Reviewerly/exHarmony huggingface dataset:
On the Noise Scheduling for Generating Plausible Designs with Diffusion Models

Deep Generative Models (DGMs) are widely used to create innovative designs across multiple industries, ranging from fashion to the automotive sector. In addition to generating images of high visual quality, the task of structural design generation imposes more stringent constrains on the semantic expression, e.g., no floating material or missing part, which we refer to as plausibility in this work. We delve into the impact of noise schedules of diffusion models on the plausibility of the outcome: there exists a range of noise levels at which the model's performance decides the result plausibility. Also, we propose two techniques to determine such a range for a given image set and devise a novel parametric noise schedule for better plausibility. We apply this noise schedule to the training and sampling of the well-known diffusion model EDM and compare it to its default noise schedule. Compared to EDM, our schedule significantly improves the rate of plausible designs from 83.4% to 93.5% and Fr\'echet Inception Distance (FID) from 7.84 to 4.87. Further applications of advanced image editing tools demonstrate the model's solid understanding of structure.
GaussianDiffusion: 3D Gaussian Splatting for Denoising Diffusion Probabilistic Models with Structured Noise

Text-to-3D, known for its efficient generation methods and expansive creative potential, has garnered significant attention in the AIGC domain. However, the amalgamation of Nerf and 2D diffusion models frequently yields oversaturated images, posing severe limitations on downstream industrial applications due to the constraints of pixelwise rendering method. Gaussian splatting has recently superseded the traditional pointwise sampling technique prevalent in NeRF-based methodologies, revolutionizing various aspects of 3D reconstruction. This paper introduces a novel text to 3D content generation framework based on Gaussian splatting, enabling fine control over image saturation through individual Gaussian sphere transparencies, thereby producing more realistic images. The challenge of achieving multi-view consistency in 3D generation significantly impedes modeling complexity and accuracy. Taking inspiration from SJC, we explore employing multi-view noise distributions to perturb images generated by 3D Gaussian splatting, aiming to rectify inconsistencies in multi-view geometry. We ingeniously devise an efficient method to generate noise that produces Gaussian noise from diverse viewpoints, all originating from a shared noise source. Furthermore, vanilla 3D Gaussian-based generation tends to trap models in local minima, causing artifacts like floaters, burrs, or proliferative elements. To mitigate these issues, we propose the variational Gaussian splatting technique to enhance the quality and stability of 3D appearance. To our knowledge, our approach represents the first comprehensive utilization of Gaussian splatting across the entire spectrum of 3D content generation processes.
ChatGPT at the Speed of Light: Optical Comb-Based Monolithic Photonic-Electronic Linear-Algebra Accelerators

This paper proposes to adopt advanced monolithic silicon-photonics integrated-circuits manufacturing capabilities to achieve a system-on-chip photonic-electronic linear-algebra accelerator with the features of optical comb-based broadband incoherent photo-detections and high-dimensional operations of consecutive matrix-matrix multiplications to enable substantial leaps in computation density and energy efficiency, with practical considerations of power/area overhead due to photonic-electronic on-chip conversions, integrations, and calibrations through holistic co-design approaches to support attention-head mechanism based deep-learning neural networks used in Large Language Models and other emergent applications.
OperARtistry: An AR-based Interactive Application to Assist the Learning of Chinese Traditional Opera (Xiqu) Makeup

Chinese Traditional Opera (Xiqu) is an important type of intangible cultural heritage and one key characteristic of Xiqu is its visual effects on face achieved via makeup. However, Xiqu makeup process, especially the eye-area makeup process, is complex and time-consuming, which poses a learning challenge for potential younger inheritors. We introduce OperARtistry, an interactive application based on Augmented Reality (AR) that offers in-situ Xiqu makeup guidance for beginners. Our application provides a step-by-step guide for Xiqu eye-area makeup, incorporating AR effects at each stage. Furthermore, we conducted an initial user study (n=6) to compare our approach with existing video-based tutorials to assess the effectiveness and usefulness of our approach. Our findings show that OperARtisty helped participants achieve high-quality eye-area makeup effects with less learning time.
Evidential Uncertainty Quantification: A Variance-Based Perspective

Uncertainty quantification of deep neural networks has become an active field of research and plays a crucial role in various downstream tasks such as active learning. Recent advances in evidential deep learning shed light on the direct quantification of aleatoric and epistemic uncertainties with a single forward pass of the model. Most traditional approaches adopt an entropy-based method to derive evidential uncertainty in classification, quantifying uncertainty at the sample level. However, the variance-based method that has been widely applied in regression problems is seldom used in the classification setting. In this work, we adapt the variance-based approach from regression to classification, quantifying classification uncertainty at the class level. The variance decomposition technique in regression is extended to class covariance decomposition in classification based on the law of total covariance, and the class correlation is also derived from the covariance. Experiments on cross-domain datasets are conducted to illustrate that the variance-based approach not only results in similar accuracy as the entropy-based one in active domain adaptation but also brings information about class-wise uncertainties as well as between-class correlations. The code is available at https://github.com/KerryDRX/EvidentialADA. This alternative means of evidential uncertainty quantification will give researchers more options when class uncertainties and correlations are important in their applications.
Multicritical dissipative phase transitions in the anisotropic open quantum Rabi model

