Coherent Sequence Research Articles

Application for Tridimensional Renal Visualization and Operative Planning

Background: Partial nephrectomy is the preferred surgical approach for renal neoplasms smaller than 7 cm, without compromising disease-free or cancer-specific survival. It is increasingly being performed in patients with complex renal tumors. Three-dimensional models of preoperative computed tomography (CT) or magnetic resonance imaging (MRI) scans are the future for accurately identifying the tumor's location and planning the surgical strategy. Objectives: This study aims to develop an application for use by medical professionals, designed to view three-dimensional (3D) renal images with radius, extension, nearness, anterior/posterior, and lines scores from preoperative CT or MRI exams. Methods: This descriptive study focuses on developing a mobile application, which was entirely created by a urologist on a no-code platform without using programming languages. The renal 3D images were generated from the patient's specific CT or MRI scans. A dedicated database was also utilized for the application. Results: The development of the NeoRenal application resulted in ten complexly designed screens created within a no-code platform, each serving a specific purpose in a coherent sequence to ensure an optimal user experience. The uniform flow and design of these screens aim to provide intuitive and comprehensive user acceptance, facilitating registration, data entry, visualization, and editing functionalities. Conclusions: The application was successfully developed and is ready to be published on various operating systems.

EXPRESS: Landmark Publications in Analytical Atomic Spectrometry: Fundamentals and Instrumentation Development.

The almost-two-centuries history of spectrochemical analysis has generated a body of literature so vast that it has become nearly intractable for experts, much less for those wishing to enter the field. Authoritative, focused reviews help to address this problem but become so granular that the overall directions of the field are lost. This broader perspective can be provided partially by general overviews but then the thinking, experimental details, theoretical underpinnings and instrumental innovations of the original work must be sacrificed. In the present compilation, this dilemma is overcome by assembling the most impactful publications in the area of analytical atomic spectrometry. Each entry was proposed by at least one current expert in the field and supported by a narrative that justifies its inclusion. The entries were then assembled into a coherent sequence and returned to contributors for a round-robin review.

Temporal-Coherence Induces Binding of Responses to Sound Sequences in Ferret Auditory Cortex.

Binding the attributes of a sensory source is necessary to perceive it as a unified entity, one that can be attended to and extracted from its surrounding scene. In auditory perception, this is the essence of the cocktail party problem in which a listener segregates one speaker from a mixture of voices, or a musical stream from simultaneous others. It is postulated that coherence of the temporal modulations of a source's features is necessary to bind them. The focus of this study is on the role of temporal-coherence in binding and segregation, and specifically as evidenced by the neural correlates of rapid plasticity that enhance cortical responses among synchronized neurons, while suppressing them among asynchronized ones. In a first experiment, we find that attention to a sound sequence rapidly binds it to other coherent sequences while suppressing nearby incoherent sequences, thus enhancing the contrast between the two groups. In a second experiment, a sequence of synchronized multi-tone complexes, embedded in a cloud of randomly dispersed background of desynchronized tones, perceptually and neurally pops-out after a fraction of a second highlighting the binding among its coherent tones against the incoherent background. These findings demonstrate the role of temporal-coherence in binding and segregation.

Insights into the Galactic Bulge Chemodynamical Properties from Gaia Data Release 3

