Coherent Response Research Articles

Managers of complex change? How United Nations treaty secretariats jointly govern institutional interplay in global environmental governance

AbstractResearch on international bureaucracies, such as treaty secretariats, has predominantly focused on broadening our understanding of their role, function, and influence within their respective regulatory domains. However, the potential for treaty secretariats to manage situations of institutional overlap by coordinating with other agencies across policy areas has remained understudied. This article offers new empirical and theoretical insights for studying collective agency and coordination mechanisms in instances of institutional interaction within hybrid regime complexes. Specifically, it investigates how the treaty secretariats of the Rio Conventions under the United Nations employ joint interplay management as a means to improve institutional coherence within the climate change, biodiversity loss, and desertification nexus. Collectively, the public agencies aim to advance knowledge and discourse, influence norm‐building processes and regulation, or build capacity and support the joint implementation of policy objectives addressing the interlinked environmental problems. They do this by interacting with various actors across governance levels, including national governments, transnational initiatives, private actors, or civil society. By tracing the process linking joint activities with effects of such interactions, this qualitative case study makes a conceptual contribution by extrapolating a mechanistic theory for joint interplay management. The article demonstrates that treaty secretariats have to contend with challenges of resource allocation, diverging mandates, leadership priorities, and the degree of politicization and timing which frequently intervene stages of strategizing and executing joint activities. The results highlight that joint interplay management can be most impactful when secretariats employ orchestration practices through joint outreach and advocacy to advance coherent institutional responses to interdependent environmental problems.

2D Time-Domain Spectroscopy for Determination of Energy and Momentum Relaxation Rates of Hydrogen-Like Donor States in Germanium.

We present measurements of the coherence times of excited states of hydrogen-like arsenic impurities in germanium (Ge:As) using a table-top two-dimensional time-domain spectroscopy (2D-TDS) system. We show that this laboratory system is capable of resolving the coherence lifetimes of atomic-like excited levels of impurity centers in semiconductors, such as those used in solid-state quantum information technologies, on a subpicosecond time scale. By fitting the coherent nonlinear response of the system with the known intracenter transition frequencies, we are able to monitor coherent population transfer and decay of the transitions from the 2p0 and 2p± states for different low excitation pulse fields. Furthermore, by examining the off-diagonal resonances in the 2D frequency-domain map, we are able to identify coherences between excited electronic states that are not visible via conventional single-frequency pump-probe or Hahn-echo measurements.

Abstract 1296: Evaluating the accuracy and reproducibility of ChatGPT models in answering lung cancer patient queries

Abstract Large language models (LLMs) such as ChatGPT can imitate human conversation and produce rapid, coherent responses, which may mask their potential for inaccuracies. With patients increasingly turning to the internet for medical information, the use of LLM chatbots for cancer-related queries risks spreading misinformation. Our study assessed ChatGPT’s accuracy and reproducibility in offering valid information and treatment advice for lung cancer in line with established guidelines. In the evolving landscape of AI-driven healthcare support, the ability of language models to provide accurate and reliable information is crucial. Our study delves into the effectiveness of OpenAI's ChatGPT models (versions 3.5 and 4.0) in responding to patient inquiries about lung cancer across various domains including general information, clinical presentation, risk factors, screening, diagnosis, staging, treatment options, prognosis, post-treatment follow-up, lifestyle recommendations, and psychosocial/educational aspects. We conducted a structured assessment, posing identical sets of questions to both ChatGPT 3.5 and 4.0. A total of 47 questions were posed with each query being repeated twice per model to evaluate both the accuracy and reproducibility of the responses. The scoring system focused on the accuracy and comprehensiveness of each response. Our findings revealed a notable disparity in the performance of the two models. GPT 4.0 demonstrated higher consistency and accuracy, with 41 out of 47 (87.2%) responses deemed accurate and comprehensive, compared to 36 out of 47 (76.6%) for GPT 3.5. In terms of reproducibility, both models exhibited strong performance: 42 out of 47 (89.3%) for GPT 3.5 and 45 out of 47 for GPT 4.0 (95.7%). When comparing responses between the models, we observed good reproducibility in 38 out of 47 questions (80.8%). A key observation was that GPT 4.0 significantly outperformed its predecessor GPT 3.5 in terms of both accuracy as well as reproducibility within its own responses, indicating a more reliable and consistent performance. The area most lacking in accuracy for both models was lung cancer staging, indicating a need for further refinement in this domain. Another key observation was the models' tendency to incorporate empathetic language, often beginning responses with expressions of sympathy and consistently advising confirmation with a medical professional. Our study underscores the potential and limitations of current AI models in patient education and support, highlighting areas for improvement and the importance of empathetic communication in AI interactions with patients. As the model continues to be trained on a larger and more comprehensive set of data, it is reasonable to anticipate further improvements in its ability to provide precise, detailed, and contextually appropriate responses. Citation Format: Asiyah Allibhai, Ahmed Allibhai, Anthony Brade, Zishan Allibhai. Evaluating the accuracy and reproducibility of ChatGPT models in answering lung cancer patient queries [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1296.

