Miracle-dz/UniFAR-FTUs huggingface dataset:
Fit The Right NP-Hard Problem: End-to-end Learning of Integer Programming Constraints

Bridging logical and algorithmic reasoning with modern machine learning techniques is a fundamental challenge with potentially transformative impact. On the algorithmic side, many NP-Hard problems can be expressed as integer programs, in which the constraints play the role of their “combinatorial speciﬁcation”. In this work, we aim to integrate integer programming solvers into neural network architectures by providing loss functions for both the objective and the constraints. The resulting end-to-end trainable architectures have the power of jointly extracting features from raw data and of solving a suitable (learned) combinatorial problem with state-of-the-art integer programming solvers. We experimentally validate our approach on artiﬁcial datasets created from random constraints, and on solving K NAPSACK instances from their description in natural language.
Optimisation of Renewable-Based Multi-Energy System with Hydrogen Energy for Urban-Industrial Symbiosis

a Department of Chemical and Environmental Engineering, Malaysia – Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100 Kuala Lumpur, Malaysia b School of Chemical and Energy Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 UTM, Johor Bahru, Johor, Malaysia c School of Energy and Chemical Engineering, Xiamen University Malaysia, Jalan Sunsuria, Bandar Sunsuria, 43900, Sepang, Selangor, Malaysia d Sustainable Process Integration Laboratory—SPIL, NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology VUT BRNO, Technická 2896/2, 616 69, Brno, Czech Republic pyliew@utm.my
360◦-video authoring pipeline using user-generated 3D models

We will present a Mixed Reality (MR) authoring and visualisation pipeline that enables non-expert users to create interactive 360◦ scene-scapes (e.g. photo-spheres or video-spheres that contain movable 3D objects), from digitised real world sources. This pipeline is distinct from others on the market because users can personalise 360 images and videos by inserting their own smartphone captured 3D objects. 3D object authoring is powered by a hybrid service that employs a mobile App to auto-select the most meaningful images from a smartphone’s camera-feed and then send then to a remote 3D reconstruction server [3, 7]. As new images arrive at the server, they are progressively added to an evolving 3D model, using Structure from Motion algorithms [9], Multi-View Stereo [6], Meshing [5] and Texturing [8] techniques. During 3D scanning, real-time user feedback is pushed back to the App, informing users about which parts of a scanned object still need attention. Finalised 3D models are then accessible through a user’s account using web RESTful APIs [4] for deployment flexibility. Such a client-server architecture produces high-quality 3D models without placing high resource demands a mobile device. Visitors to the stand will be encouraged to create their own 3D models from objects lying on a table. After 3D object creation, visitors will be encouraged to experiment with GamecastTV [1] to place/render their objects into user selected 360◦ scenes (coming either from pre-recorded sources or live streamed). Fig. 1 shows how 3D objects have been inserted into an innovative game. Finally, visitors will be encouraged to utilise the LiveSpaces [2] webased service to visualise and interact with their authored 360◦ scenescapes. Fig. 2 shows how digitalised real-world objects can be used inside 360◦ photo-spheres.
Application of the Czech Republic for Full Membership Report

This paper is an application by the Czech Republic for Full Membership of SCAR as an Initial Stage Program. The letter of application and an annex on scientific work undertaken and to be continued by the Czech Republic are provided. The Czech Republic adopted the Protocol on Environmental Protection to the Antarctic Treaty in 2004, and became a consultative party to the Antarctic Treaty in 2014 (acknowledging the prior status of Czechoslovakia since 1962).
Finite-difference time-domain modeling and experimental characterization of planar waveguide fluorescence sensors