We investigate the nonequilibrium steady state of the anisotropic open quantum Rabi model, which exhibits first-order and second-order dissipative phase transitions upon varying the degree of anisotropy between the coupling strengths of rotating and counterrotating terms. Using both semiclassical and quantum approaches, we find a rich phase diagram resulting from the interplay between the anisotropy and the dissipation. First, there exists a bistable phase where both the normal and superradiant phases are stable. Second, there are multicritical points where the phase boundaries for the first- and second-order phase transitions meet. We show that a new set of critical exponents governs the scaling of the multicritical points. Finally, we discuss the feasibility of observing the multicritical transitions and bistability using a pair of trapped ions where the anisotropy can be tuned by the controlling the intensity of the Raman transitions. Our study enlarges the scope of critical phenomena that may occur in finite-component quantum systems, which could be useful for the applications in the critical quantum sensing.
Inspecting Explainability of Transformer Models with Additional Statistical Information

Transformer becomes more popular in the vision domain in recent years so there is a need for finding an effective way to interpret the Transformer model by visualizing it. In recent work, Chefer et al. can visualize the Transformer on vision and multi-modal tasks effectively by combining attention layers to show the importance of each image patch. However, when applying to other variants of Transformer such as the Swin Transformer, this method can not focus on the predicted object. Our method, by considering the statistics of tokens in layer normalization layers, shows a great ability to interpret the explainability of Swin Transformer and ViT.
ML-LMCL: Mutual Learning and Large-Margin Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding

Spoken language understanding (SLU) is a fundamental task in the task-oriented dialogue systems. However, the inevitable errors from automatic speech recognition (ASR) usually impair the understanding performance and lead to error propagation. Although there are some attempts to address this problem through contrastive learning, they (1) treat clean manual transcripts and ASR transcripts equally without discrimination in fine-tuning; (2) neglect the fact that the semantically similar pairs are still pushed away when applying contrastive learning; (3) suffer from the problem of Kullback-Leibler (KL) vanishing. In this paper, we propose Mutual Learning and Large-Margin Contrastive Learning (ML-LMCL), a novel framework for improving ASR robustness in SLU. Specifically, in fine-tuning, we apply mutual learning and train two SLU models on the manual transcripts and the ASR transcripts, respectively, aiming to iteratively share knowledge between these two models. We also introduce a distance polarization regularizer to avoid pushing away the intra-cluster pairs as much as possible. Moreover, we use a cyclical annealing schedule to mitigate KL vanishing issue. Experiments on three datasets show that ML-LMCL outperforms existing models and achieves new state-of-the-art performance.
Label-Synchronous Neural Transducer for Adaptable Online E2E Speech Recognition

Although end-to-end (E2E) automatic speech recognition (ASR) has shown state-of-the-art recognition accuracy, it tends to be implicitly biased towards the training data distribution which can degrade generalisation. This paper proposes a label-synchronous neural transducer (LS-Transducer), which provides a natural approach to domain adaptation based on text-only data. The LS-Transducer extracts a label-level encoder representation before combining it with the prediction network output. Since blank tokens are no longer needed, the prediction network performs as a standard language model, which can be easily adapted using text-only data. An Auto-regressive Integrate-and-Fire (AIF) mechanism is proposed to generate the label-level encoder representation while retaining low latency operation that can be used for streaming. In addition, a streaming joint decoding method is designed to improve ASR accuracy while retaining synchronisation with AIF. Experiments show that compared to standard neural transducers, the proposed LS-Transducer gave a 12.9% relative WER reduction (WERR) for intra-domain LibriSpeech data, as well as 21.4% and 24.6% relative WERRs on cross-domain TED-LIUM 2 and AESRC2020 data with an adapted prediction network.
Deep Clustering with Self-supervision using Pairwise Data Similarities

