Miracle-dz/UniFAR-FTUs huggingface dataset:
A Style-Oriented Approach to Intelligent Generation of Product Creative Concepts

Traditional product concept innovation design relies on designers’ design experience and is hindered by integrating tacit knowledge such as work experience, time, and ideas. Rapidly iterating design solutions and meeting diverse market needs has become one of the challenges for designers. Intuitive and varied visual stimulation can quickly inspire designers' creative thinking, effectively improve their efficiency, and assist them in creating various styles of products, breaking the product innovation and design process of design thinking curing. In this paper, we propose a style-oriented approach for the intelligent generation of creative product concepts to help designers quickly obtain creative inspiration and improve design efficiency. First, we build a high-quality hairdryer dataset and generate multiple styles of hairdryer images by training the StyleGAN3 generative adversarial network to solve the hairdryer creative concept image generation problem. Second, a stable diffusion model is introduced to optimise and innovatively generate the obtained images so that they can innovatively generate various details to maintain the original styles and thus improve the image quality. Third, a 3D hairdryer model is generated based on the Shap∙E model and parametric design using the images as input conditions. Finally, the effectiveness of product conceptual design generation is assessed using three indicators: novelty, variety, and timeliness. The intelligent product creative concept generation methodology system we constructed provides designers with a dedicated, fast, and effective source of conceptual creative inspiration, helping to advance product conceptual design solutions efficiently.
Hyperspectral Band Selection by Multitask Sparsity Pursuit

Hyperspectral images have been proved to be effective for a wide range of applications; however, the large volume and redundant information also bring a lot of inconvenience at the same time. To cope with this problem, hyperspectral band selection is a pertinent technique, which takes advantage of removing redundant components without compromising the original contents from the raw image cubes. Because of its usefulness, hyperspectral band selection has been successfully applied to many practical applications of hyperspectral remote sensing, such as land cover map generation and color visualization. This paper focuses on groupwise band selection and proposes a new framework, including the following contributions: 1) a smart yet intrinsic descriptor for efficient band representation; 2) an evolutionary strategy to handle the high computational burden associated with groupwise-selection-based methods; and 3) a novel MTSP-based criterion to evaluate the performance of each candidate band combination. To verify the superiority of the proposed framework, experiments have been conducted on both hyperspectral classification and color visualization. Experimental results on three real-world hyperspectral images demonstrate that the proposed framework can lead to a significant advancement in these two applications compared with other competitors.
Viscoelastic constitutive modeling of creep and stress relaxation in polymers and polymer matrix composites

Abstract: This chapter discusses the basic concepts of viscoelastic behavior of polymers and polymeric composites as well as respective modeling for both the linear and nonlinear behavior. The material is discussed in four main sections. The first discusses creep-recovery and stress relaxation experiments. The second section discusses the concept of linearity in viscoelasticity and a short presentation of the time–temperature, time–stress and time–temperature–stress superposition principles is made. The third section discusses the different linear viscoelastic models, from the basic viscoelastic elements up to the generalized models. Finally, the fourth section discusses the nonlinear viscoelastic behavior of polymers and polymeric composites and different predicting models and methods along with specific applications are presented.
Multi-View Learning and Link Farm Discovery

The first part of this abstract focuses on estimation of mix- ture models for problems in which multiple views of the instances are available. Examples of this setting include clustering web pages or re- search papers that have intrinsic (text) and extrinsic (references) at- tributes. Mixture model estimation is a key problem for both semi- supervised and unsupervised learning. An appropriate optimization cri- terion quantifies the likelihood and the consensus among models in the individual views; maximizing this consensus minimizes a bound on the risk of assigning an instance to an incorrect mixture component. An EM algorithm maximizes this criterion. The second part of this abstract fo- cuses on the problem of identifying link spam. Search engine optimizers inflate the page rank of a target site by spinning an artificial web for the sole purpose of providing inbound links to the target. Discriminating "natural" from "artificial" web sites is a difficult multi-view problem.
Telemetric, computer aided system for non-invasive examination of heart rate variability, type VariaPulse TF 3

The authors present hardware and software which enables one to enlarge the practical application of the method of spectral analysis of R-R intervals variability, used for the evaluation of heart rate regulation in which autonomic nervous system and sympathetic and vagal effects play an important role. This developing method, applied and verified by many authors, is at present realized with the help of expensive monitoring systems, which prevents its more general use. To solve the problem, the authors used a microcomputer measuring system, type TF-3, with telemetric transfer of values of R-R intervals, equipped with special software including the procedures of measuring, filtration and calculation of parameters of spectral analysis. The results of analyses make it possible to depict and quantify the manifestation of factors influencing the variability of heart rate, including the sympathetic and vagal effects and their relationship during the specified functional load, drug application etc.
Efﬁcacy of the Use of Exergames in Promoting the Mental Health of the Elderly: Protocol of a Systematic Review