We explore the chemodynamical properties of the Galaxy in the azimuthal velocity V ϕ and metallicity [Fe/H] space using red giant stars from Gaia Data Release 3. The row-normalized V ϕ –[Fe/H] maps form a coherent sequence from the bulge to the outer disk, clearly revealing the thin/thick disk and the Splash. The metal-rich stars display bar-like kinematics, while the metal-poor stars show dispersion-dominated kinematics. The intermediate-metallicity population (−1 < [Fe/H]< − 0.4) can be separated into two populations, one that is bar-like, i.e., dynamically cold ( σVR∼80 km s−1) and fast-rotating (V ϕ ≳ 100 km s−1), and the Splash, which is dynamically hot ( σVR∼110 km s−1) and slow-rotating (V ϕ ≲ 100 km s−1). We compare the observations in the bulge region with an Auriga simulation where the last major merger event occurred ∼10 Gyr ago: only stars born around the time of the merger reveal a Splash-like feature in the V ϕ –[Fe/H] space, suggesting that the Splash is likely merger-induced, predominantly made up of heated disk stars and the starburst associated with the last major merger. Since the Splash formed from the proto-disk, its lower metallicity limit coincides with that of the thick disk. The bar formed later from the dynamically hot disk with [Fe/H] > − 1 dex, with the Splash not participating in the bar formation and growth. Moreover, with a set of isolated evolving N-body disk simulations, we confirm that a nonrotating classical bulge can be spun up by the bar and develop cylindrical rotation, consistent with the observations for the metal-poor stars.

Image sequence sorting algorithm for commercial tasks.

The sorting of sequences of images is crucial for augmenting user engagement in various virtual commercial platforms, particularly within the real estate sector. A coherent sequence of images respecting room type categorization significantly enhances the intuitiveness and seamless navigation of potential customers through listings. This study methodically formalizes the challenge of image sequence sorting and expands its applicability by framing it as an ordering problem. The complexity lies in devising a universally applicable solution due to computational demands and impracticality of exhaustive searches for optimal sequencing. To tackle this, our proposed algorithm employs a shortest path methodology grounded in semantic similarity between images. Tailored specifically for the real estate sector, it evaluates diverse similarity metrics to efficiently arrange images. Additionally, we introduce a genetic algorithm to optimize the selection of semantic features considered by the algorithm, further enhancing its effectiveness. Empirical evidence from our dataset demonstrates the efficacy of the proposed methodology. It successfully organizes images in an optimal sequence across 85% of the listings, showcasing its effectiveness in enhancing user experience in virtual commercial platforms, particularly in real estate. This study presents a novel approach to sorting sequences of images in virtual commercial platforms, particularly beneficial for the real estate sector. The proposed algorithm effectively enhances user engagement by providing more intuitive and visually coherent image arrangements.

Bloch oscillation phases investigated by multipath Stückelberg atom interferometry

Atoms undergoing Bloch oscillations (BOs) in an accelerating optical lattice acquire momentum of two photon recoils per BO. This technique provides a large momentum transfer tool for atom optics, but its full exploitation for atom interferometric sensors requires both experimental and theoretical characterization of associated phases. Each BO involves a Landau-Zener crossing with multiple crossings inducing interference known as Stückelberg interference. We develop a multipath Stückelberg interferometer and investigate atomic phase evolution during BOs, up to 100 photon recoil momentum transfer. We compare to numerically calculated single-particle Schrödinger evolution, demonstrate highly coherent BO sequences, and assess phase stability requirements for BO-enhanced precision interferometry in fundamental physics and sensing applications. Published by the American Physical Society 2024

Taking time to compose thoughts with prefrontal schemata

Under what conditions can prefrontal cortex direct the composition of brain states, to generate coherent streams of thoughts? Using a simplified Potts model of cortical dynamics, crudely differentiated into two halves, we show that once activity levels are regulated, so as to disambiguate a single temporal sequence, whether the contents of the sequence are mainly determined by the frontal or by the posterior half, or by neither, depends on statistical parameters that describe its microcircuits. The frontal cortex tends to lead if it has more local attractors, longer lasting and stronger ones, in order of increasing importance. Its guidance is particularly effective to the extent that posterior cortices do not tend to transition from state to state on their own. The result may be related to prefrontal cortex enforcing its temporally-oriented schemata driving coherent sequences of brain states, unlike the atemporal “context” contributed by the hippocampus. Modelling a mild prefrontal (vs. posterior) lesion offers an account of mind-wandering and event construction deficits observed in prefrontal patients.

