Miracle-dz/UniFAR-FTUs huggingface dataset:
Supporting the creation of sequence of events in nursing education

— The goal of this research was to develop a Sequence of Events Creation Support System for nursing students. We first conducted a student attitude survey on Sequence of Events to identify points that the students felt were difficult. The three main problem areas were 1) thinking about correlations, 2) drawing diagrams, and 3) collecting references. We therefore designed the system that would help students overcome these difficulties. The system, which makes use of Microsoft Excel VBA, enables students to easily draw, review, and revise Sequence of Events. We examined Sequence of Events created using the Sequence of Events Creation Support System and created by hand to determine the differences in the content of the Sequence of Events created and in the creation process. Experimental results demonstrated the effectiveness of the system. Next, we conducted lectures and exercises using the system in two 1st year courses at a nursing college and we conducted a questionnaire survey to evaluate the effectiveness of the system. Based on the results of this evaluation, we confirmed that the opinions obtained were for the most part positive.
On-line non-overlapping camera calibration net

We propose an easy-to-use non-overlapping camera calibration method. First, successive images are fed to a PoseNet-based network to obtain ego-motion of cameras between frames. Next, the pose between cameras are estimated. Instead of using a batch method, we propose an on-line method of the inter-camera pose estimation. Furthermore, we implement the entire procedure on a computation graph. Experiments with simulations and the KITTI dataset show the proposed method to be effective in simulation.
Inductive Representation Learning on Temporal Graphs

Inductive representation learning on temporal graphs is an important step toward salable machine learning on real-world dynamic networks. The evolving nature of temporal dynamic graphs requires handling new nodes as well as capturing temporal patterns. The node embeddings, which are now functions of time, should represent both the static node features and the evolving topological structures. Moreover, node and topological features can be temporal as well, whose patterns the node embeddings should also capture. We propose the temporal graph attention (TGAT) layer to efficiently aggregate temporal-topological neighborhood features as well as to learn the time-feature interactions. For TGAT, we use the self-attention mechanism as building block and develop a novel functional time encoding technique based on the classical Bochner's theorem from harmonic analysis. By stacking TGAT layers, the network recognizes the node embeddings as functions of time and is able to inductively infer embeddings for both new and observed nodes as the graph evolves. The proposed approach handles both node classification and link prediction task, and can be naturally extended to include the temporal edge features. We evaluate our method with transductive and inductive tasks under temporal settings with two benchmark and one industrial dataset. Our TGAT model compares favorably to state-of-the-art baselines as well as the previous temporal graph embedding approaches.
Omnidirectional Three Module Robot Design and Simulation

This paper introduces the Omnidirectional Tractable Three Module Robot for traversing inside complex pipe networks. The robot consists of three omnidirectional modules fixed 120° apart circumferentially which can rotate about their axis allowing holonomic motion of the robot. Holonomic motion enables the robot to overcome motion singularity when negotiating T-junctions and further allows the robot to arrive in a preferred orientation while taking turns inside a pipe. The singularity region while negotiating T-junctions is analyzed to formulate the geometry of the region. The design and motion capabilities are validated by conducting simulations in MSC ADAMS on a simplified lumped-model of the robot.
SOH Mach-Zehnder Modulators for 100 GBd PAM4 Signaling with Sub-1 dB Phase-Shifter Loss

We demonstrate 280 pm-long silicon-organic hybrid (SOH) modulators with optical phase-shifter losses of 0.7 dB and π-voltages of 1.5 V. We show OOK and PAM4 signaling at 100 GBd with a BER below the 7% HD-FEC limit.
Repair Rate Lower Bounds for Distributed Storage

A primary objective of a distributed storage system is to reliably store huge amounts of source data for long periods of time using a large number of high storage capacity nodes. Storage nodes are prone to permanent failures, where such node failures cause all stored data to be permanently lost, and failed nodes are replaced with nodes that initially store no data. To maintain recoverability of the source data as storage nodes fail and are replaced, a total amount of storage capacity that is larger than the source data size is allocated to store the source data, and a repairer continually reads data from and writes data to the storage nodes as they fail and are replaced. We prove information-theoretic lower bounds on the rate at which a repairer reads data from the storage system as a function of the rate at which nodes fail and the amount by which the allocated storage capacity exceeds the source data size. These lower bounds hold for any repairer, i.e., any repairer that does not read data at or above the lower bound rate will provably not maintain recoverability of the source data. The bounds are provably tight asymptotically as the number of storage nodes grows and the allocated storage capacity approaches the source data size.
Clustered coloring of graphs excluding a subgraph

