Reviewerly/exHarmony huggingface dataset:
Assessing environmental and radiological impacts and lithological mapping of beryl-bearing rocks in Egypt using high-resolution sentinel-2 remote sensing images

Abstract Emerald and other beryls represent a family of the most valuable gemstone around the world and particularly in Egypt. Beryllium (Be) contents in beryl-bearing bedrocks in south Sinai (Wadi Ghazala and Wadi Sedri), and in central and south Eastern Desert of Egypt (Igla area, Zabara-Um Addebaa belt, Homret Akarem, and Homret Mukpid) were investigated in this study. The environmental risk levels of Be, associated major ions, and heavy metals in groundwater nearby to beryl-bearing mineralization were also evaluated. Results showed that Be contents ranged from 1 to 374 ppm in beryl-bearing bedrocks, while in nearby groundwater, Be content has a range of 0.0001–0.00044 mg/L with an average of 0.00032 mg/L, which is within the permissible levels and below (0.004) the U.S. EPA maximum contaminant level (MCL). Most levels of heavy metals (e.g., Be, B, Ni, V, Fe, and Al) in the investigated groundwater of central and south Eastern Desert and south Sinai are within the permissible levels and below their corresponding U.S. EPA MCLs. This study also investigated the radiological risk of natural radionuclides distributed in beryl-bearing bedrocks in the study area using gamma spectrometry; Sodium Iodide [NaI(Tl)] scintillation detector. Among the estimated mean 238 U, 232 Th, and 226 Ra activity concentrations of the studied beryl-bearing rocks, Homret Mukpid (79, 87.15, 60.26 Bq kg −1 ) and Homret Akarem (111.6, 51.17, 85.1 Bq kg −1 ) contain the highest values. This may be attributed to their highly fractionated granitic rocks that host uranium and thorium reservoir minerals such as zircon, allanite, and monazite. The estimated data of multi-radiological parameters such as absorbed gamma dose, outdoor and indoor annual effective dose, radium equivalent activity, internal and external indices, index of excess cancer, and effective dose to human organs reflecting no significant impacts from the emitted natural gamma radiation.
Language Models of Code are Few-Shot Commonsense Learners

We address the general task of structured commonsense reasoning: given a natural language input, the goal is to generate a graph such as an event or a reasoning-graph.To employ large language models (LMs) for this task, existing approaches "serialize" the output graph as a flat list of nodes and edges.Although feasible, these serialized graphs strongly deviate from the natural language corpora that LMs were pre-trained on, hindering LMs from generating them correctly.In this paper, we show that when we instead frame structured commonsense reasoning tasks as code generation tasks, pre-trained LMs of code are better structured commonsense reasoners than LMs of natural language, even when the downstream task does not involve source code at all.We demonstrate our approach across three diverse structured commonsense reasoning tasks.In all these natural language tasks, we show that using our approach, a code generation LM (CODEX) outperforms natural-LMs that are fine-tuned on the target task (e.g., T5) and other strong LMs such as GPT-3 in the few-shot setting.Our code and data are available at https: //github.com/madaan/CoCoGen .
DiaASQ: A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis

The rapid development of aspect-based sentiment analysis (ABSA) within recent decades shows great potential for real-world society.The current ABSA works, however, are mostly limited to the scenario of a single text piece, leaving the study in dialogue contexts unexplored.To bridge the gap between fine-grained sentiment analysis and conversational opinion mining, in this work, we introduce a novel task of conversational aspect-based sentiment quadruple analysis, namely DiaASQ, aiming to detect the quadruple of target-aspect-opinionsentiment in a dialogue.We manually construct a large-scale high-quality DiaASQ dataset in both Chinese and English languages.We deliberately develop a neural model to benchmark the task, which advances in effectively performing end-to-end quadruple prediction, and manages to incorporate rich dialogue-specific and discourse feature representations for better cross-utterance quadruple extraction.We hope the new benchmark will spur more advancements in the sentiment analysis community.
Evaluating Token-Level and Passage-Level Dense Retrieval Models for Math Information Retrieval

With the recent success of dense retrieval methods based on bi-encoders, studies have applied this approach to various interesting downstream retrieval tasks with good efficiency and in-domain effectiveness.Recently, we have also seen the presence of dense retrieval models in Math Information Retrieval (MIR) tasks, but the most effective systems remain classic retrieval methods that consider handcrafted structure features.In this work, we try to combine the best of both worlds: a welldefined structure search method for effective formula search and efficient bi-encoder dense retrieval models to capture contextual similarities.Specifically, we have evaluated two representative bi-encoder models for token-level and passage-level dense retrieval on recent MIR tasks.Our results show that bi-encoder models are highly complementary to existing structure search methods, and we are able to advance the state-of-the-art on MIR datasets.
Designs for research, teaching, and learning: A framework for future education