: Background: Scientiﬁc and technological advancements have signiﬁcantly impacted our daily lives, and it is not easy to imagine living without their beneﬁts. However, elderly individuals may experience limited access to these resources, impeding their autonomy. To address this, the use of exergames has been proposed to promote the mental health and socialization of older people. This study aims to systematically review the effectiveness of exergames in promoting older people’s mental health and socialization. Methods: The Joanna Briggs Institute’s recommended methodology will be used for a systematic review. Databases relevant to the research topic, such as those regarding gerontologic care, aged rehabilitation, geriatric games, video games, play and playthings, mental health, anxiety, depression, stress, and physiological factors, will be searched for experimental and quasi-experimental studies, as well as randomized studies with and without control groups. Results: The systematic literature review will synthesize ﬁndings on the effectiveness of exergames in promoting mental health and socialization in the elderly population. Conclusions: Exergames may have the potential to effectively improve the mental health and socialization of the elderly population. Identifying which exergames are most effective for these purposes is important to inform the provision of healthcare to this population.
Yeast Two-Hybrid System and its Advances in Protein Interaction Study

With the advent of new sequencing technologies, genome sequencing efforts are speeding up the discovery process at a faster rate. With this explosion of sequence information, there is a need to understand how genes work in concert in order to fulfill the cells functions. The yeast two-hybrid system used to identify protein-protein interactions is one of the most powerful and versatile methods for characterizing a protein’s function. Yeast two hybrid systems is one of the basic technologies used in protein interaction studies for both academic researchers and those in biotechnology and pharmaceutical companies. Most of interaction studies reported in literature gives an insight about molecular mechanism underlying the biological processes. Several advancements made over the original Yeast two hybrid systems, which will help in understanding and revealing many facts in the complex field of protein interactions. In this review we discussed about the yeast two hybrid system and its advances in protein interaction.
Notions of Correctness when Evaluating Protein Name Taggers

This paper introduces four different notions of correctness to be used when measuring the performance of protein name taggers, each of which reflects certain characteristics of the tagger under evaluation. The discussion regarding the different notions is centered around the evaluation of two protein name taggers; Yapex, developed by the authors, and KeX developed by Fukuda et al. (1998). For the purpose of illustrating the difference between the ways of evaluation, both taggers are applied to a test corpus of 101 MEDLINE abstracts in which all occurrences of protein names have been marked up by domain experts.
A multi-threading architecture for multilevel secure transaction processing

A TCB and security kernel architecture for supporting multi-threaded, queue-driven transaction processing applications in a multilevel secure environment is presented. Our design exploits hardware security features of the Intel 80/spl times/86 processor family. Intel's CPU architecture provides hardware with two distinct descriptor tables. We use one of these in the usual way for process isolation. For each process, the descriptor table holds the descriptors of "system-low" segments, such as code segments, used by every thread in a process. We use the second table to hold descriptors for segments known to individual threads within the process. This allocation, together with an appropriately designed scheduling policy, permits us to avoid the full cost of process creation when only switching between threads of different security classes in the same process. Where large numbers of transactions are encountered on transaction queues, this approach has benefits over traditional multilevel systems.
A MAN FROM SAN JUAN: FACIAL APPROXIMATION WITHIN ANTHROPOLOGY

Within both anthropology and forensic iden- tifi cation, a facial approximation (also known as "facial reconstruction") is often presented as an accomplished fact, with minimal, or no evidence of the research and methods used to achieve the result. This paper presents a detailed overview of the research and methods used for a 2D computer graphic facial approximation of a prehistoric man unearthed in the Jachal Valley, San Juan Province, Argentina. Understood to be a member of the extinct Huarpe, this individual's skull displays many of
Hardware decoders for polar codes: An overview

Polar codes are an exciting new class of error correcting codes that achieve the symmetric capacity of memoryless channels. Many decoding algorithms were developed and implemented, addressing various application requirements: from error-correction performance rivaling that of LDPC codes to very high throughput or low-complexity decoders. In this work, we review the state of the art in polar decoders implementing the successive-cancellation, belief propagation, and list decoding algorithms, illustrating their advantages.
Issues for Consideration in the Analysis of Microarray Data in Behavioural Studies

Microarrays are one of several technologies that allow for measurement the expression of thousands of genes simultaneously. This technological advance provides a challenge for the analysis of these data. In this review we discuss these analytical issues from the initial quality control to normalization, differential expression, clustering and finally functional pathway analysis. We focus on Affymetrix data but many of the issues are the same for other array platforms.
Accelerated Evolution of Enhancer Hotspots in the Mammal Ancestor