A graph coloring has bounded clustering if each monochromatic component has bounded size. This paper studies clustered coloring, where the number of colors depends on an excluded subgraph. This is a much weaker assumption than previous works, where typically the number of colors depends on an excluded minor. We prove the following: 1. Graphs of bounded treewidth and with no $K_{s,t}$-subgraph are $s+1$-choosable with bounded clustering, which is best possible. 2. Graphs excluding a fixed minor and with no $K_{s,t}$-subgraph are $s+2$-colorable with bounded clustering. The number of colors here is best possible. 3. Graphs excluding a fixed odd minor and with no $K_{s,t}$-subgraph are $2s+1$-colorable with bounded clustering, generalizing a result of the first author and Oum. 4. Graphs with no $K_{s+1}$-subdivision are $4s-5$-colorable with bounded clustering. It is the first $O(s)$ bound for such graphs. 5. Graphs excluding a fixed minor and with no $K_{s+1}$-subdivision are $s+1$-colorable with bounded clustering. This implies that $K_{s+1}$-minor-free graphs are $s+1$-colorable with bounded clustering, which is within one color of the clustered analogue of Hadwiger's conjecture. 6. Graphs with bounded treewidth and with no $K_{s+1}$-subdivision are $s$-choosable with bounded clustering. This implies the clustered analogue of Hajos' conjecture for graphs of bounded treewidth. 7. Graphs with bounded layered treewidth and with no $K_{s,t}$-subgraph are $s+2$-colorable with bounded clustering. Layered treewidth is a key tool in the above proofs, and is of independent interest since many natural graph classes have bounded layered treewidth.
Task-Space Synergies for Reaching Using Upper-Limb Prostheses

Synergistic prostheses enable the coordinated movement of the human-prosthetic arm, as required by activities of daily living. This is achieved by coupling the motion of the prosthesis to the human command, such as the residual limb movement in motion-based interfaces. Previous studies demonstrated that developing human-prosthetic synergies in joint-space must consider individual motor behaviour and the intended task to be performed, requiring personalisation and task calibration. In this work, an alternative synergy-based strategy, utilising a synergistic relationship expressed in task-space, is proposed. This task-space synergy has the potential to replace the need for personalisation and task calibration with a model-based approach requiring knowledge of the individual user’s arm kinematics, the anticipated hand motion during the task and voluntary information from the prosthetic user. The proposed method is compared with surface electromyography-based and joint-space synergy-based prosthetic interfaces in a study of motor behaviour and task performance on able-bodied subjects using a VR-based transhumeral prosthesis. Experimental results showed that for a set of forward reaching tasks the proposed task-space synergy achieves comparable performance to joint-space synergies without the need to rely on time-consuming calibration processes or human motor learning. Case study results with an amputee subject motivate the further development of the proposed task-space synergy method.
Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction

Session-based target behavior prediction aims to predict the next item to be interacted with specific behavior types (e.g., clicking). Although existing methods for session-based behavior prediction leverage powerful representation learning approaches to encode items’ sequential relevance in a low-dimensional space, they suffer from several limitations. Firstly, they focus on only utilizing the same type of user behavior for prediction, but ignore the potential of taking other behavior data as auxiliary information. This is particularly crucial when the target behavior is sparse but important (e.g., buying or sharing an item). Secondly, item-to-item relations are modeled separately and locally in one behavior sequence, and they lack a principled way to globally encode these relations more effectively. To overcome these limitations, we propose a novel Multi-relational Graph Neural Network model for Session-based target behavior Prediction, namely MGNN-SPred for short. Specifically, we build a Multi-Relational Item Graph (MRIG) based on all behavior sequences from all sessions, involving target and auxiliary behavior types. Based on MRIG, MGNN-SPred learns global item-to-item relations and further obtains user preferences w.r. t. current target and auxiliary behavior sequences, respectively. In the end, MGNN-SPred leverages a gating mechanism to adaptively fuse user representations for predicting next item interacted with target behavior. The extensive experiments on two real-world datasets demonstrate the superiority of MGNN-SPred by comparing with state-of-the-art session-based prediction methods, validating the benefits of leveraging auxiliary behavior and learning item-to-item relations over MRIG.
Semigroup-fication of univalent self-maps of the unit disc

Let $f$ be a univalent self-map of the unit disc. We introduce a technique, that we call {\sl semigroup-fication}, which allows to construct a continuous semigroup $(\phi_t)$ of holomorphic self-maps of the unit disc whose time one map $\phi_1$ is, in a sense, very close to $f$. The semigrup-fication of $f$ is of the same type as $f$ (elliptic, hyperbolic, parabolic of positive step or parabolic of zero step) and there is a one-to-one correspondence between the set of boundary regular fixed points of $f$ with a given multiplier and the corresponding set for $\phi_1$. Moreover, in case $f$ (and hence $\phi_1$) has no interior fixed points, the slope of the orbits converging to the Denjoy-Wolff point is the same. The construction is based on holomorphic models, localization techniques and Gromov hyperbolicity. As an application of this construction, we prove that in the non-elliptic case, the orbits of $f$ converge non-tangentially to the Denjoy-Wolff point if and only if the Koenigs domain of $f$ is "almost symmetric" with respect to vertical lines.
A Survey on Predictive Maintenance for Industry 4.0