&lt;p&gt;Deep clustering incorporates embedding into clustering to find a lower dimensional space appropriate for clustering. In this paper, we propose a novel deep clustering framework with self-supervision using pairwise data similarities (DCSS). The proposed method consists of two successive phases. In the first phase, we propose to form hypersphere-like groups of similar data points, i.e. one hypersphere per cluster, employing an autoencoder that is trained using cluster-specific losses. The hyper-spheres are formed in the autoencoder’s latent space. In the second phase, we propose to employ pairwise data similarities to create a K-dimensional space that is capable of accommodating more complex cluster distributions, hence providing more accurate clustering performance. K is the number of clusters. The autoencoder’s latent space obtained in the first phase is used as the input of the second phase. The effectiveness of both phases is demonstrated on seven benchmark datasets by conducting a rigorous set of experiments. The DCSS code is available: https://github.com/Armanfard-Lab/DCSS&lt;/p&gt;
Adaptive pseudo‐spectrum based track‐before‐detect for targets with uncertain existence

Abstract In the existing velocity filtering based track‐before‐detect (VF‐TBD), target existence is generally assumed to be constant, and multiple successive frames in a sliding window are jointly processed. However, when the target existence changes abruptly in a processing batch, the incorporation of noise‐only frames may corrupt target energy integration. This results in degraded detection and estimation performances with serious decision delays. A pseudo‐spectrum based VF‐TBD is proposed to effectively detect and track targets with existence uncertainty. An adaptive integration strategy is developed to realise effective energy accumulation with only informative frames by matching target appearance and disappearance times in addition to the velocity. The change of target existence can be discovered in time, and the decision delay is reduced. An estimation strategy based on the maximum output SNR is presented to supply accurate estimations of not only target motion parameters (including the position and velocity) but also time parameters (including appearance and disappearance times). The target output envelope, SNR gain and receiver operating characteristic curve of targets with uncertain existence are analysed theoretically. Numerical simulations exhibit the validity of the proposed method.
Ontological and Axiological Aspects in the Evaluation of Modern Education

The modern socio-cultural situation is characterized by the change of value coordinates, the breakdown of the familiar world and significant transformations in various spheres and areas. Most significantly, these changes affect the field of education, which is an essential element of spiritual culture. The content of educational pro-grams reflects the demands of modern society to the individual, to the complex of those competencies that he should possess. In the life of an individual and society as a whole, education should become an instrument of humanization, which is aimed at cognition and improvement of the world and oneself. In modern socio-cultural life, education is a significant sphere in which the development and realization of creative and spiritual poten-tial takes place. Education cannot be characterized as a certain amount of knowledge broadcast from the teacher to the student, its primary task is to create conditions for the process of cognition of the individual him-self and the surrounding world. Formation of worldview culture, which includes learned and accepted universal values, aspirations to improve their personal and professional qualities, is the aim and value of modern educa-tion.
An Appraisal Towards the Technological Improvement of Library Operations Management in Digital Era

Operations management has turned into a challenging agenda for non-profitable organizations in the current competitive world. To better resolve such issue, technological and digital advancements pave the way to improve the efficiency of activities, and in this context, library serves significant potentials. This transformation within library, primarily demands for identification of activities contributing to the accomplishment of operations management, which is addressed in this paper through literature study in combination with field observations and discussion with librarians. This phase is followed by investigation of feasible digital technologies that potentially contribute to the upbuilding of the outlined activities. Given the current high-pace digital era, the findings of this study demonstrate that activities in library serve significant potential for digital promotion. For instance, material handling could take the advantage of autonomous mobile robots (AMRs) and artificial intelligence (AI). However, there are some limitations that are deemed essential to be considered prior to such transformation, and these barriers are articulated in this study.
PEMANFAATAN 3D PRINTING UNTUK PEMBUATAN PEMEGANG TOOLS DI WORKSHOP GO KART

3D printing is now one of the technologies that is developing for all fields, especially in the automotive sector, the existence of 3D printing is very helpful for making the desired designs and 3D printing has a big role to meet the needs of several workshops, because with this technology The concept created can be influential to support the neatness of the workshop, especially in storing tools. One of the benefits that can be generated from this technology is to make parts that can help tidy up the tools in the workshop. Of course, the concept and design that is made must be thought of beforehand, its usability and function so that it can function properly for the workshop itself. Therefore, this technology has become an opportunity for solutions to solve problems so that the manufacture of products that will be used for workshops is greatly helped by the existence of a 3D printer. The results achieved are the difference in dimensions on the key chain prototype which may be due to the difference in the depreciation value of the material used when the material becomes solid. 3D Printing machine is very helpful in making prototypes when the product will be made in bulk quantities. And can also be used to make parts that have complex shapes.
ANALISIS PEMASANGAN BOX CULVERT (2 m x 1.5 m) SEBAGAI SALURAN PENYEIMBANG DI KM 54+490 SAMPAI DENGAN KM 55+070

