Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Analysis of Self-Collimation-Based Photonic Crystal Beam Splitter

We present a self-collimation-based beam splitter in a two-dimensional photonic crystal (2D-PC) by introducing defects near the termination. From the equi-frequency contour (EFC) calculations and the finite-difference time-domain (FDTD) simulations, we show that the defects can give rise to the splitting of self-collimated beams in 2D-PCs and the directivity of the deflected beam can be improved by the defect along the PC surface. In order to get different kinds of beam splitters, including the Y-shaped, one-to-three, one-to-four structures, and so on, we only need to modify the structure of the output surface (along X-M direction). The proposed splitter may have practical applications in integrated photonic circuits.
Genetic study of stock structure in North Pacific sei whales

The Institute of Cetacean Research has conducted genetic analyses of stock structure in North Pacific sei whales based on historical genetic samples, and samples collected more recently by the IWC-POWER (biopsy samples) and JARPNII program. Results have contributed to the in-depth assessment of North Pacific sei whales conducted by the IWC Scientific Committee. This paper presents details of the research area, samples, genetic approaches, and main results of the genetic analyses of stock structure of sei whales in the pelagic area of the North Pacific. Results of the genetic analyses are consistent with the hypothesis of a single stock of sei whales in the pelagic area of the North Pacific surveyed (140°E–130°W).
Learning Comment Topics from Code

In this paper, we describe the method of learning comment topics against corresponding code fragments in order to generate topics for the source code that does not contain any documentation or comment. Topics are particular distributions over vocabularies that describe or reveal the meaning or intention of the code. The built system might be helpful for searching code techniques as well as for code classification. The major components of our system are: 1. topic models learned from comments that convert comments into topics; 2. code analyzer and encoder that extract information from codes; 3. recurrent neural networks that learn to predict topics from low-level instruction sequences. The results we obtained are somewhat promising, but cannot be stated as very successful. We spare some of our most interesting unexplored ideas in the future work part, which we believe could work well for the aimed task.
New Method for Calibration of Horizontal Fuel Tanks

Nowadays, when price of the fuel increases rapidly each day, the accurate control of the fuel tanks in the fuel stations becomes of a great importance. The underground horizontal fuel tanks in the stations can be easily deformed during the installation or affected by geological or other reasons, thus full volume and volume chart of the tank can be corrupted. Under these circumstances the recalibration of the tank volume chart becomes very important. For the calibration of horizontal fuel tanks in Lithuania the liquid filled method is used. In this article a new method, based on inside 3D laser scans of horizontal fuel tank and advanced data processing, was presented. DOI: http://dx.doi. org/10.5755/j01.eee. 18.9. 2816
RDF-OWL based recommender for videos and books of future electronic libraries

—This paper proposes an architecture of components as well as proposed techniques/technologies for a future electronic library semantic based recommender system, this system will be using RDF-OWL technology to be used for multimedia recommendation of videos and books. To analyze videos, a first item enrichment approach aims at using video dialogue translated into text and process them through semantic enrichment methods to extract concepts to allow consistent recommendation between videos and books. This paper presents how linked data concepts could be used to enrich textual and video media information using linked data content from open repositories.
Rational Approach to Prediction of Shear Capacity of RC Beam-Column Elements

This paper presents a new predictive formula for the shear capacity evaluation of reinforced concrete members subjected to combined axial, bending, and shear forces. The formula is based on a tensorial approach to concrete shear resistance that is very similar to the one used in the shear-enhanced fiber beam element. The total shear resistance is broken down into the contribution of concrete and contribution of shear reinforcement. The concrete contribution to the shear resistance is calculated using a normal-shear stress failure envelope. Normal (longitudinal) stresses are calculated from axial and bending forces acting on the concrete member. In the formulation, a number of simplifications are made to keep the formula as simple as possible but still sufficiently accurate. The resulting formulation, although capable of accounting for all of the major variables that influence the shear strength, including size effect, remains particularly simple and with a compact notation. The predictions of the proposed formula are compared with those used by the American Concrete Institute (ACI), the European Committee for Standardization (CEN) Eurocode, and the International Federation for Structural Concrete (fib) model code, and its accuracy is checked against a vast experimental database available in the literature. Results and comparisons are very encouraging and confirm the soundness of the underlying mechanical model. The capability of this model to provide a unified approach for reinforced and unreinforced members opens up the possibility to extend the application of the proposed formula to engineered cementitious composites, such as fiber-reinforced concrete.
Off-Policy Actor-Critic with Emphatic Weightings