Gemini versus ChatGPT: applications, performance, architecture, capabilities, and implementation

This research paper presents an in-depth comparative examination of Gemini and ChatGPT, two prominent conversational AI models, exploring their respective applications, performance metrics, architectural variances, and overall capabilities. As conversational AI becomes increasingly prevalent across industries, comprehending the nuances of these models becomes pivotal for effective deployment. The paper initiates by outlining the wide array of applications for both Gemini and ChatGPT, spanning industries such as customer service, construction, finance, education, healthcare, and entertainment. It analyzes how each model addresses specific use cases, emphasizing their flexibility and potential impact across different sectors. Following this, the study assesses the performance of Gemini and ChatGPT through both empirical benchmarks and real-world deployment scenarios. Key metrics, including response coherence, accuracy, latency, and scalability, are scrutinized to gauge the models' ability to generate contextually appropriate and coherent responses in conversational contexts. Moreover, the paper elucidates the architectural distinctions between Gemini and ChatGPT, covering variances in training methodologies, model architectures, and underlying technologies. Understanding these architectural nuances provides deeper insights into the computational mechanisms underpinning each model's performance. Lastly, the paper explores the capabilities of Gemini and ChatGPT in handling complex linguistic phenomena, deciphering user intents, and sustaining engaging dialogues over prolonged interactions. This discussion encompasses language generation, sentiment analysis, context retention, and ethical considerations, shedding light on the potential of these models to facilitate meaningful human-computer interactions. Through this thorough comparative analysis, the research contributes to the ongoing conversation surrounding conversational AI systems. It offers valuable insights into the strengths and limitations of Gemini and ChatGPT, empowering stakeholders to make informed decisions regarding their optimal utilization across diverse applications.

Coherent long-term body-size responses across all Northwest Atlantic herring populations to warming and environmental change despite contrasting harvest and ecological factors.

Body size is a key component of individual fitness and an important factor in the structure and functioning of populations and ecosystems. Disentangling the effects of environmental change, harvest and intra- and inter-specific trophic effects on body size remains challenging for populations in the wild. Herring in the Northwest Atlantic provide a strong basis for evaluating hypotheses related to these drivers given that they have experienced significant warming and harvest over the past century, while also having been exposed to a wide range of other selective constraints across their range. Using data on mean length-at-age 4 for the sixteen principal populations over a period of 53 cohorts (1962-2014), we fitted a series of empirical models for temporal and between-population variation in the response to changes in sea surface temperature. We find evidence for a unified cross-population response in the form of a parabolic function according to which populations in naturally warmer environments have responded more negatively to increasing temperature compared with those in colder locations. Temporal variation in residuals from this function was highly coherent among populations, further suggesting a common response to a large-scale environmental driver. The synchrony observed in this study system, despite strong differences in harvest and ecological histories among populations and over time, clearly indicates a dominant role of environmental change on size-at-age in wild populations, in contrast to commonly reported effects of fishing. This finding has important implications for the management of fisheries as it indicates that a key trait associated with population productivity may be under considerably less short-term management control than currently assumed. Our study, overall, illustrates the need for a comparative approach within species for inferences concerning the many possible effects on body size of natural and anthropogenic drivers in the wild.