The finite-difference time-domain method (FDTD) is a powerful numerical technique for solving Maxwell's equations in a discretized space and time grid. Its applications have up to now been in the analysis of electrically large structures in the microwave domain, and the scope of investigations has been extended only recently to the optical region. Because of computer memory limitations, the method is generally restricted to configurations which extend to the order of tens of wavelengths in three dimensions, or hundreds of wavelengths in two dimensions. Optical sensor structures are therefore of suitable size to be modeled with FDTD, and e.g., fluorescence sensor design can benefit from the use of FDTD in optimization of the waveguide structures. In general, the integration of chemical and optical design is difficult, but FDTD can bring the two design approaches closer together. One of the main advantages of FDTD is its ability to include near-field effects, such as distribution of protein molecules on the active surface of optical sensors in the model, which has been shown to be important is estimating the fluorescent excitation and collection efficiencies of molecules on surfaces. In addition, for planar structures, two-dimensional models are adequate for studying many aspects of sensor design. We applied FDTD to design planar fluorescence sensors. Excitation and emission models were analyzed for planar waveguide structures with side collection of emitted light in mind. Planar waveguides were fabricated on fused silica substrates and the characteristics of the waveguides were compared to the model. Good agreement was found with the FDTD modeling to the physical model, and based on this knowledge, an FDTD sensor model was prepared predicting good fluorescence excitation and emission side collection efficiencies.
3D reconstruction in your pocket

We present a pipeline to create digital 3D replicas of real-world objects using oﬀ-the-shelf smart-phones. Our methodology uses a hybrid approach where keyframes from a smartphone’s camera are automatically selected and sent to a server for 3D reconstruction. The smartphone periodically receives back 3D model updates for progress visualisation, thus users can concurrently and collaboratively perform acquisitions and speed up the reconstruction of large objects, e.g. buildings. We will show examples of 3D reconstructions performed by both individuals and groups.
Semantic Segmentation of Low Earth Orbit Satellites using Convolutional Neural Networks

Ground-based imaging of objects in Low Earth Orbit (LEO) is complicated by atmospheric turbulence, which make it difficult to identify key features or components on the object of interest. Many automated image reconstruction techniques are in use, but expert labor is needed to subjectively discern and identify truth features on a partially reconstructed image. In this paper, we present a deep learning approach for semantic segmentation of ground-based images of LEO objects. We investigate the performance under various atmospheric turbulence strengths in terms of the Fried parameter ($r_{0}$) and show the viability of this method.
Learning Implicit Environment Field

In this work, we propose a new representation for scenes dubbed as the environment ﬁeld that encodes the “reach-ing distance” – the distance between any position in the scene to a goal along a feasible trajectory. We demonstrate that this new representation can directly guide the dynamic behaviors of agents 1 inside the environment. We learn the environment ﬁeld using a neural implicit function from discretely sampled training data and showcase its application in human trajectory prediction in indoor environments and agent navigation in mazes. Furthermore, to generate semantically plausible trajectories for humans, we learn a
Evaluation of synthetic antimicrobial peptides for the control of mulberry brown leaf spot disease incited by Myrothecium roridum

Brown leaf spot of mulberry is caused by necrotrophic pathogen Myrothecium roridum (ascomycete) and results in the foliage loss (12-15%) during June-September in West Bengal. The management practice for brown leaf spot (BLS) includes application of dithio-carbamate fungicides; which are combative for user-health and the environment. The current study evaluates application of synthetic antimicrobial peptides (AMPs) as an alternative for BLS suppression. Eight AMPs (cecropin-mellitin derivatives) were screened initially for anti-M. roridum activity in vitro (0-72hrs at 24h interval). Four AMPs (<50μM) inhibited significant conidial growth suppression and the minimum inhibitory concentration (MIC50) recorded for most effective AMP (PRE-2) was ~23μM after 24h. LDH (lactate dehydrogenase) activity of Bombyx mori haemocytes treated with PRE-2 (50 to 175μM showed negligible cytotoxicity post-4h treatment: 0.68% at 50μM and 9.4% at 175μM. The effectiveness of PRE-2 (100μM) was assessed by spraying on BLS inflicted mulberry leaves ex vivo. Around 42% BLS suppression was recorded 20 days posttreatment. DNA isolated from PRE-2 treated and untreated samples infected with BLS were profiled with 7 SSRS and 5 ITS primers. ITS 1&4 and ITS 3&4 primer pairs revealed significant variation in band intensities between control and AMP treated samples.
Sustainable Food Sourcing in Higher Education: Definition and Goal-Setting for Duke University