A variety of theoretically-sound policy gradient algorithms exist for the on-policy setting due to the policy gradient theorem, which provides a simplified form for the gradient. The off-policy setting, however, has been less clear due to the existence of multiple objectives and the lack of an explicit off-policy policy gradient theorem. In this work, we unify these objectives into one off-policy objective, and provide a policy gradient theorem for this unified objective. The derivation involves emphatic weightings and interest functions. We show multiple strategies to approximate the gradients, in an algorithm called Actor Critic with Emphatic weightings (ACE). We prove in a counterexample that previous (semi-gradient) off-policy actor-critic methods--particularly Off-Policy Actor-Critic (OffPAC) and Deterministic Policy Gradient (DPG)--converge to the wrong solution whereas ACE finds the optimal solution. We also highlight why these semi-gradient approaches can still perform well in practice, suggesting strategies for variance reduction in ACE. We empirically study several variants of ACE on two classic control environments and an image-based environment designed to illustrate the tradeoffs made by each gradient approximation. We find that by approximating the emphatic weightings directly, ACE performs as well as or better than OffPAC in all settings tested.
Coherent elastic neutrino-nucleus scattering (
CEνNS
) event rates for Ge, Zn, and Si detector materials

Realistic nuclear structure calculations are presented for the event rates due to coherent elastic neutrino-nucleus scattering (CEνNS), assuming neutrinos from pion-decay at-rest, from nuclear reactors and from Earth’s interior. We focus on the currently interesting Germanium isotopes, 70,73,76Ge, which constitute detector materials of the recently planned CEνNS experiments. We study in addition the potential use of 64,70Zn and 28Si isotopes as promising CEνNS detectors. From nuclear physics perspectives, recently, calculations have been carried out within the framework of the deformed shell-model (DSM), based on realistic nuclear forces, and assessed on the reproducibility of spectroscopic nuclear properties. The high confidence level acquired by their agreement with experimental results and by their comparison with other mostly phenomenological calculations encouraged the use of DSM to extract predictions for the CEνNS event rates of the above isotopes. Our detailed estimation of the nuclear physics aspects of the recently observed neutral current coherent neutrino-nucleus scattering may shed light on unravelling the still remaining uncertainties for the CEνNS process within and beyond the Standard Model.
Project CGX: Scalable Deep Learning on Commodity GPUs

The ability to scale out training workloads has been one of the key performance enablers of deep learning. The main scaling approach is data-parallel GPU-based training, which has been boosted by hardware and software support for highly efficient inter-GPU communication, in particular via bandwidth overprovisioning. This support comes at a price: there is an order of magnitude cost difference between “cloud-grade” servers with such support, relative to their “consumer-grade” counterparts, although server-grade and consumer-grade GPUs can have similar computational envelopes. In this paper, we investigate whether the expensive hardware overprovisioning approach can be supplanted via algorithmic and system design, and propose a framework called CGX, which provides efficient software support for communication compression. We show that this framework is able to remove communication bottlenecks from consumer-grade multi-GPU systems, in the absence of hardware support: when training modern models and tasks to full accuracy, our framework enables self-speedups of 2-3X on a commodity system using 8 consumer-grade NVIDIA RTX 3090 GPUs, and enables it to surpass the throughput of an NVIDIA DGX-1 server, which has similar peak FLOPS but benefits from bandwidth overprovisioning.
Non-separable Spatio-temporal Graph Kernels via SPDEs

Gaussian processes (GPs) provide a principled and direct approach for inference and learning on graphs. However, the lack of justified graph kernels for spatio-temporal modelling has held back their use in graph problems. We leverage an explicit link between stochastic partial differential equations (SPDEs) and GPs on graphs, introduce a framework for deriving graph kernels via SPDEs, and derive non-separable spatio-temporal graph kernels that capture interaction across space and time. We formulate the graph kernels for the stochastic heat equation and wave equation. We show that by providing novel tools for spatio-temporal GP modelling on graphs, we outperform pre-existing graph kernels in real-world applications that feature diffusion, oscillation, and other complicated interactions.
Neuro-Hotnet: A Graph Theoretic Approach for Brain FC Estimation

There has been increasing interest in the potential of multi-modal imaging to obtain more robust estimates of Functional Connectivity (FC) in high-dimensional settings. We develop novel algorithms adapting graphical methods incorporating diffusion tensor imaging (DTI) and statistically rigorous control to FC estimation with computational efficiency and scalability. Our proposed algorithm leverages a graphical random walk on DTI data to define a new measure of structural influence that highlights connected components of interest. We then test for minimum sub-network size and find the subnetwork topology using permutation testing before the discovered components are tested for significance. Extensive simulations demonstrate that our method has comparable power to other currently used methods, with the advantage of greater speed, equal or more robustness, and simple implementation. To verify our approach, we analyze task-based fMRI data obtained from the Human Connectome Project database, which reveal novel insights into brain interactions during performance of a motor task. We expect that the transparency and flexibility of our approach will prove valuable as further understanding of the structure-function relationship informs the future of network estimation. Scalability will also only become more important as neurological data become more granular and grow in dimension.
Pose Recognition in the Wild: Animal pose estimation using Agglomerative Clustering and Contrastive Learning