Temporal Development GAN (TD-GAN): Crafting More Accurate Image Sequences of Biological Development

In this study, we propose a novel Temporal Development Generative Adversarial Network (TD-GAN) for the generation and analysis of videos, with a particular focus on biological and medical applications. Inspired by Progressive Growing GAN (PG-GAN) and Temporal GAN (T-GAN), our approach employs multiple discriminators to analyze generated videos at different resolutions and approaches. A new Temporal Discriminator (TD) that evaluates the developmental coherence of video content is introduced, ensuring that the generated image sequences follow a realistic order of stages. The proposed TD-GAN is evaluated on three datasets: Mold, Yeast, and Embryo, each with unique characteristics. Multiple evaluation metrics are used to comprehensively assess the generated videos, including the Fréchet Inception Distance (FID), Frechet Video Distance (FVD), class accuracy, order accuracy, and Mean Squared Error (MSE). Results indicate that TD-GAN significantly improves FVD scores, demonstrating its effectiveness in generating more coherent videos. It achieves competitive FID scores, particularly when selecting the appropriate number of classes for each dataset and resolution. Additionally, TD-GAN enhances class accuracy, order accuracy, and reduces MSE compared to the default model, demonstrating its ability to generate more realistic and coherent video sequences. Furthermore, our analysis of stage distribution in the generated videos shows that TD-GAN produces videos that closely match the real datasets, offering promising potential for generating and analyzing videos in different domains, including biology and medicine.

Coherent structures and sequences of exact kink and anti-kink solutions to the complex cubic–quintic Ginzburg-Landau equation perturbed by intrapulse Raman scattering

Coherent structures and sequences of exact kink and anti-kink solutions to the complex cubic–quintic Ginzburg-Landau equation perturbed by intrapulse Raman scattering

Transforming Text Generation in NLP: Deep Learning with GPT Models and 2023 Twitter Corpus Using Transformer Architecture

This paper presents the design, implementation, and evaluation of a Transformer-based Generative Pre-trained Transformer (GPT) model tailored for character-level text generation. Leveraging the robust architecture of the Transformer, the model has been trained on a corpus sourced from social media text data, with the aim of exploring the intricacies of language patterns within a condensed and informal text setting. Key aspects of the model include a multi-head self-attention mechanism with a custom head configuration, positional embeddings, and layer normalization to promote stability in learning. It operates with a defined set of hyperparameters: a batch size of 32, a block size of 128, 200 iterations, a learning rate of 3e-4, and employs 4 attention heads across 4 layers with an embedding dimension of 384. The model has been optimized using the AdamW optimizer and includes regularization through dropout to prevent overfitting.Through a series of training iterations, the model demonstrates a converging behavior in loss metrics, indicating effective learning, and showcases the capacity to generate coherent text sequences post-training. Training and validation losses have been reported, revealing the nuances in model performance and generalization capabilities. The generated text samples postulate the model's potential in capturing the contextual flow of the dataset. This study further plots the loss curves, visually representing the training dynamics and convergence patterns. The final model, encapsulated within a PyTorch framework, presents a step forward in the realm of neural text generation, contributing to the ongoing advancements in language modeling and its applications in understanding and generating human-like text.

Pre-Service Elementary Teachers Learning to Plan Modeling-Based Investigations

ABSTRACT Learning to plan modeling-based investigations (MBIs) presents significant challenges for pre-service elementary teachers. Although they understand disciplinary core ideas and modeling practices, they address both as competing learning goals and, as novices, they have not experienced how to structure MBIs for their future students. In the context of a science teacher education course, we have designed a set of pedagogical supports around a biology-specific epistemic tool to help pre-service elementary teachers meet those challenges. This study builds the case of how a group of four pre-service elementary teachers learns to plan MBIs while working with the designed supports. We use the professional vision as a theoretical and analytical framework to characterize the group’s learning through discourses to build shared professional understandings of planning MBIs around biology core ideas. Analysis focused on their highlighting and coding of relevant aspects of disciplinary core ideas, modeling, and structuring MBIs, as well as their creation and use of material representations. The findings illustrate that: a) epistemic negotiation supports the group in integrating biology core ideas and modeling practices when developing explanatory models, and b) pedagogical negotiation, through the de-construction of those explanatory models, supports the group structuring of a MBI, including a natural phenomenon and questions students can investigate in a coherent sequence. We discuss how a biology-specific epistemic tool embedded in pedagogical supports helps pre-service teachers learn to plan MBIs by facilitating both epistemic and pedagogical negotiations.