Food production is one of the most impactful parts of human's footprint, both on our environment and our social structure. Knowing this, many colleges and universities are enacting sustainable purchasing guidelines and procurement goals in dining services to decrease the negative externalities of food consumption on campus. This study used semi-structured interviews, focus groups, and collection of material culture to evaluate Duke University's current sustainable food procurement initiatives and to research how peer institutions create and track progress towards sustainable food goals. Based on this research, I recommend a definition of sustainable food for Duke University in six product categories. I also give broad guidelines for " best practices " in setting and maintaining sustainable sourcing goals in dining services.
Factors Influencing College Students’ Intention To Shop Online

: T here are many benefits of e-commerce, however not all internet users ever conduct online shopping. The main purpose of this study is to explore the factors that influence college students’ intention to shop online. Theory of Planned Behavior (TPB) is often used as a basic model to examine how intention of buying online is formed. This study used TPB model which is integrated with Technological Acceptance Model (TAM). The model was analyzed using Partial Least Square (PLS) analysis. As for the data, as many as 150 respondents were analyzed. The respondents were college students who had never bought goods online. The results of the analysis suggest that only attitude proven to direct influence on Intention, while perceived ease of use and perceived usefulness were proven to influence Intention through attitude that acts as their mediator variable. Attitude gives the greatest influence to Intention.
Log periodic dipole arrays

A new class of coplanar dipole arrays is introduced. The antennas described provide unidirectional radiation patterns of constant beamwidth and nearly constant input impedances over any desired bandwidth. The broad-band properties are achieved by making use of the principles of log periodic antenna design. Models are discussed which are capable of providing 8- to 9-db directive gain with an associated input standing wave ratio of 1:2:1 on a 75-ohm feeder, and this performance is independent of frequency. The free-space properties of several of these arrays have been measured and the results are presented. The antenna configuration is simple, permitting practical methods of fabrication, and the design should prove useful in many applications. It makes possible, for example, the construction of "all-wave" rotatable beams of very low cross section for use in the hf to uhf spectrum.
DESIGN AND FABRICATION OF AN ELECTROMAGNETIC PISTON ENGINE

ABSTRACT The electromagnetic piston engine is a kind of reciprocating engine that works on the principle of electromagnetism i.e like poles attract and unlike poles repel each other. The engine consists of the same basic parts as that of a conventional engine, the piston, the connecting rod, the cylinder and the crankshaft. The electromagnet used in this paper is an AC solenoid magnet and the crankshaft, connecting rod are made of mild steel. The piston has a neodymium magnet attached to the top as this magnet is extremely powerful. When the magnet and the solenoid are of opposite poles, the piston attracts moving the solenoid from BDC to TDC. Once the solenoid changes poles and become the same pole as that of the magnet, it repels and pushes the piston from TDC to BDC. The engine built in this paper is a 4 cylinder V4 configuration engine. The results of this experiment and project is that as of now, this engine cannot be used to lift high power loads and cannot be used for large vehicular aspects. However, this engine can be improved in all aspects and with sustainable developments to the engine, This can soon be used as a viable alternative to the actual engine. The advantage of doing so is that companies will not have to change any existing designs and principals in both the planning and manufacturing phases. With just a few updates to their existing manufacturing lines, They can move to an all electric engine setup in the car as the future of automobiles lie in electric vehicles and this engine will help the companies reduce the costs of manufacturing and setup by a huge margin hence increasing profits.
Horseshoe and Strawderman-Berger Estimators for Non-negative Normal Means