Mammals have evolved remarkably different sensory, reproductive, metabolic, and skeletal systems. To explore the genetic basis for these differences, we developed a comparative genomics approach to scan whole-genome multiple sequence alignments to identify regions that evolved rapidly in an ancestral lineage but are conserved within extant species. This pattern suggests that ancestral changes in function were maintained in descendants. After applying this test to therian mammals, we identified 4,797 accelerated regions, many of which are noncoding and located near developmental transcription factors. We then used mouse transgenic reporter assays to test if noncoding accelerated regions are enhancers and to determine how therian-specific substitutions affect their activity in vivo. We discovered enhancers with expression specific to the therian version in brain regions involved in the hormonal control of milk ejection, uterine contractions, blood pressure, temperature, and visual processing. This work underscores the idea that changes in developmental gene expression are important for mammalian evolution, and it pinpoints candidate genes for unique aspects of mammalian biology.
Embedded Named Entity Recognition using Probing Classifiers

Extracting semantic information from generated text is a useful tool for applications such as automated fact checking or retrieval augmented generation. Currently, this requires either separate models during inference, which increases computational cost, or destructive fine-tuning of the language model. Instead, we propose directly embedding information extraction capabilities into pre-trained language models using probing classifiers, enabling efficient simultaneous text generation and information extraction. For this, we introduce an approach called EMBER and show that it enables named entity recognition in decoder-only language models without fine-tuning them and while incurring minimal additional computational cost at inference time. Specifically, our experiments using GPT-2 show that EMBER maintains high token generation rates during streaming text generation, with only a negligible decrease in speed of around 1% compared to a 43.64% slowdown measured for a baseline using a separate NER model. Code and data are available at https://github.com/nicpopovic/EMBER.
Dysen-VDM: Empowering Dynamics-aware Text-to-Video Diffusion with LLMs

Text-to-video (T2V) synthesis has gained increasing attention in the community, in which the recently emerged diffusion models (DMs) have promisingly shown stronger performance than the past approaches. While existing state-of-the-art DMs are competent to achieve high-resolution video generation, they may largely suffer from key limitations (e.g., action occurrence disorders, crude video motions) with respect to the intricate temporal dynamics modeling, one of the crux of video synthesis. In this work, we investigate strengthening the awareness of video dynamics for DMs, for high-quality T2V generation. Inspired by human intuition, we design an innovative dynamic scene manager (dubbed as Dysen) module, which includes (step-1) extracting from input text the key actions with proper time-order arrangement, (step-2) transforming the action schedules into the dynamic scene graph (DSG) representations, and (step-3) enriching the scenes in the DSG with sufficient and reasonable details. Taking advantage of the existing powerful LLMs (e.g., ChatGPT) via in-context learning, Dysen realizes (nearly) human-level temporal dynamics understanding. Finally, the resulting video DSG with rich action scene details is encoded as fine-grained spatio-temporal features, integrated into the backbone T2V DM for video generating. Experiments on popular T2V datasets suggest that our Dysen-VDM consistently outperforms prior arts with significant margins, especially in scenarios with complex actions. Codes at https://haofei.vip/Dysen-VDM
Learning Time Slot Preferences via Mobility Tree for Next POI Recommendation

Next Point-of-Interests (POIs) recommendation task aims to provide a dynamic ranking of POIs based on users' current check-in trajectories. The recommendation performance of this task is contingent upon a comprehensive understanding of users' personalized behavioral patterns through Location-based Social Networks (LBSNs) data. While prior studies have adeptly captured sequential patterns and transitional relationships within users' check-in trajectories, a noticeable gap persists in devising a mechanism for discerning specialized behavioral patterns during distinct time slots, such as noon, afternoon, or evening. In this paper, we introduce an innovative data structure termed the ``Mobility Tree'', tailored for hierarchically describing users' check-in records. The Mobility Tree encompasses multi-granularity time slot nodes to learn user preferences across varying temporal periods. Meanwhile, we propose the Mobility Tree Network (MTNet), a multitask framework for personalized preference learning based on Mobility Trees. We develop a four-step node interaction operation to propagate feature information from the leaf nodes to the root node. Additionally, we adopt a multitask training strategy to push the model towards learning a robust representation. The comprehensive experimental results demonstrate the superiority of MTNet over eleven state-of-the-art next POI recommendation models across three real-world LBSN datasets, substantiating the efficacy of time slot preference learning facilitated by Mobility Tree.
Stent Graft-in-Stent Graft as a Rescue Technique for Endovascular Treatment of Giant Extracranial Internal Carotid Aneurysm