Production issues at Volkswagen in 2016 lead to dramatic losses in sales of up to 400 million Euros per week. This example shows the huge financial impact of a working production facility for companies. Especially in the data-driven domains of Industry 4.0 and Industrial IoT with intelligent, connected machines, a conventional, static maintenance schedule seems to be old-fashioned. In this paper, we present a survey on the current state of the art in predictive maintenance for Industry 4.0. Based on a structured literate survey, we present a classification of predictive maintenance in the context of Industry 4.0 and discuss recent developments in this area.
Dissecting Neural ODEs

Continuous deep learning architectures have recently re-emerged as variants of Neural Ordinary Differential Equations (Neural ODEs). The infinite-depth approach offered by these models theoretically bridges the gap between deep learning and dynamical systems; however, deciphering their inner working is still an open challenge and most of their applications are currently limited to the inclusion as generic black-box modules. In this work, we "open the box" and offer a system-theoretic perspective, including state augmentation strategies and robustness, with the aim of clarifying the influence of several design choices on the underlying dynamics. We also introduce novel architectures: among them, a Galerkin-inspired depth-varying parameter model and neural ODEs with data-controlled vector fields.
Probabilistic principal component and linear discriminant analysis of interaction of 1064 nm CW laser with ICP plasma:Whistler wave exited anti stokes cooling

Pattern recognition techniques are known to play an emerging role for studying underlying physics of light-matter interaction. Photon condensation and Anti-Stokes cooling remain compelling phenomena of laser plasma interaction. In this work, the interaction of a 1064 nm continuum-wave laser with inductively-coupled plasma (ICP) of Hg has been studied by probabilistic pattern recognition over observed time resolved spectral data. Probabilistic principal component analysis (PPCA) over spectral data reveals the collective behavior of condensation and Stokes and Anti-Stokes line profiles. Probabilistic linear discriminant analysis (PLDA) of spectra proficiently illuminates the homoclinic orbit Whistler waves in 3D and decompose the spectral data to up and down scattered photons spectra as well as phonon-electron(hybrid) spectrum. According to hybrid spectrum, cooling is maintained by the electron-phonon relaxation. On the other hand, electron oscillation (wpe= 0.16 kHz) and density (ne~4x1013 cm^-3) of plasma are extracted by PLDA which does not demand kinetic modeling of spectra
CodeBERT: A Pre-Trained Model for Programming and Natural Languages

We present CodeBERT, a bimodal pre-trained model for programming language (PL) and natural language (NL). CodeBERT learns general-purpose representations that support downstream NL-PL applications such as natural language code search, code documentation generation, etc. We develop CodeBERT with Transformer-based neural architecture, and train it with a hybrid objective function that incorporates the pre-training task of replaced token detection, which is to detect plausible alternatives sampled from generators. This enables us to utilize both “bimodal” data of NL-PL pairs and “unimodal data, where the former provides input tokens for model training while the latter helps to learn better generators. We evaluate CodeBERT on two NL-PL applications by fine-tuning model parameters. Results show that CodeBERT achieves state-of-the-art performance on both natural language code search and code documentation generation. Furthermore, to investigate what type of knowledge is learned in CodeBERT, we construct a dataset for NL-PL probing, and evaluate in a zero-shot setting where parameters of pre-trained models are fixed. Results show that CodeBERT performs better than previous pre-trained models on NLPL probing.
Identifying Invariant Factors Across Multiple Environments with KL Regression

Many datasets are collected from multiple environments (e.g. different labs, perturbations, etc.), and it is often advantageous to learn models and relations that are invariant across environments. Invariance can improve robustness to unknown confounders and improve generalization to new domains. We develop a novel framework --KL regression-- to reliably estimate regression coefficients in a challenging multi-environment setting, where latent confounders affect the data from each environment. KL regression is based on a new objective of simultaneously minimizing the KL- divergence between a parametric model and the observed data from each environment. We prove that KL regression recovers the true invariant factors under a flexible confounding setup. Moreover, it is computationally efficient as we derive an analytic solution for its global optimum. In systematic experiments, we validate the improved performance of KL regression compared to commonly used approaches.
How Interaction Designers Use Tools to Manage Ideas