Dialogue Generation Model with Hierarchical Encoding and Semantic Segmentation of Dialogue Context

Dialogue generation, as a crucial subtask of dialogue systems, is garnering increasing attention in the field of Natural Language Processing (NLP). The success of dialogue generation relies on effectively utilizing context information to ensure coherent and diverse responses. However, current approaches heavily rely on external sources rather than leveraging the inherent dialogue content. We propose a new approach to address this challenge by introducing semantic segmentation from the field of image processing into NLP. Our contribution lies in the development of a Dialogue Generation model with Hierarchical Encoding and Semantic segmentation of dialogue Context, which is called DGHESC. This model is topic and speaker-aware, capturing the flow of topic and speaker information within the dialogue context using a hierarchical transformer-based framework. Specifically, we extract semantic information at the word-level for each utterance, segment the dialogue context based on topic and speaker semantics, and employ attention mechanisms to model the context at the utterance-level. Experimental results on two open-domain datasets demonstrate the effectiveness of DGHESC. It enhances response quality and achieves state-of-the-art performances on the datasets.

Active acoustic metamaterials with independently programmable bulk modulus and full mass density tensor

Active acoustic metamaterials consist of unit cells with sensor-driver pairs that produce a coherent response to incident waves. The effective acoustic properties of the metamaterials depend on the gains programmed between the sensing and driving components. The strength of the monopole response to the local pressure determines the effective bulk modulus and the dipole response to the local particle velocity determines the effective mass density. The unit cells are controlled individually, so in theory the metamaterials can be scaled to an arbitrary size and geometry, but prior realizations were limited to only a few cells with 1D operation. Here, we present an active acoustic metamaterial of nine cells in 2D with programmable bulk modulus and mass density tensor. We demonstrate the ability to independently program the property components (bulk modulus and four mass density elements) for any desired set within stable limits, including previously unattainable acoustic properties necessary for the fabrication of transformation acoustics devices. We also demonstrate complex effective properties, specifically a lossy layer with impedance matched to the background, such that incident waves are absorbed with no reflection. The effective properties of the active metamaterial are validated by comparing the experimental total and scattered fields to ideal simulation results.

International family justice as collaborative justice

AbstractGlobalization has fueled the rise of international family disputes, which raise difficult legal issues that cannot be addressed by any court or jurisdiction working alone. These challenges require a considered and coherent response on the international front, supported by the willingness of individual family judges to communicate and cooperate to identify and implement practical solutions. To meet these new challenges, this paper proposes a vision of international family justice as collaborative justice. There are three aspects or phases to the proposed endeavor: (a) the articulation of common aspirations and values; (b) continuing cooperation and communication between family courts and institutions; and (c) convergence in the substantive norms and practice of international family law.

Coherent response of inhomogeneously broadened and spatially localized emitter ensembles in waveguide QED

Coherent response of inhomogeneously broadened and spatially localized emitter ensembles in waveguide QED

Identifying temporal correlations between natural single-shot videos and EEG signals

Objective. Electroencephalography (EEG) is a widely used technology for recording brain activity in brain-computer interface (BCI) research, where understanding the encoding-decoding relationship between stimuli and neural responses is a fundamental challenge. Recently, there is a growing interest in encoding-decoding natural stimuli in a single-trial setting, as opposed to traditional BCI literature where multi-trial presentations of synthetic stimuli are commonplace. While EEG responses to natural speech have been extensively studied, such stimulus-following EEG responses to natural video footage remain underexplored. Approach. We collect a new EEG dataset with subjects passively viewing a film clip and extract a few video features that have been found to be temporally correlated with EEG signals. However, our analysis reveals that these correlations are mainly driven by shot cuts in the video. To avoid the confounds related to shot cuts, we construct another EEG dataset with natural single-shot videos as stimuli and propose a new set of object-based features. Main results. We demonstrate that previous video features lack robustness in capturing the coupling with EEG signals in the absence of shot cuts, and that the proposed object-based features exhibit significantly higher correlations. Furthermore, we show that the correlations obtained with these proposed features are not dominantly driven by eye movements. Additionally, we quantitatively verify the superiority of the proposed features in a match-mismatch task. Finally, we evaluate to what extent these proposed features explain the variance in coherent stimulus responses across subjects. Significance. This work provides valuable insights into feature design for video-EEG analysis and paves the way for applications such as visual attention decoding.

