoid losing the money; however, building this system is challenging due to many difficult problems, e.g.imbalanced data, computing costs, etc. Over the last three decades, there are various research relates to fraud detection but no agreement on what is the best approach to build the fraud detection system. In this thesis, we aim to answer some questions such as i) how to build a simplified and effective Fraud Detection System that not only easy to implement but also providing reliable results and our proposed Fraud Detection Pipeline is a potential backbone of the system and is easy to be extended or upgraded, ii) when to update models in our system (and keep the accuracy stable) in order to reduce the cost of updating process, iii) how to deal with an extreme imbalance in big data classification problem, e.g. fraud detection, since this is the gap between two difficult problems, iv) further, how to apply graph-based semi-supervised learning to detect fraudulent transactions.
Cognitive Visual-learning Environment for PostgreSQL

PostgreSQL is an object-relational database (ORDBMS) that was introduced into the database community and has been avidly used for a variety of information extraction use cases. It is also known to be an advanced SQL-compliant open source Object RDBMS. However, users have not yet resolved to PostgreSQL due to the fact that it is still under the layers and the complexity of its persistent textual environment for an amateur user.Hence, there is a dire need to provide an easy environment for users to comprehend the procedure and standards with which databases are created, tables and the relationships among them, manipulating queries and their flow based on conditions in Postgresql. As such, this project identifies the dominant features offered by Postgresql, analyzes the constraints that exist in the database user community in migrating to Postgresql and based on the scope and constraints identified, develop a system that will serve as a query generation platform as well as a learning tool that will provide an interactive environment to cognitively learn PostgreSQL query building. This is achieved using a visual editor incorporating a textual editor for a well-versed user. By providing visually-draggable query components to work with, this research aims to offer a cognitive, visual and tactile environment where users can interactively learn PostgreSQL query generation.
Understanding Jargon: Combining Extraction and Generation for Definition Modeling

Can machines know what twin prime is? From the composition of this phrase, machines may guess twin prime is a certain kind of prime, but it is still difficult to deduce exactly what twin stands for without additional knowledge. Here, twin prime is a jargon - a specialized term used by experts in a particular field. Explaining jargon is challenging since it usually requires domain knowledge to understand. Recently, there is an increasing interest in extracting and generating definitions of words automatically. However, existing approaches, either extraction or generation, perform poorly on jargon. In this paper, we propose to combine extraction and generation for jargon definition modeling: first extract self- and correlative definitional information of target jargon from the Web and then generate the final definitions by incorporating the extracted definitional information. Our framework is remarkably simple but effective: experiments demonstrate our method can generate high-quality definitions for jargon and outperform state-of-the-art models significantly, e.g., BLEU score from 8.76 to 22.66 and human-annotated score from 2.34 to 4.04.
Representing Hierarchical Structure by Using Cone Embedding

Graph embedding is becoming an important method with applications in various areas, including social networks and knowledge graph completion. In particular, Poincaré embedding has been proposed to capture the hierarchical structure of graphs, and its effectiveness has been reported. However, most of the existing methods have isometric mappings in the embedding space, and the choice of the origin point can be arbitrary. This fact is not desirable when the distance from the origin is used as an indicator of hierarchy, as in the case of Poincaré embedding. In this paper, we propose cone embedding , embedding method in a metric cone, which solve these problems, and we gain further beneﬁts: 1) we provide an indicator of hierarchical information that is both geometri-cally and intuitively natural to interpret, and 2) we can extract the hierarchical structure from a graph embedding output of other methods by learning additional one-dimensional parameters.
The innovation element of the diploma (b.eng.) programs at DTU

In September 2014 the first version of the newly developed CDIO-based diploma (B.Eng) programs were launched at DTU (Nyborg et al., 2015). The programs are the result of a comprehensive merger process of former diploma programs, namely the programs at Engineering College of Copenhagen (now DTU Diploma) and the Technical University of Denmark. The most significant new activity in the programs is the introduction of a common 10 ECTS compulsory course in innovation in the later part of the programs. The idea behind this course is to give students the opportunity to collaborate on interdisciplinary real-life projects. This course strengthens not only innovation skills but personal and interpersonal skills as well.
Roles of Endomembrane Alkali Cation/Proton Exchangers in Synaptic Function and Neurodevelopmental Disorders

Endomembrane alkali cation (Na+, K+)/proton (H+) exchangers (eNHEs) are increasingly associated with neurological disorders. These eNHEs play integral roles in regulating the luminal pH, processing, and trafficking of cargo along the secretory (Golgi and post-Golgi vesicles) and endocytic (early, recycling, and late endosomes) pathways, essential regulatory processes vital for neuronal development and plasticity. Given the complex morphology and compartmentalization of multipolar neurons, the contribution of eNHEs in maintaining optimal pH homeostasis and cargo trafficking is especially significant during periods of structural and functional development and remodeling. While the importance of eNHEs has been demonstrated in a variety of non-neuronal cell types, their involvement in neuronal function is less well understood. In this review, we will discuss their emerging roles in excitatory synaptic function, particularly as it pertains to cellular learning and remodeling. We will also explore their connections to neurodevelopmental conditions, including intellectual disability, autism, and attention deficit hyperactivity disorders.
Sequence to structure analysis of the ORF4 protein from Hepatitis E virus