"Designs for research, teaching, and learning: A framework for future education." Innovations in Education and Teaching International, 60(5), pp. 795–796
EGFNet: Edge-Aware Guidance Fusion Network for RGB–Thermal Urban Scene Parsing

Urban scene parsing is the core of the intelligent transportation system, and RGB–thermal urban scene parsing has recently attracted increasing research interest in the field of computer vision. However, most existing approaches fail to perform good boundary extraction for prediction maps and cannot fully use high-level features. In addition, these methods simply fuse the features from RGB and thermal modalities but are unable to obtain comprehensive fused features. To address these problems, an edge-aware guidance fusion network (EGFNet) was developed in this study for RGB–thermal urban scene parsing. First, a prior edge map generated using the RGB and thermal images were introduced to capture detailed information in the prediction map and then embed the prior edge cues into the feature maps. To fuse the RGB and thermal information effectively, a multimodal fusion module was designed that guarantees adequate cross-modal fusion. Considering the importance of high-level semantic information, global and semantic information modules were proposed to extract rich semantic information from the high-level features. For decoding, simple elementwise addition was utilized for cascaded feature fusion. Finally, to improve the parsing accuracy, multitask deep supervision was applied to the semantic and boundary maps. Extensive experiments were performed on benchmark datasets to demonstrate the effectiveness of the proposed EGFNet and its superior performance compared with the state-of-the-art methods.
Purification-based quantum error mitigation of pair-correlated electron simulations

Abstract An important measure of the development of quantum computing platforms has been the simulation of increasingly complex physical systems. Before fault-tolerant quantum computing, robust error-mitigation strategies were necessary to continue this growth. Here, we validate recently introduced error-mitigation strategies that exploit the expectation that the ideal output of a quantum algorithm would be a pure state. We consider the task of simulating electron systems in the seniority-zero subspace where all electrons are paired with their opposite spin. This affords a computational stepping stone to a fully correlated model. We compare the performance of error mitigations on the basis of doubling quantum resources in time or in space on up to 20 qubits of a superconducting qubit quantum processor. We observe a reduction of error by one to two orders of magnitude below less sophisticated techniques such as postselection. We study how the gain from error mitigation scales with the system size and observe a polynomial suppression of error with increased resources. Extrapolation of our results indicates that substantial hardware improvements will be required for classically intractable variational chemistry simulations.
Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents

Large Language Models (LLMs) have demonstrated remarkable zero-shot generalization across various language-related tasks, including search engines. However, existing work utilizes the generative ability of LLMs for Information Retrieval (IR) rather than direct passage ranking. The discrepancy between the pre-training objectives of LLMs and the ranking objective poses another challenge. In this paper, we first investigate generative LLMs such as ChatGPT and GPT-4 for relevance ranking in IR. Surprisingly, our experiments reveal that properly instructed LLMs can deliver competitive, even superior results to state-of-the-art supervised methods on popular IR benchmarks. Furthermore, to address concerns about data contamination of LLMs, we collect a new test set called NovelEval, based on the latest knowledge and aiming to verify the model’s ability to rank unknown knowledge. Finally, to improve efficiency in real-world applications, we delve into the potential for distilling the ranking capabilities of ChatGPT into small specialized models using a permutation distillation scheme. Our evaluation results turn out that a distilled 440M model outperforms a 3B supervised model on the BEIR benchmark. The code to reproduce our results is available at www.github.com/sunnweiwei/RankGPT.
NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstruction

Recent methods for neural surface representation and rendering, for example NeuS [59], have demonstrated the remarkably high-quality reconstruction of static scenes. However, the training of NeuS takes an extremely long time (8 hours), which makes it almost impossible to apply them to dynamic scenes with thousands of frames. We propose a fast neural surface reconstruction approach, called NeuS2, which achieves two orders of magnitude improvement in terms of acceleration without compromising reconstruction quality. To accelerate the training process, we parameterize a neural surface representation by multi-resolution hash encodings and present a novel lightweight calculation of second-order derivatives tailored to our networks to leverage CUDA parallelism, achieving a factor two speed up. To further stabilize and expedite training, a progressive learning strategy is proposed to optimize multi-resolution hash encodings from coarse to fine. We extend our method for fast training of dynamic scenes, with a proposed incremental training strategy and a novel global transformation prediction component, which allow our method to handle challenging long sequences with large movements and deformations. Our experiments on various datasets demonstrate that NeuS2 significantly outperforms the state-of-the-arts in both surface reconstruction accuracy and training speed for both static and dynamic scenes. The code is available at our website: https://vcai.mpi-inf.mpg.de/projects/NeuS2/.
Plan-Based Character Diversity