Visual Questioner using Bert Transformer

The Visual Questioner, an innovative machine learning endeavor, represents a cutting-edge project designed to process images and facilitate user interaction through AI-generated responses to inquiries. Focused on the fusion of visual recognition and natural language understanding, the overarching objective of this project is to cultivate a robust model capable of discerning intricate details within an image and, subsequently, formulating coherent textual responses to user-generated questions. At the core of the Visual Questioner lies the imperative to bridge the semantic gap between visual content and linguistic expression, enhancing the depth of comprehension and interaction within the artificial intelligence paradigm. By harnessing advanced algorithms and neural network architectures, this initiative aspires to elevate the sophistication of image-based question-answering systems, presenting a pivotal advancement in the realm of human-machine communication. This research not only delves into the technical intricacies of image understanding but also underscores the imperative role of natural language generation, thereby contributing substantively to the evolving landscape of AI applications. As we navigate through the complexities of this project, its potential implications span diverse domains, ranging from human-computer interaction to autonomous systems and beyond, underscoring the multifaceted significance of the Visual Questioner in contemporary artificial intelligence research.

The Game-Changing Impact of AI Chatbots on Education ChatGPT and Beyond

ChatGPT, an AI-driven chatbot, delivers coherent and valuable responses by analysing extensive data sets. This article explores the profound impact of ChatGPT on contemporary education, as discussed by prominent academics, scientists, distinguished researchers, and engineers. The study delves into ChatGPT's capabilities, its application in the education sector, and the identification of potential concerns and challenges. Initial assessments reveal variations in ChatGPT's performance across diverse subjects, including finance, coding, mathematics, and general public queries. While ChatGPT can assist educators by generating instructional content, providing suggestions, serving as an online educator for learners, answering questions and revolutionising education through smartphones and IoT devices, it has shortcomings. These include the risk of generating inaccurate or false information and evading plagiarism detection systems, a critical concern for maintaining originality. The commonly reported "hallucinations" in Generative AI, which applies to ChatGPT, also limit its utility in situations where precision is paramount. A notable deficiency in ChatGPT is the absence of a stochastic measure to facilitate authentic and empathetic communication with users. The article suggests that educational institutions' academic regulations and evaluation practices should be updated if ChatGPT is integrated into the educational toolkit. To effectively navigate the transformative effects of ChatGPT in the learning environment, it is imperative to educate teachers and students about its capabilities and limitations.

Atomic observation and structural evolution of covalent organic framework rotamers

Dynamic 3D covalent organic frameworks (COFs) have shown concerted structural transformation and adaptive gas adsorption due to the conformational diversity of organic linkers. However, the isolation and observation of COF rotamers constitute undergoing challenges due to their comparable free energy and subtle rotational energy barrier. Here, we report the atomic-level observation and structural evolution of COF rotamers by cryo-3D electron diffraction and synchrotron powder X-ray diffraction. Specifically, we optimize the crystallinity and morphology of COF-320 to manifest its coherent dynamic responses upon adaptive inclusion of guest molecules. We observe a significant crystal expansion of 29 vol% upon hydration and a giant swelling with volume change up to 78 vol% upon solvation. We record the structural evolution from a non-porous contracted phase to two narrow-pore intermediate phases and the fully opened expanded phase using n-butane as a stabilizing probe at ambient conditions. We uncover the rotational freedom of biphenylene giving rise to significant conformational changes on the diimine motifs from synclinal to syn-periplanar and anticlinal rotamers. We illustrate the 10-fold increment of pore volumes and 100% enhancement of methane uptake capacity of COF-320 at 100 bar and 298 K. The present findings shed light on the design of smarter organic porous materials to maximize host-guest interaction and boost gas uptake capacity through progressive structural transformation.