High dimensional inference problems are generating considerable interest due to the availability and accessibility of massive amount of data in several fields. Modern statistical problems, however, involve natural constraints on model parameters. For such estimation problems, it is not fitting to apply standard estimates designed for unrestricted alternatives and then to truncate it. Given a n dimensional independent normal observation with common variance, we consider the classical normal mean estimation problem where the mean vector lies in a non-negative orthant. We study the behavior and risk properties of Bayesian estimators under two popular priors, the horseshoe prior and Strawderman-Berger prior, originally developed in the unrestricted mean vector estimation regime and then restrict the distribution of prior to satisfy the parameter constraint. The performance of posterior mean based on the horseshoe prior and the posterior mean and posterior median based on Strawderman-Berger prior is compared with the maximum likelihood estimator, numerically under different sparsity configurations.
A voltage and frequency droop control method for parallel inverters

In this paper, a new control method for the parallel operation of one or several inverters in an island grid or the mains is described. Frequency and voltage control, including mitigation of voltage harmonics, are achieved without the need for any common control circuitry or communication between the inverters. Each inverter supplies a current that is the result of the voltage difference between a reference AC voltage source and the grid voltage across a virtual impedance with real and/or imaginary parts. The reference AC voltage source is synchronised with the grid, with a phase shift, depending on the difference between nominal and real grid frequency. A detailed analysis show that this approach has superior behaviour in comparison with the existing droop control methods, considering the mitigation of voltage harmonics, short-circuit behaviour and, in the case of a non-negligible line resistance, the 'efficient' control of frequency and voltage. Experiments show the behaviour of the method for an inverter feeding a highly distorted load and during the connection of two parallel inverters in operation.
Collapse testing and analysis of a light-frame wood garage wall

AbstractLight-frame wood (woodframe) buildings have been tested at full-scale sparingly around the world, primarily due to the cost associated with such testing and a general lack of facilities large enough to test at system level. The data from the tests that have been performed are used to help develop nonlinear time history analysis models that can predict the behavior of woodframe buildings during strong ground shaking. It is difficult to test buildings to the point of collapse because such tests can damage costly test equipment, i.e., the shake table. This paper presents the results of a dynamic collapse test on a light-frame wood garage wall and the results of a numerical model to simulate the wall behavior all the way to the collapse point. The tested wall was heavily damaged as a result of subjecting it to a ground motion recorded during the 1995 Kobe earthquake. Then, 85% of a near-fault ground motion recording from the Northridge earthquake was used, which immediately collapsed the test specimen.
Clinical Evaluation of Acanthosis Nigricans and Its Correlation with Endocrine, Metabolic and Nutritional Factors in Gujarat,India

Acanthosis nigricans (AN) refers to the velvety, black hyperpigmentation in flexures usually on the neck and axillae. AN is associated with multiple endocrinopathies and insulin resistance is reported to be a common denominator among them. The aim of the study was to identify the correlation of metabolic factors (Serum glucose, lipid profile), endocrine factors (TSH, serum insulin), and nutritional factors (vitamin B12, Ferritin) in AN patients in Aravalli district of Gujarat state. A total of Seventeen Patients (11 male and 6 female) were included in the study History, clinical observations, physical parameters, and biochemical tests were noted in AN patients and correlation of some likely factors with AN was explored. The age was 28.41±15.09 years for AN group and 35.45±8.74 years for control group. The mean BMI was found to be higher in AN at 1 % level of significance (P = 0.007) implying that overweight/obesity is associated with AN. The mean TSH value was found to be higher in AN than control group at 5 % level of significance (P = 0.0115) implying that hypothyroidism (elevated TSH value) is associated with AN. The 45.45 % patients had higher blood glucose levels and some of the patients had higher level of insulin. The mean lipid profile, vitamin B12 and serum ferritin were found to be at non-significance level. All statistical tests were performed using graph pad prism software (version 5.0). Positive correlations were observed between higher fasting glucose and hyperinsulinemia with AN and others factors such as metabolic and endocrine like overweight/obesity, hypothyroidism, higher glucose and serum insulin with AN. This implies that AN can serve as a marker for detecting hyperinsulinemia and hypothyroidism. Some of the parameters like, Serum lipid profile, vitamin B12 and ferritin levels could not be correlated to AN. Thus, patients with AN can be targeted for lifestyle and behavioral modifications at an early stage to avoid the serious consequences of AN.
Genetic Suppression Meets Structure Prediction: Probing a Spore Germination Receptor Complex