Non-player character diversity enriches game environments increasing their replay value. We propose a method for obtaining character behavior diversity based on the diversity of plans enacted by characters, and demonstrate this method in a scenario in which characters have multiple choices. Using case-based planning techniques, we reuse plans for varied character behavior, which simulate different personality traits.
Computation of avoidance regions for driver assistance systems by using a Hamilton‐Jacobi approach

Summary We consider the problem of computing safety regions, modeled as nonconvex backward reachable sets, for a nonlinear car collision avoidance model with time‐dependent obstacles. The Hamilton‐Jacobi‐Bellman framework is used. A new formulation of level set functions for obstacle avoidance is given, and sufficient conditions for granting the obstacle avoidance on the whole time interval are obtained even though the conditions are checked only at discrete times. Different scenarios, including various road configurations, different geometry of vehicle and obstacles, as well as fixed or moving obstacles, are then studied and computed. Computations involve solving nonlinear partial differential equations of up to five space dimensions plus time with nonsmooth obstacle representations, and an efficient solver is used to this end. A comparison with a direct optimal control approach is also done for one of the examples.
Reformulating Temporal Action Logics in Answer Set Programming

Temporal Action Logics (TAL) is a class of temporal logics for reasoning about actions. We present a reformulation of TAL in Answer Set Programming (ASP), and discuss some synergies it brings. First, the reformulation provides a means to compute TAL using efficient answer set solvers. Second, TAL provides a structured high-level language for ASP (possibly with constraint solving). Third, the reformulation allows us to compute integration of TAL and ontologies using answer set solvers, and we illustrate its usefulness in the healthcare domain in the context of medical expert systems.
A Methodology For Assigning Project Teams

Why do we use team based projects when we teach?In part it is because we want students to apply new knowledge beyond structured homework problems.The benefit of working in a team is that each team member can contribute towards a common goal and achieve more than one could individually.And, after graduation, our students will work as part of a team on many projects.How are student teams usually made?When the students are allowed to group themselves the results are clusters of friends, interspersed with human filler.When grouped by instructor the teams can be designed to distribute strengths and weaknesses across all teams evenly to ensure even chances of success.Methods to categorize strengths and weaknesses can be as simple as grades 1 , or as complex as personality indicators.When personality indicators are used the teams are designed to have complimentary personalities.What makes a team successful or fail?A team that functions well will approach a task and produce strong results without 'issues'.Some teams will be pulled off track by conflicts with an individual and produce sub-optimal solutions.Teams that are largely non-functional will simply fail because of widespread personality conflicts.Essentially, conflicts impact the team performance.Issues that tend to lead to major conflicts on student teams include but are not limited to a lack of motivation, lack of talent, working too much, and abrasive personalities.The topics of personality and conflicts must be dealt with separately when forming teams, with conflicts being given the higher priority.This paper describes a method for forming project teams that can be copied or modified for use in other courses.In simple terms the method can be described as "Group students on teams with similar challenges, and everybody will benefit".
Interpersonal video communication as a site of human sociality

Preview this article: Interpersonal video communication as a site of human sociality, Page 1 of 1 < Previous page | Next page > /docserver/preview/fulltext/prag.27.3.01har-1.gif
3D Visualization of Brain Tumors Using MR Images: A Survey

Background: Three-Dimensional visualization of brain tumors is very useful in both diagnosis and treatment stages of brain cancer. Discussion: It helps the oncologist/neurosurgeon to take the best decision in Radiotherapy and/or surgical resection techniques. 3D visualization involves two main steps; tumor segmentation and 3D modeling. Conclusion: In this article, we illustrate the most widely used segmentation and 3D modeling techniques for brain tumors visualization. We also survey the public databases available for evaluation of the mentioned techniques.
Wave-shaped round functions and primitive groups

Round functions used as building blocks for iterated block ciphers, both in the case of Substitution-Permutation Networks (SPN) and Feistel Networks (FN), are often obtained as the composition of different layers. The bijectivity of any encryption function is guaranteed by the use of invertible layers or by the Feistel structure. In this work a new family of ciphers, called wave ciphers, is introduced. In wave ciphers, round functions feature wave functions, which are vectorial Boolean functions obtained as the composition of non-invertible layers, where the confusion layer enlarges the message which returns to its original size after the diffusion layer is applied. Efficient decryption is guaranteed by the use of wave functions in FNs. It is shown how to avoid that the group generated by the round functions acts imprimitively, a serious flaw for the cipher. The primitivity is a consequence of a more general result, which reduce the problem of proving that a given FN generates a primitive group to proving that an SPN, directly related to the given FN, generates a primitive group. Finally, a concrete instance of real-world size wave cipher is proposed as an example, and its resistance against differential and linear cryptanalyses is also established.
Handwritten Arabic Documents Segmentation into Text Lines using Seam Carving