Functionally coherent transcriptional responses of Jatropha curcas and Pseudomonas fragi for rhizosphere mediated degradation of pyrene

Pyrene is an extremely hazardous, carcinogenic polycyclic aromatic hydrocarbon (PAH). The plant–microbe interaction between Pseudomonas fragi DBC and Jatropha curcas was employed for biodegradation of pyrene and their transcriptional responses were compared. The genome of P. fragi DBC had genes for PAH degrading enzymes i.e. dioxygenases and dehydrogenases, along with root colonization (trpD, trpG, trpE and trpF), chemotaxis (flhF and flgD), stress adaptation (gshA, nuoHBEKNMG), and detoxification (algU and yfc). The transcriptional expression of catA and yfc that respectively code for catabolic enzyme (catechol-1, 2-dioxygnase) and glutathione-s-transferase for detoxification functions were quantitatively measured by qPCR. The catA was expressed in presence of artificial root exudate with or without pyrene, and glucose confirming the non-selective approach of bacteria, as desired. Pyrene induced 100-fold increase of yfc expression than catA, while there was no expression of yfc in absence of pyrene. The transcriptome of plant roots, in presence of pyrene, with or without P. fragi DBC inoculation was analysed. The P. fragi DBC could upregulate the genes for plant growth, induced the systemic acquired resistance and also ameliorated the stress response in Jatropha roots.

TodBR: Target-Oriented Dialog with Bidirectional Reasoning on Knowledge Graph

Target-oriented dialog explores how a dialog agent connects two topics cooperatively and coherently, which aims to generate a “bridging” utterance connecting the new topic to the previous conversation turn. The central focus of this task entails multi-hop reasoning on a knowledge graph (KG) to achieve the desired target. However, current target-oriented dialog approaches suffer from inefficiencies in reasoning and the inability to locate pertinent key information without bidirectional reason. To address these limitations, we present a bidirectional reasoning model for target-oriented dialog implemented on a commonsense knowledge graph. Furthermore, we introduce an automated technique for constructing dialog subgraphs, which aids in acquiring multi-hop reasoning capabilities. Our experiments demonstrate that our proposed method attains superior performance in reaching the target while providing more coherent responses.

A data-driven methodology to design user-friendly tariffs in energy communities

In recent years, energy communities have emerged as a feasible solution to empower domestic end-users to engage in local power trading with their neighbours, in an attempt to improve the efficiency and economy of residential consumers. From a mercantilist point of view, launching local markets with eventual local electricity prices might be beneficial for community users as they are inhibited from external volatile prices and possible market imperfections. However, local pricing strategies should take into account users’ preferences and avoid undesirable effects of response fatigue (i.e. excessive number of response signals within a short-time period). This way, local electricity tariffs should be stable and send coherent response signals easily interpretable by users. In this sense, the necessity of developing proper designing tools for local electricity tariffs is clear. This paper focuses on this issue. In particular, the main novelties of this paper are twofold: on the one hand, the developed tool designs community tariffs over a year basis instead of daily spot prices, as made in existing approaches. Thereby, the resulting tariff keeps stable yearly similar to conventional tariffs offered by retailers worldwide. Secondly, the designed tariff takes into account the negative effects of response fatigue, so that the considered pricing mechanism limits the number of pricing signals sent to consumers, taking this feature as an external parameter. This way, the designer is able to tune up the total number of pricing signals that users received within a time period, thus ensuring that they are not discouraged to partake in the community. The proposed design approach is raised as a data-driven framework, taking advantage of real databases collecting demand, renewable generation and retailer prices. Such profiles serve as inputs for a designed bi-level Stackelberg-based problem, in which the reaction of prosumers is implicitly assumed. A case study is conducted on a benchmark energy community. Different tariff mechanisms are analysed such as flat, time-of-use and happy hours tariffs. The results obtained serve to validate the new proposal as well as analyse the effect of local market mechanisms in energy communities.