Cutting without a Knife: A Slice-Selective 2D 1H-13C HSQC NMR Sequence for the Analysis of Inhomogeneous Samples.

Nuclear magnetic resonance (NMR) is a powerful technique with applications ranging from small molecule structure elucidation to metabolomics studies of living organisms. Typically, solution-state NMR requires a homogeneous liquid, and the whole sample is analyzed as a single entity. While adequate for homogeneous samples, such an approach is limited if the composition varies as would be the case in samples that are naturally heterogeneous or layered. In complex samples such as living organisms, magnetic susceptibility distortions lead to broad 1H line shapes, and thus, the additional spectral dispersion afforded by 2D heteronuclear experiments is often required for metabolite discrimination. Here, a novel, slice-selective 2D, 1H-13C heteronuclear single quantum coherence (HSQC) sequence was developed that exclusively employs shaped pulses such that only spins in the desired volume are perturbed. In turn, this permits multiple volumes in the tube to be studied during a single relaxation delay, increasing sensitivity and throughput. The approach is first demonstrated on standards and then used to isolate specific sample/sensor elements from a microcoil array and finally study slices within a living earthworm, allowing metabolite changes to be discerned with feeding. Overall, slice-selective NMR is demonstrated to have significant potential for the study of layered and other inhomogeneous samples of varying complexity. In particular, its ability to select subelements is an important step toward developing microcoil receive-only arrays to study environmental toxicity in tiny eggs, cells, and neonates, whereas localization in larger living species could help better correlate toxin-induced biochemical responses to the physical localities or organs involved.

Rhythm is a Dancer: Music-Driven Motion Synthesis with Global Structure.

Synthesizing human motion with a global structure, such as a choreography, is a challenging task. Existing methods tend to concentrate on local smooth pose transitions and neglect the global context or the theme of the motion. In this work, we present a music-driven motion synthesis framework that generates long-term sequences of human motions which are synchronized with the input beats, and jointly form a global structure that respects a specific dance genre. In addition, our framework enables generation of diverse motions that are controlled by the content of the music, and not only by the beat. Our music-driven dance synthesis framework is a hierarchical system that consists of three levels: pose, motif, and choreography. The pose level consists of an LSTM component that generates temporally coherent sequences of poses. The motif level guides sets of consecutive poses to form a movement that belongs to a specific distribution using a novel motion perceptual-loss. And the choreography level selects the order of the performed movements and drives the system to follow the global structure of a dance genre. Our results demonstrate the effectiveness of our music-driven framework to generate natural and consistent movements on various dance types, having control over the content of the synthesized motions, and respecting the overall structure of the dance.

Sequences in harmony: Cognitive interactions between musical and visual narrative structure

From film and television to graphic storytelling, tonal music can accompany visual narratives in a variety of contexts. The apprehension of both musical and narrative sequences involves temporal categories in ordered patterning, which raises an interesting question: Do musical progressions and visual narratives rely on shared sequence processing mechanisms? If this is the case, then cues from music and sequential static images, when presented simultaneously, should interact during audiovisual online processing. We tested this question by measuring reaction times to target picture panels appearing in visual narrative (comic strip) sequences, which were presented panel by panel and synchronized with musical chord progressions. Image sequences were either coherent narratives or incoherent (random) panels, and they were aligned with musical accompaniment consisting of coherent tonal chord progressions or non-tonal (unrelated) chords. Reaction times were faster for target images in coherent sequences than incoherent sequences, and even faster for coherent images with tonal accompaniment than non-tonal chords. This indicated an interaction between sequential structures across domains. We take these results as evidence for a shared, domain-general sequence processing mechanism operating across music and visual narrative.