Animal pose estimation has recently come into the limelight due to its application in biology, zoology, and aquaculture. Deep learning methods have effectively been applied to human pose estimation. However, the major bottleneck to the application of these methods to animal pose estimation is the unavailability of sufficient quantities of labeled data. Though there are ample quantities of unlabelled data publicly available, it is economically impractical to label large quantities of data for each animal. In addition, due to the wide variety of body shapes in the animal kingdom, the transfer of knowledge across domains is ineffective. Given the fact that the human brain is able to recognize animal pose without requiring large amounts of labeled data, it is only reasonable that we exploit unsupervised learning to tackle the problem of animal pose recognition from the available, unlabelled data. In this paper, we introduce a novel architecture that is able to recognize the pose of multiple animals fromunlabelled data. We do this by (1) removing background information from each image and employing an edge detection algorithm on the body of the animal, (2) Tracking motion of the edge pixels and performing agglomerative clustering to segment body parts, (3) employing contrastive learning to discourage grouping of distant body parts together. Hence we are able to distinguish between body parts of the animal, based on their visual behavior, instead of the underlying anatomy. Thus, we are able to achieve a more effective classification of the data than their human-labeled counterparts. We test our model on the TigDog and WLD (WildLife Documentary) datasets, where we outperform state-of-the-art approaches by a significant margin. We also study the performance of our model on other public data to demonstrate the generalization ability of our model.
Automatically detecting anomalous exoplanet transits

Raw light curve data from exoplanet transits is too complex to naively apply traditional outlier detection methods. We propose an architecture which estimates a latent representation of both the main transit and residual deviations with a pair of variational autoencoders. We show, using two fabricated datasets, that our latent representations of anomalous transit residuals are significantly more amenable to outlier detection than raw data or the latent representation of a traditional variational autoencoder. We then apply our method to real exoplanet transit data. Our study is the first which automatically identifies anomalous exoplanet transit light curves. We additionally release three first-of-their-kind datasets to enable further research.
On the origin and the detection of characteristic axion wiggles in photon spectra

Photons propagating in an external magnetic field may oscillate into axions or axion-like particles (ALPs). Such oscillations will lead to characteristic features in the energy spectrum of high-energy photons from astrophysical sources that can be used to probe the existence of ALPs. In this work, we revisit the signatures of these oscillations and stress the importance of a proper treatment of turbulent magnetic fields. We implement axions into ELMAG, a standard tool for modelling in a Monte Carlo framework the propagation of gamma-rays in the Universe, complementing thereby the usual description of photon-axion oscillations with a Monte Carlo treatment of high-energy photon propagation and interactions. We also propose an alternative method of detecting axions through the discrete power spectrum using as observable the energy dependence of wiggles in the photon spectra.
Engineering Edge-Cloud Offloading of Big Data for Channel Modelling in THz-range Communications

Channel estimation in mmWave and THz-range wireless communications (producing Gb/Tb-range of data) is critical to configuring system parameters related to transmission signal quality, and yet it remains a daunting challenge both in software and hardware. Current methods of channel estimations, be it modeling- or data-based (machine learning (ML)), - use and create big data. This in turn requires a large amount of computational resources, read operations to prove if there is some predefined channel configurations, e.g., QoS requirements, in the database, as well as write operations to store the new combinations of QoS parameters in the database. Especially the ML-based approach requires high computational and storage resources, low latency and a higher hardware flexibility. In this paper, we engineer and study the offloading of the above operations to edge and cloud computing systems to understand the suitability of edge and cloud computing to provide rapid response with channel and link configuration parameters on the example of THz channel modeling. We evaluate the performance of the engineered system when the computational and storage resources are orchestrated based on: 1) monolithic architecture, 2) microservices architectures, both in edge-cloud based approach. For microservices approach, we engineer both Docker Swarm and Kubernetes systems. The measurements show a great promise of edge computing and microservices that can quickly respond to properly configure parameters and improve transmission distance and signal quality with ultra-high speed wireless communications.
On Effective Scheduling of Model-based Reinforcement Learning

Model-based reinforcement learning has attracted wide attention due to its su-perior sample efﬁciency. Despite its impressive success so far, it is still unclear how to appropriately schedule the important hyperparameters to achieve adequate performance, such as the real data ratio for policy optimization in Dyna-style model-based algorithms. In this paper, we ﬁrst theoretically analyze the role of real data in policy training, which suggests that gradually increasing the ratio of real data yields better performance. Inspired by the analysis, we propose a framework named AutoMBPO to automatically schedule the real data ratio as well as other hyperparameters in training model-based policy optimization (MBPO) algorithm, a representative running case of model-based methods. On several continuous control tasks, the MBPO instance trained with hyperparameters scheduled by AutoMBPO can signiﬁcantly surpass the original one, and the real data ratio schedule found by AutoMBPO shows consistency with our theoretical analysis.
Generative Pre-Trained Transformer for Design Concept Generation: An Exploration