Endovascular treatment of a giant extracranial internal carotid aneurysm by a stent graft implantation was unsuccessful due to a high flow leak directly through the stent graft's coating. The problem was solved deploying a second stent graft inside the previously implanted one resulting in complete exclusion of the aneurysmal sac and patent carotid lumen preservation. The review of the literature did not provide a case using this endovascular strategy. Follow-up for more than 12 months, using CT angiography, showed confirmed aneurysmal exclusion and carotid patency and no clinical complications have been detected.
LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

While polyhedral compilers have shown success in implementing advanced code transformations, they still have challenges in selecting the most profitable transformations that lead to the best speedups. This has motivated the use of machine learning to build cost models to guide the search for polyhedral optimizations. State-of-the-art polyhedral compilers have demonstrated a viable proof-of-concept of this approach. While such a proof-of-concept has shown promise, it still has significant limitations. State-of-the-art polyhedral compilers that use a deep-learning cost model only support a small subset of affine transformations, limiting their ability to apply complex code transformations. They also only support simple programs that have a single loop nest and a rectangular iteration domain, limiting their applicability to many programs. These limitations significantly impact the generality of such compilers and autoschedulers and put into question the whole approach. In this paper, we introduce LOOPer, the first polyhedral autoscheduler that uses a deep-learning based cost model and covers a large set of affine transformations and programs. It supports the exploration of a large set of affine transformations, allowing the application of complex sequences of polyhedral transformations. It also supports the optimization of programs with multiple loop nests and with rectangular and non-rectangular iteration domains, allowing the optimization of an extensive set of programs. We implement and evaluate LOOPer and show that it achieves speedups over the state-of-the-art. On the Polybench benchmark, LOOPer achieves a geometric mean speedup of 1.59x over Tiramisu. LOOPer also achieves competitive speedups with a geometric mean speedup of 1.34x over Pluto, a state-of-the-art polyhedral compiler that does not use a machine-learning based cost model.
Spectroscopy of laser cooling transitions in MgF

We measure the complete set of transition frequencies necessary to laser cool and trap MgF molecules. Specifically, we report the frequency of multiple low $J$ transitions of the $X^2\Sigma^+(v^{\prime\prime}=0,1) \rightarrow A^2\Pi_{1/2}(v^\prime=0)$, $X^2\Sigma^+(v^{\prime\prime}=1,2) \rightarrow A^2\Pi_{1/2}(v^\prime=1)$, and $X^2\Sigma^+(v^{\prime\prime}=1) \rightarrow B^2\Sigma^+(v^{\prime}=0)$ bands of MgF. The measured $X^2\Sigma^+(v^{\prime\prime}=1)\rightarrow B^2\Sigma^+(v^\prime=0)$ spectrum allowed the spin-rotation and hyperfine parameters of the $B^2\Sigma^+(v=0)$ state of MgF to be determined. Furthermore, we demonstrate optical cycling in MgF by pumping molecules into the $X^2\Sigma^+(v=1,2)$ states. Optical pumping enhances the spectroscopic signals of transitions originating in the $N^{\prime\prime}=1$ level of the $X^2\Sigma^+(v^{\prime\prime}=1,2)$ states.
Topological Representations of Heterogeneous Learning Dynamics of Recurrent Spiking Neural Networks

Spiking Neural Networks (SNNs) have become an essential paradigm in neuroscience and artificial intelligence, providing brain-inspired computation. Recent advances in literature have studied the network representations of deep neural networks. However, there has been little work that studies representations learned by SNNs, especially using unsupervised local learning methods like spike-timing dependent plasticity (STDP). Recent work by \cite{barannikov2021representation} has introduced a novel method to compare topological mappings of learned representations called Representation Topology Divergence (RTD). Though useful, this method is engineered particularly for feedforward deep neural networks and cannot be used for recurrent networks like Recurrent SNNs (RSNNs). This paper introduces a novel methodology to use RTD to measure the difference between distributed representations of RSNN models with different learning methods. We propose a novel reformulation of RSNNs using feedforward autoencoder networks with skip connections to help us compute the RTD for recurrent networks. Thus, we investigate the learning capabilities of RSNN trained using STDP and the role of heterogeneity in the synaptic dynamics in learning such representations. We demonstrate that heterogeneous STDP in RSNNs yield distinct representations than their homogeneous and surrogate gradient-based supervised learning counterparts. Our results provide insights into the potential of heterogeneous SNN models, aiding the development of more efficient and biologically plausible hybrid artificial intelligence systems.