Train is one of the land transportation infrastructure that have an important role in distributing things and passengers. However, currently there are still some obstacles in the operation of the railway, mainly caused by natural disasters. In general, the National DisasterManagement Agency (BNPB) noted that 95% of disasters that occurred in Indonesia were dominated by hydrometeorological disasters, one of which was flooding.In February 2021, there was a flood on the railway line, precisely between Lemahabang -Kedunggedeh Station, DAOP 1 Jakarta on the rail line KM54+490 to KM 55+070 which caused the body of the upstream and downstream rail lines collapse, resulting in the cessation of the train operations.Therefore, a total of 31 box culverts will be planned to drain the 5-year flood discharge and become a balancing channel for the railroad if the flood phenomenon occurs again
CNN-based Visual Servoing for Simultaneous Positioning and Flattening of Soft Fabric Parts

&lt;p&gt;This paper proposes CNN-based visual servoing for simultaneous positioning and flattening of a soft fabric part placed on a table by a dual manipulator system. We propose a network for multimodal data processing of grayscale images captured by a camera and force/torque applied to force sensors. The training dataset is collected by moving the real manipulators, which enables the network to map the captured images and force/torque to the manipulator’s motion in Cartesian space. We apply structured lighting to emphasize the features of the surface of the fabric part since the surface shape of the non-textured fabric part is difficult to recognize by a single grayscale image. Through experiments, we show that the fabric part with unseen wrinkles can be positioned and flattened by the proposed visual servoing scheme.&lt;/p&gt; &lt;p&gt;&lt;br&gt;&lt;/p&gt; &lt;p&gt;&lt;br&gt;&lt;/p&gt; &lt;p&gt; *The complete version of this preprint paper is in proceedings of ICRA 2023. Please refer to "https://ieeexplore.ieee.org/document/10160635". &lt;/p&gt;
Intelligent Control Over Multiple Ovens by Connection Models

&lt;p&gt;Intelligent control technology and intelligent information integration technology are the two core technologies of intelligent control system. In this paper, intelligent control and intelligent system are studied from two perspectives: multi-agent integration and neural network intelligent control technology. The multi-agent oriented research aims to provide an architecture suitable for information integration of distributed intelligent control systems. The research of neural network aims to deeply study the theory and technology of neural control, an important branch of intelligent control. The main conclusions of this paper are as follows: In the aspect of multi-layer forward neural network learning algorithm, the weight learning and structure learning methods of multi-layer forward neural network are studied. A heuristic genetic algorithm for sample division and a composite structure learning method are proposed. The sample division heuristic genetic algorithm uses the sample division method to construct the initial genetic population, and combines the subspace division for heuristic search. Several algebraic properties of sample set classes and their training are presented and proved.&lt;/p&gt;
Understanding of P vs NP Problem: With Extension of the Continuum Hypothesis

&lt;p&gt;P vs NP problem turns out to be P=NP in quantum computing but remains questions of optimization in classical computing.&lt;/p&gt;
Programming Computational Electromagnetic Applications Assisted by Large Language Models

&lt;p&gt;This article discusses the possibilities and limitations of using large language models (LLMs) in software development with applications in computational electromagnetics. Three tasks are discussed: code translation, code generation, and code description. The tests showed that LLMs are generally very useful. Even when errors occurred, they can provide useful hints to find a solution.&lt;/p&gt;
Upaya Peningkatan Keterampilan Membaca Di Kelas Tinggi

Reading is the ability to use thoughts and actions to perform visual activities, pronounce a series of letters into words and sentences, master reading techniques and understand the content of the reading correctly. The method used in this study is a literature analysis study. Reading skills are skills that focus on reading words and sentences. The aspects in reading, it seems like pronunciation accuracy, intonation, fluency, clarity of voice and reading ability intact. Reading skills are the ability for a person to be able to read a reading properly and correctly, and can get the message contained in the reading. The purpose of reading is to find and obtain information, include its content and understand the meaning of reading.
Gamifikasi dalam Pengajaran Bahasa Inggris dengan Menggunakan Platform Genially

Genially is one of the educational tools that advocates interactive visual communication. Genially serves as a comprehensive online platform where teachers can create captivating presentations, interactive graphics, infographics, gamification, quizzes, breakouts, portfolios, and more, enriching them with interactivity and animation effects within seconds. The objective of this workshop's research is to identify students' perceptions of the impact of the gamification teaching method using the Genially platform and to engage and enhance students' interest in the field of education through ELT (English Language Teaching). In this study, various findings related to the respondents' views on the gamification approach in teaching English using the Genially platform were discovered. The results indicate that the gamification approach through the Genially platform receives a positive response from participants regarding the English learning process, particularly including students' interest and motivation in learning English. This research is expected to serve as innovation, inspiration, and evaluation for educators and students in its implementation within English language teaching and learning through gamification methods using the Genially platform, involving various other instruments in the future.