Abstract Novel concepts are essential for design innovation and can be generated with the aid of data stimuli and computers. However, current generative design algorithms focus on diagrammatic or spatial concepts that are either too abstract to understand or too detailed for early phase design exploration. This paper explores the uses of generative pre-trained transformers (GPT) for natural language design concept generation. Our experiments involve the use of GPT-2 and GPT-3 for different creative reasonings in design tasks. Both show reasonably good performance for verbal design concept generation.
A Survey on Task Assignment in Crowdsourcing

Quality improvement methods are essential to gathering high-quality crowdsourced data, both for research and industry applications. A popular and broadly applicable method is task assignment that dynamically adjusts crowd workflow parameters. In this survey, we review task assignment methods that address: heterogeneous task assignment, question assignment, and plurality problems in crowdsourcing. We discuss and contrast how these methods estimate worker performance, and highlight potential challenges in their implementation. Finally, we discuss future research directions for task assignment methods, and how crowdsourcing platforms and other stakeholders can benefit from them.
Faster-Than-Nyquist Asynchronous NOMA Outperforms Synchronous NOMA

Faster-than-Nyquist (FTN) signaling aided non-orthogonal multiple access (NOMA) is conceived and its achievable rate is quantified in the presence of <italic>random</italic> link delays of the different users. We reveal that exploiting the link delays may potentially lead to a signal-to-interference-plus-noise ratio (SINR) gain, while transmitting the data symbols at FTN rates has the potential of increasing the degree-of-freedom (DoF). We then unveil the fundamental trade-off between the SINR and DoF. In particular, at a sufficiently high symbol rate, the SINR gain vanishes while the DoF gain achieves its maximum, where the achievable rate is almost <inline-formula> <tex-math notation="LaTeX">$(1+\beta)$ </tex-math></inline-formula> times higher than that of the conventional synchronous NOMA transmission in the high signal-to-noise ratio (SNR) regime, with <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> being the roll-off factor of the signaling pulse. Our simulation results verify our analysis and demonstrate considerable rate improvements over the conventional power-domain NOMA scheme.
Solving Linear Algebra by Program Synthesis

We solve MIT's Linear Algebra 18.06 course and Columbia University's Computational Linear Algebra COMS3251 courses with perfect accuracy by interactive program synthesis. This surprisingly strong result is achieved by turning the course questions into programming tasks and then running the programs to produce the correct answers. We use OpenAI Codex with zero-shot learning, without providing any examples in the prompts, to synthesize code from questions. We quantify the difference between the original question text and the transformed question text that yields a correct answer. Since all COMS3251 questions are not available online the model is not overfitting. We go beyond just generating code for questions with numerical answers by interactively generating code that also results visually pleasing plots as output. Finally, we automatically generate new questions given a few sample questions which may be used as new course content. This work is a significant step forward in solving quantitative math problems and opens the door for solving many university level STEM courses by machine.
Enabling Equivariance for Arbitrary Lie Groups

Although provably robust to translational perturbations, convolutional neural networks (CNNs) are known to suffer from extreme performance degradation when presented at test time with more general geometric transformations of inputs. Recently, this limitation has motivated a shift infocus from CNNs to Capsule Networks (CapsNets). However, CapsNets suffer from admitting relatively few theoretical guarantees of invariance. We introduce a rigourous mathematical framework to permit invariance to any Lie group of warps, exclusively using convolutions (over Lie groups), without the need for capsules. Previous work on group convolutions has been hampered by strong assumptions about the group, which precludes the application of such techniques to common warps in computer vision such as affine and homographic. Our framework enables the implementation of group convolutions over any finite-dimensional Lie group. We empirically validate our approach on the benchmark affine-invariant classification task, where we achieve ~30% improvement in accuracy against conventional CNNs while outperforming most CapsNets. As further illustration of the generality of our framework, we train a homography-convolutional model which achieves superior robustness on a homography-perturbed dataset, where CapsNet results degrade.
IKEA Object State Dataset: A 6DoF object pose estimation dataset and benchmark for multi-state assembly objects

Utilizing 6DoF(Degrees of Freedom) pose information of an object and its components is critical for object state detection tasks. We present IKEA Object State Dataset, a new dataset that contains IKEA furniture 3D models, RGBD video of the assembly process, the 6DoF pose of furniture parts and their bounding box. The proposed dataset will be available at https: // github. com/ mxllmx/ IKEAObjectStateDataset .