Detecting and Grounding Important Characters in Visual Stories

Characters are essential to the plot of any story. Establishing the characters before writing a story can improve the clarity of the plot and the overall flow of the narrative. However, previous work on visual storytelling tends to focus on detecting objects in images and discovering relationships between them. In this approach, characters are not distinguished from other objects when they are fed into the generation pipeline. The result is a coherent sequence of events rather than a character-centric story. In order to address this limitation, we introduce the VIST-Character dataset, which provides rich character-centric annotations, including visual and textual co-reference chains and importance ratings for characters. Based on this dataset, we propose two new tasks: important character detection and character grounding in visual stories. For both tasks, we develop simple, unsupervised models based on distributional similarity and pre-trained vision-and-language models. Our new dataset, together with these models, can serve as the foundation for subsequent work on analysing and generating stories from a character-centric perspective.

An improved algorithm for design of broadband excitation, inversion, and mixing pulse sequences by iterative optimization of phases: TOPS-2

This paper presents an improved iterative algorithm (TOPS-2) for the design of broadband inversion, excitation and coherent transfer mixing sequence (TOCSY) pulses. The evolution of the Bloch vector is presented as a sequence of small constant flip angle pulses with varying phases and constant amplitude. This paper describes an improved algorithm for iterative optimization of piece-wise constant phases as we incorporate the quadratic terms in the propagators. In our iterative optimization we obtain a closed-form expression for each phase, and these phases are optimized sequentially using the new improved algorithm. This paper compares the simulation results of the TOPS vs TOPS-2 and shows that TOPS-2 perform better. Experimental validation of excitation and inversion TOPS-2 pulse sequence is performed with .5% H2O in 99.5% D2O, and experimental validation of TOPS-2 mixing (TOCSY) pulse sequence is done with 0.1% of Ethylbenzene (EB) in CDCl3 solvent.

A rationale-augmented NLP framework to identify unilateral contractual change risk for construction projects

Construction projects often see owners who exert their dominance by modifying the standard contracts without contractors’ prior consent. This can lead to undesirable outcomes for contractors, thus referred to as unilateral contractual change risk (UCCR) in this study. As such, identifying UCCR proactively becomes essential for contractors, particularly in engineering-procurement-construction (EPC) projects where the claim scope is limited. Although natural language processing (NLP) has shown promise in the identification of UCCR, it faces difficulties due to the high cognitive demands required. To fill the gap, we propose a rational-augmented NLP framework that emulates human reasoning to identify UCCR in EPC contracts in an explainable and effective manner. The framework (1) leverages NLP techniques to disassemble contract text into features that draw the attention of human readers for obligation-related comprehension, and (2) generates a coherent sequence of intermediate reasoning steps using a customized Microsoft Excel and-in interface to identify predetermined categories of UCCR. Our framework, which achieves an outstanding F1 score of 0.87, is trained on a widely used standard form of EPC contract. It also presents a user-friendly interface for contractors to discern any intentional or malicious acts committed by the owner during the contract stage. Furthermore, our methodology can be adapted to enhance risk management in other sectors.