Despite the thousands of spore germinant receptor operons identified in genomes of Bacilli and Clostridia, understanding how the three essential receptor components act as a signal transduction machine in germination remains limited. The paper by Amon et al. in this issue uses the classical genetic approach of suppression to define a region of likely interaction between the GerAA and GerAB proteins: it provides a first glimpse into potential events within the receptor complex. ABSTRACT Despite the thousands of spore germinant receptor operons identified in genomes of bacilli and clostridia, understanding how the three essential receptor components act as a signal transduction machine in germination remains limited. The paper by Amon et al. in this issue uses the classical genetic approach of suppression to define a region of likely interaction between the GerAA and GerAB proteins: it provides a first glimpse into potential events within the receptor complex (J. D. Amon, L. Artzi, and D. Z. Rudner, J Bacteriol 204:e00470-21, 2022, https://doi.org/10.1128/JB.00470-21).
A good reason for using OMP: average case results

In sparse approximation the goal is to approximate a given signal y ∈ R by a linear combination of a small number S d of elements φi ∈ R, called atoms, out of a given larger set, such as a basis or a frame, called the dictionary. Storing the normalised atoms as columns in the dictionary matrix Φ = (φ1 . . . , φK), and denoting the restriction to the columns indexed by a set I by ΦI , we can write informally,
Experimental validation of a simple, low-cost, T-junction droplet generator fabricated through 3D printing

Three-dimensional printing has been recently proposed and assessed for continuous flow microfluidic devices. In this paper the focus is on a new application of this rapid and low cost method for microfluidic device prototyping: droplets production through a T-junction generator. The feasibility of this new methodology is assessed by means of an experimental study in which the statistical parameters which characterize the production of droplets are analyzed. Furthermore, this study assesses the validity of previous theoretical and experimental results, obtained for a PDMS T-junction droplet generator, also in the case of a 3D printed Acrylonitrile microfluidic chip. Finally, the feasibility of producing monodisperse droplets by analyzing the polydispersity index of the prepared droplets is demonstrated.
Decoupling the Depth and Scope of Graph Neural Networks

State-of-the-art Graph Neural Networks (GNNs) have limited scalability with respect to the graph and model sizes. On large graphs, increasing the model depth often means exponential expansion of the scope (i.e., receptive field). Beyond just a few layers, two fundamental challenges emerge: 1. degraded expressivity due to oversmoothing, and 2. expensive computation due to neighborhood explosion. We propose a design principle to decouple the depth and scope of GNNs -- to generate representation of a target entity (i.e., a node or an edge), we first extract a localized subgraph as the bounded-size scope, and then apply a GNN of arbitrary depth on top of the subgraph. A properly extracted subgraph consists of a small number of critical neighbors, while excluding irrelevant ones. The GNN, no matter how deep it is, smooths the local neighborhood into informative representation rather than oversmoothing the global graph into"white noise". Theoretically, decoupling improves the GNN expressive power from the perspectives of graph signal processing (GCN), function approximation (GraphSAGE) and topological learning (GIN). Empirically, on seven graphs (with up to 110M nodes) and six backbone GNN architectures, our design achieves significant accuracy improvement with orders of magnitude reduction in computation and hardware cost.