Assessing the Acceptance and Utilization of ChatGPT by Chinese University Students in English Writing Education

As an advanced large language model, ChatGPT has garnered significant attention from academic researchers and educators due to its capabilities in generating coherent natural-language responses based on user input, facilitating extended dialogues, and contextual comprehension. Numerous scholarly investigations delve into the merits and potential shortcomings of ChatGPT within educational contexts, emphasizing both the model’s inherent attributes and the scarcity of end-user feedback. Furthermore, the Technology Acceptance Model and the Information System Success Model serve as prevalent frameworks in assessing user receptivity and satisfaction with novel technologies. This research employed an online questionnaire to probe Chinese university students’ perceptions and adoption of ChatGPT in English writing instruction, assessing five distinct dimensions using a Likert scale. The findings suggest a favorable inclination among students towards ChatGPT concerning its utility and effectiveness. However, ChatGPT has yet to supplant the indispensable role of educators entirely, and apprehensions persist about potential privacy infringements and associated technological issues.

Multiple coherent amplitude modes and exciton-phonon coupling in quasi-one-dimensional excitonic insulator Ta2NiSe5.

An excitonic insulator (EI) is an intriguing correlated electronic phase of condensed excitons. Ta2NiSe5 is a model material for investigating condensed excitonic states. Herein, femtosecond pump-probe spectroscopy is used to study the coherent phonon dynamics and associated exciton-phonon coupling in single-crystal Ta2NiSe5. The reflectivity time series consists of exponential decay due to hot carriers and damped oscillations due to the Ag phonon vibration. Given the in-plane anisotropic thermal conductivity of Ta2NiSe5, coherent phonon oscillations are stronger with perpendicular polarization to its quasi-one-dimensional chains. The 1-, 2-, and 4-THz vibration modes show coherent amplitude responses in the EI phase of Ta2NiSe5 with increasing temperature, totally different from those of normal coherent phonons (the 3- and 3.7-THz modes). The amplitude modes at higher frequencies decouple with the EI order parameter at lower temperatures, as supported by theoretical analysis with a model Hamiltonian of the exciton-phonon coupling system. Our work provides valuable insights into the character of the EI order parameter and its coupling to multiple coherent amplitude modes.

Towards Building an Open-Domain Dialogue System Incorporated With Internet Memes

In recent years, Internet memes have been widely used in online chatting. Compared with text-based communication, conversations become more expressive and attractive when Internet memes are incorporated. This paper presents our solutions for the Meme incorporated Open-domain Dialogue (MOD) Challenge of DSTC10, where three tasks are involved: text response modeling, meme retrieval, and meme emotion classification. Firstly, we leverage a large-scale pre-trained dialogue model for coherent and informative response generation. Secondly, based on interaction-based text-matching, our approach can retrieve appropriate memes with good generalization ability. Thirdly, we propose to model the emotion flow (EF) in conversations and introduce an auxiliary task of emotion description prediction (EDP) to boost the performance of meme emotion classification. Experimental results on the MOD dataset demonstrate that our methods can incorporate Internet memes into dialogue systems effectively.

Enhanced double resonance Raman scattering in multilayer graphene with broadband coherent anti-Stokes Raman spectroscopy.

Graphene's unique gapless band structure and remarkably large third-order optical susceptibility have drawn significant attention to its nonlinear optical response, particularly in the context of coherent anti-Stokes Raman scattering (CARS). Under the combined influence of phononic and electronic resonances, the CARS response of graphene has been observed to exhibit a distinctive feature of time-resolved dip-to-peak evolution. Here, we report a greatly enhanced double resonance Raman mode beyond the G mode of multi-layer graphene with broadband CARS measurements. The significant difference in the intensity ratio between CARS and SR for this mode may be attributed to the preferential activation of low-frequency phonons in the impulsive stimulated Raman scattering process (ISRS) and a lower dephasing rate. Our results build on a foundation towards a deeper exploration of the coherent Raman response of two-dimensional materials.