Drill-Rep: Repetition counting for automatic shot hole depth recognition based on combined deep learning-based model

In order to solve the problems of low efficiency and dependability of human experience in shot hole depth recognition by manual interpretation of the massive drilling videos, this paper proposed a repetition counting framework from the perspective of representation object motion, and firstly studied and realized the automatic shot hole depth recognition by adopting the proposed framework by combining multiple deep learning-based models. Firstly, the periodic motion characteristics of the objects in the drilling video were analyzed, and the motion extraction model of the representative object was established based on YOLOv5 and Deep SORT algorithm to obtain the spatiotemporal sequence. Then, the sequence noise and redundant information were filtered according to the spatiotemporal relationship between the drill pipes and workers, to obtain a coherent, smooth, and clean motion sequence. On this basis, a drill pipe motion counting model was established by using the YOLOv5 network to detect the temporal boundary and repetition number of the drill pipe motion. Finally, combined with the pipe motion extraction model and counting model, the comprehensive reliable recognition method of shot hole depth was presented. The shot hole depth recognition trial was carried out on 1029 drilling videos from the Bureau of Geophysical Prospecting INC. The proposed method achieved 90.5% recognition accuracy (Acc) and 0.027 mean absolute error (MAE) with 64 frames per second (FPS) and 0.39 average recognition time ratio (Rt). Therefore, the proposed repetition counting framework solved the problem of efficient and intelligent drilling depth recognition of massive drilling videos and thus provided a new idea for repetition counting.

Methods in molecular biogeography: The case of New Caledonia

AbstractAimTo examine the different methods currently used in molecular biogeography. Methods of interpreting evolution in space (ancestral‐area algorithms) always find a centre of origin for a group in the region of a paraphyletic basal grade, although regionally restricted basal grades can also be generated by simple vicariance. Current analyses of the timeline of evolution are usually based on the conversion of fossil‐calibrated ages (minimum clade ages) into maximum clade ages by imposing arbitrary, subjective priors. Thus, methods of analysing both space and time in evolution are flawed in theory. They are also inefficient in practice, as indicated by recent papers on the history of the New Caledonian biota, examined here as a case‐study. Work using current methods has left the phenomena as unexplained ‘conundrums’ and ‘enigmas’.LocationNew Caledonia and surrounding areas.TaxonVarious plants and animals.MethodsThe method favoured here is a synthesis of biogeography and geology. Tectonic features that coincide spatially with phylogenetic breaks (nodes) are identified. Fossils are used to provide estimates of minimum clade age, while the age of the tectonic features provides estimate of actual clade age. If the sequence of nodes in the phylogeny and the chronological sequence of the tectonic events match, a coherent sequence of vicariance events is indicated.ResultsSeveral critical studies on New Caledonian biogeography have been published in the last 5 years. The results from these can be analysed using revised methodology and integrated to give an alternative model of regional history.Main ConclusionsThe synthesis of geology and biology suggests a new interpretation of the New Caledonian biota, one in which the key processes are tectonic history, vicariance and metapopulation dynamics, rather than chance dispersal (as a mode of speciation), adaptation and radiation. The new model recognises the autochthonous, Mesozoic roots of many New Caledonian lineages.

Supporting Guided Exploratory Visual Analysis on Time Series Data with Reinforcement Learning.

The exploratory visual analysis (EVA) of time series data uses visualization as the main output medium and input interface for exploring new data. However, for users who lack visual analysis expertise, interpreting and manipulating EVA can be challenging. Thus, providing guidance on EVA is necessary and two relevant questions need to be answered. First, how to recommend interesting insights to provide a first glance at data and help develop an exploration goal. Second, how to provide step-by-step EVA suggestions to help identify which parts of the data to explore. In this work, we present a reinforcement learning (RL)-based system, Visail, which generates EVA sequences to guide the exploration of time series data. As a user uploads a time series dataset, Visail can generate step-by-step EVA suggestions, while each step is visualized as an annotated chart combined with textual descriptions. The RL-based algorithm uses exploratory data analysis knowledge to construct the state and action spaces for the agent to imitate human analysis behaviors in data exploration tasks. In this way, the agent learns the strategy of generating coherent EVA sequences through a well-designed network. To evaluate the effectiveness of our system, we conducted an ablation study, a user study, and two case studies. The results of our evaluation suggested that Visail can provide effective guidance on supporting EVA on time series data.