Hepatitis E virus (HEV) is the main cause of acute hepatitis worldwide. HEV accounts for up to 30% mortality rate in pregnant women, with highest incidences reported for genotype 1 (G1) HEV. The contributing factors in adverse cases during pregnancy in women due to HEV infection is still debated. The mechanism underlying the pathogenesis of viral infection is attributed to different genomic component of HEV, i.e., open reading frames (ORFs): ORF1, ORF2, ORF3 and ORF4. Recently, ORF4 has been discovered in enhancing the replication of GI isolates of HEV through regulation of an IRES-like RNA element. However, its characterization through computational methodologies remains unexplored. In this novel study, we provide comprehensive overview of ORF4 protein's genetic and molecular characteristics through analyzing its sequence and different structural levels. A total of three different datasets (Human, Rat and Ferret) of ORF4 genomes were built and comparatively analyzed. Several non-synonymous mutations in conjunction with higher entropy values were observed in rat and ferret datasets, however, limited variation was observed in human ORF4 genomes. Higher transition to tranversion ratio was observed in the ORF4 genomes. Studies have reported the association of intrinsic disordered proteins (IDP) with drug discovery due to its role in several signaling and regulatory processes through protein-protein interactions (PPIs). As PPIs are potent drug target sources, thus the ORF4 protein was explored by analyzing its polypeptide structure in order to shed light on its intrinsic disorder. Pressures that lead towards preponderance of disordered-promoting amino acid residues shaped the evolution of ORF4. The intrinsic disorder propensity analysis revealed ORF4 protein (Human) as a highly disordered protein (IDP). Predominance of coils and lack of secondary structure further substantiated our findings suggesting its involvement in binding to ligand molecules. Thus, ORF4 contributes to cellular signaling processes through protein-protein interactions, as IDPs are targets for regulation to accelerate the process of drug designing strategies against HEV infections.
An empirical examination of sustainable metropolitan development in Semarang City, Indonesia

ABSTRACT In the urban and regional context, conflicts between economic growth and environmental concerns are so intense that sustainable development might be elusive. Evidence indicates that the spatial dynamics in many Indonesian metropolitan regions are not in accordance with sustainability. This research is thus aimed at empirically examining sustainable metropolitan development of Semarang City, based on a model proposed by Sugiri, Buchori, and Soetomo [2011. “Sustainable Metropolitan Development: Towards an Operational Model for Semarang Metropolitan Region. ” The International Journal of Environmental, Cultural, Economic and Social Sustainability 7 (5): 301–324]. The methods mainly involve spatial analysis using GIS and comparison of statistical data, supported by complementary qualitative information. The results show that the spatial development of Semarang City tends to be unsustainable, based on important indicators. However, the availability of such local initiatives as spatial plans and environmental impact studies should be considered essential in ensuring sustainable development. In addition, the growth of small-scale industries is prospective to increase public involvement in productive activities.
Effect of Loneliness on Older Adults’ Autobiographical Memory Retrieval

Summary People are currently experiencing unprecedented levels of loneliness and social isolation due to the current pandemic, and these conditions may have harmful effects on the relationships we have with our personal histories. Loneliness impairs executive function (Sin et al., 2020). Executive function helps manage memory specificity and episodic detail (e.g. Piolino et al., 2010), and memory valence (e.g. Carstensen & DeLiema, 2018). Given this interaction, a potential consequence of loneliness could be changes in autobiographical memory, and these changes could differ depending on age. Summary Touch is an understudied channel of emotional communication that is important for children’s development. This study will examine connection and intensity of mother-child touch in typically-developing 9-year-old children matched with children with clini-cally-significant behaviour problems. For children with behaviour problems, disconnected touch is expected to be more intense, and connected touch less intense, than for children without behaviour problems. Summary Older age is characterised by more positive and less negative emotional experiences, the so-called “age advantages. ” In two studies, we replicated and extended these findings during the COVID-19 pandemic. In Study 1, using data from 23,629 participants in 63 countries, we provided strong support for the per-sistence of the age advantages during the COVID-19 pandemic. In Study 2, we conducted a direct comparison of the age advantages before vs during the COVID-19 pandemic (total N = 1,891). We showed that the age advantage is diminished for negative emotions during the pandemic. Summary Empathy is important for navigating our social worlds; however, we know little about its early development or the extent of cultural variability. Using a longitudinal cross-cultural design, we investigated the development of empathy in Ugandan and UK infants. When witnessing an adult in distress, we found infants’ empathic comforting was predicted by age but not by culture. However, Ugandan infants showed greater concerned facial affect than UK infants. Results highlight both cross-cultural continuities and variability in early empathy development.