This article presents a grounded theory analysis based on a qualitative study of professional interaction designers (n = 20) with a focus on how they use tools to manage design ideas. Idea management can be understood as a subcategory of the field personal information management, which includes the activities around the capture, organization, retrieval, and use of information. Idea management pertains to the management and use of ideas, a particular type of information, as part of creative activities. The article identifies tool-supported idea management strategies and needs of professional interaction designers, and discusses the context and consequences of these strategies. Based on our analysis, we identify a conceptual framework of 10 strategies which are supported by tools: saving, externalizing, advancing, exploring, archiving, clustering, extracting, browsing, verifying, and collaborating. Finally, we discuss how this framework can be used to characterize and analyze existing and novel idea management tools.
Multi-Step Model-Agnostic Meta-Learning: Convergence and Improved Algorithms

As a popular meta-learning approach, the model-agnostic meta-learning (MAML) algorithm has been widely used due to its simplicity and effectiveness. However, the convergence of the general multi-step MAML still remains unexplored. In this paper, we develop a new theoretical framework, under which we characterize the convergence rate and the computational complexity of multi-step MAML. Our results indicate that N -step MAML attains the convergence with linearly increasing complexity with N under a properly chosen inner stepsize. We then take a further step to develop a more efficient Hessian-free MAML. We first show that the existing zeroth-order Hessian estimator contains a constant-level estimation error so that the MAML algorithm can perform unstably. To address this issue, we propose a novel Hessian estimator via a gradient-based Gaussian smoothing method, and show that it achieves a much smaller estimation bias and variance, and the resulting algorithm achieves the same performance guarantee as the original MAML under mild conditions. Our experiments validate our theory and demonstrate the effectiveness of the proposed Hessian estimator.
First order least-squares formulations for eigenvalue problems

In this paper we discuss spectral properties of operators associated with the least-squares finite-element approximation of elliptic partial differential equations. The convergence of the discrete eigenvalues and eigenfunctions towards the corresponding continuous eigenmodes is studied and analyzed with the help of appropriate $L^2$ error estimates. A priori and a posteriori estimates are proved.
Analyzing Grant-Free Access for URLLC Service

5G New Radio (NR) is expected to support new ultra-reliable low-latency communication (URLLC) service targeting at supporting the small packets transmissions with very stringent latency and reliability requirements. Current Long Term Evolution (LTE) system has been designed based on grant-based (GB) (i.e., dynamic grant) random access, which can hardly support the URLLC requirements. Grant-free (GF) (i.e., configured grant) access is proposed as a feasible and promising technology to meet such requirements, especially for uplink transmissions, which effectively saves the time of requesting/waiting for a grant. While some basic GF access features have been proposed and standardized in NR Release-15, there is still much space to improve. Being proposed as 3GPP study items, three GF access schemes with Hybrid Automatic Repeat reQuest (HARQ) retransmissions including Reactive, K-repetition, and Proactive, are analyzed in this article. Specifically, we present a spatio-temporal analytical framework for the contention-based GF access analysis. Based on this framework, we define the latent access failure probability to characterize URLLC reliability and latency performances. We propose a tractable approach to derive and analyze the latent access failure probability of the typical UE under three GF HARQ schemes. Our results show that under shorter latency constraints, the Proactive scheme provides the lowest latent access failure probability, whereas, under longer latency constraints, the K-repetition scheme achieves the lowest latent access failure probability, which depends on K. If K is overestimated, the Proactive scheme provides lower latent access failure probability than the K-repetition scheme.
Constraining stellar population parameters from narrow band photometric surveys using convolutional neural networks

Upcoming large-area narrow band photometric surveys, such as Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS), will enable us to observe a large number of galaxies simultaneously and efficiently. However, it will be challenging to analyse the spatially resolved stellar populations of galaxies from such big data to investigate galaxy formation and evolutionary history. We have applied a convolutional neural network (CNN) technique, which is known to be computationally inexpensive once it is trained, to retrieve the metallicity and age from J-PAS-like narrow-band images. The CNN was trained using synthetic photometry from the integral field unit spectra of the Calar Alto Legacy Integral Field Area survey and the age and metallicity obtained in a full spectral fitting on the same spectra. We demonstrate that our CNN model can consistently recover age and metallicity from each J-PAS-like spectral energy distribution. The radial gradients of the age and metallicity for galaxies are also recovered accurately, irrespective of their morphology. However, it is demonstrated that the diversity of the data set used to train the neural networks has a dramatic effect on the recovery of galactic stellar population parameters. Hence, future applications of CNNs to constrain stellar populations will rely on the availability of quality spectroscopic data from samples covering a wide range of population parameters.