Inspired from human perception and common text documents characteristics based on readability constraints, an Arabic text line segmentation approach is proposed using seam carving.Taking the gray scale of the image as input data, this technique offers better results at extracting handwritten text lines without the need for the binary representation of the document image.In addition to its fast processing time, its versatility permits to process a multitude of document types, especially documents presenting low text-to-background contrast such as degraded historical manuscripts or complex writing styles like cursive handwriting.Even if our focus in this paper was on Arabic text segmentation, this method is language independent.Tests on a public database of 123 handwritten Arabic documents showed a line detection rate of 97.5% for a matching score of 90%.
The Bordiga surface as critical locus for 3-view reconstructions

In Computer Vision, images of dynamic or segmented scenes are modeled as linear projections from Pk to P2. The reconstruction problem consists in recovering the position of the projected objects and the projections themselves from their images, after identifying many enough correspondences between the images. A critical locus for the reconstruction problem is a variety in Pk containing the objects for which the reconstruction fails. In this paper, we deal with projections both of points from P4 to P2 and of lines from P3 to P2. In both cases, we consider 3 projections, minimal number for a uniquely determined reconstruction. In the case of projections of points, we declinate the Grassmann tensors introduced in Hartley and Schaffalitzky (2004) in our context, and we use them to compute the equations of the critical locus. Then, given the ideal that defines this locus, we prove that, in the general case, it defines a Bordiga surface, or a scheme in the same irreducible component of the associated Hilbert scheme. Furthermore, we prove that every Bordiga surface is actually the critical locus for the reconstruction for suitable projections. In the case of projections of lines, we compute the defining ideal of the critical locus, that is the union of 3 α-planes and a line congruence of bi-degree (3,6) and sectional genus 5 in the Grassmannian G(1,3)⊂P5. This last surface is biregular to a Bordiga surface (Verra, 1988). We use this fact to link the two reconstruction problems by showing how to compute the projections of one of the two settings, given the projections of the other one. The link is effective, in the sense that we describe an algorithm to compute the projection matrices.
QR Based Despeckling Approach for Medical Ultrasound Images

Background: An approach based on QR decomposition, to remove speckle noise from medical ultrasound images, is presented in this paper. Methods: The speckle noisy image is segmented into small overlapping blocks. A global covariance matrix is calculated by averaging the corresponding covariances of the blocks. QR decomposition is applied to the global covariance matrix. To filter out speckle noise, the first subset of orthogonal vectors of the Q matrix is projected onto the signal subspace. The proposed approach is compared with five benchmark techniques; Homomorphic Wavelet Despeckling (HWDS), Speckle Reducing Anisotropic Diffusion (SRAD), Frost, Kuan and Probabilistic Non-Local Mean (PNLM). Results and Conclusion: When applied to different simulated and real ultrasound images, the QR based approach has secured maximum despeckling performance while maintaining optimal resolution and edge detection, and that is regardless of image size or nature of speckle; fine or rough.
Double images encryption in optical image subtraction/addition 4F system

Abstract In this paper, double images encrypted method in the 4f system of image subtraction/addition is proposed. Encryption and decryption are implemented in the same system of image subtraction/addition while the encryption and decryption of two images are once addition and subtraction process of images respectively which will improve the feasibility of implementation in actual optics. So this method has high practicality and utilization ratio with high information processing speed. Furthermore, system parameters of the configuration can also serve as additional keys for encryption and the two images are mutual keys, so the security performance of the encryption system is also improved. Numerical simulation results demonstrate the feasibility and flexibility of the proposed method.
Decoupled Intensity-Based Nonmetric Visual Servo Control

This brief addresses the problem of vision-based robot control where the equilibrium state is defined via a goal image. Specifically, we consider the class of intensity-based nonmetric solutions, which provide for high accuracy, versatility, and robustness. Existing techniques within that class present a fully coupled translational and rotational control error dynamics, what increases analysis complexity and may degrade system performance. This brief proposes a new intensity-based nonmetric visual servoing technique that decouples the translational control error dynamics, regardless of the observed object characteristics, camera displacements, and their relative poses. The obtained system is thus lower triangular in the general case. For some practical cases, the proposed general technique leads to the Grail of a fully decoupled (i.e., strictly diagonal) linear dynamics. The theoretical analysis of local diffeomorphism, local exponential stability, and those decoupling properties is provided. Improved performances are also experimentally confirmed using a 6-DoF robotic arm in various positioning and tracking tasks.