Reaching the last mile: best practices in leveraging the power of ICTs to communicate climate services to farmers at scale

This report reviews key ICTs for Development (ICT4D) Programs, Innovations and Information Exchange Platforms which are experimented within South Asia to explore the use and scale-ability of these innovative approaches to other parts of Africa and the developing world. Learning from the pioneering experiences of pilot projects across India and Africa in ICT development, we assess the potential ICTs offer to not only communicate climate information and related advisory services but also to build capacity and increase the resilience of rural smallholders. It is our hope that such South-South learning can pave the way for improved cross-regional experience sharing to tackle common challenges in reaching ‘the last mile’ with salient rural extension services, including climate information services

Reason out Your Layout: Evoking the Layout Master from Large Language Models for Text-to-Image Synthesis

Recent advancements in text-to-image (T2I) generative models have shown remarkable capabilities in producing diverse and imaginative visuals based on text prompts. Despite the advancement, these diffusion models sometimes struggle to translate the semantic content from the text into images entirely. While conditioning on the layout has shown to be effective in improving the compositional ability of T2I diffusion models, they typically require manual layout input. In this work, we introduce a novel approach to improving T2I diffusion models using Large Language Models (LLMs) as layout generators. Our method leverages the Chain-of-Thought prompting of LLMs to interpret text and generate spatially reasonable object layouts. The generated layout is then used to enhance the generated images' composition and spatial accuracy. Moreover, we propose an efficient adapter based on a cross-attention mechanism, which explicitly integrates the layout information into the stable diffusion models. Our experiments demonstrate significant improvements in image quality and layout accuracy, showcasing the potential of LLMs in augmenting generative image models.Comment: preprin

Privacy-Preserving Predictive Modeling: Harmonization of Contextual Embeddings From Different Sources

Background: Data sharing has been a big challenge in biomedical informatics because of privacy concerns. Contextual embedding models have demonstrated a very strong representative capability to describe medical concepts (and their context), and they have shown promise as an alternative way to support deep-learning applications without the need to disclose original data. However, contextual embedding models acquired from individual hospitals cannot be directly combined because their embedding spaces are different, and naive pooling renders combined embeddings useless. Objective: The aim of this study was to present a novel approach to address these issues and to promote sharing representation without sharing data. Without sacrificing privacy, we also aimed to build a global model from representations learned from local private data and synchronize information from multiple sources. Methods: We propose a methodology that harmonizes different local contextual embeddings into a global model. We used Word2Vec to generate contextual embeddings from each source and Procrustes to fuse different vector models into one common space by using a list of corresponding pairs as anchor points. We performed prediction analysis with harmonized embeddings. Results: We used sequential medical events extracted from the Medical Information Mart for Intensive Care III database to evaluate the proposed methodology in predicting the next likely diagnosis of a new patient using either structured data or unstructured data. Under different experimental scenarios, we confirmed that the global model built from harmonized local models achieves a more accurate prediction than local models and global models built from naive pooling. Conclusions: Such aggregation of local models using our unique harmonization can serve as the proxy for a global model, combining information from a wide range of institutions and information sources. It allows information unique to a certain hospital to become available to other sites, increasing the fluidity of information flow in health care

A three-DOF ultrasonic motor using four piezoelectric ceramic plates in bonded-type structure

A three-DOF ultrasonic motor is presented in this paper. The proposed motor consists of four piezoelectric ceramic plates and a mental base with a flange that can fix the motor on a rack. The proposed motor takes advantage of a longitudinal mode and two bending modes, different hybrids of which can realize three-DOF actuation. Because of symmetric structure of the proposed motor, the resonance frequencies of the two bending modes are identical. And the resonance frequency of the longitudinal mode was tuned closed to the ones of the bending modes by adjusting the structural parameters in modal analysis. Then trajectories of nodes on the driving foot were obtained by the transient analysis to verify the feasibility of driving principle. Experiments including vibration shape test and output characteristic test were executed. The starting voltages of the rotation along horizontal axes are about 10 Vp-p. Under driving voltages of 200 Vp-p, the output velocities of three DOF can reach 280 rpm, 277 rpm and 327 rpm, respectively. The results of the experiments indicate that the proposed motor is characterized by low starting voltages and high output velocities

Let Models Speak Ciphers: Multiagent Debate through Embeddings

Discussion and debate among Large Language Models (LLMs) have gained considerable attention due to their potential to enhance the reasoning ability of LLMs. Although natural language is an obvious choice for communication due to LLM's language understanding capability, the token sampling step needed when generating natural language poses a potential risk of information loss, as it uses only one token to represent the model's belief across the entire vocabulary. In this paper, we introduce a communication regime named CIPHER (Communicative Inter-Model Protocol Through Embedding Representation) to address this issue. Specifically, we remove the token sampling step from LLMs and let them communicate their beliefs across the vocabulary through the expectation of the raw transformer output embeddings. Remarkably, by deviating from natural language, CIPHER offers an advantage of encoding a broader spectrum of information without any modification to the model weights. While the state-of-the-art LLM debate methods using natural language outperforms traditional inference by a margin of 1.5-8%, our experiment results show that CIPHER debate further extends this lead by 1-3.5% across five reasoning tasks and multiple open-source LLMs of varying sizes. This showcases the superiority and robustness of embeddings as an alternative "language" for communication among LLMs

Analysis of the Accumulation of Major Aroma Components in Japanese Apricot Fruit (Prunus mume Siebold et Zucc.) during Ripening

The major characteristic aroma components of Japanese apricot fruit grown in Dayi county, Sichuan Province were determined by headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS) based on odor activity values (OAVs). The pattern of accumulation of the major aroma components was investigated by analysis of aroma precursors and their correlation with climate factors was analyzed. The results showed that ethyl butyrate, β-myrcene, ethyl 3-methyl-butyrate, benzaldehyde and nonanal were the major characteristic aroma substances of Japanese apricot fruit, and C6 and C9 compounds were the major aroma components. C6 aroma substances had a high correlation with unsaturated fatty acid precursors. There was a positive correlation between the synthesis of C6 and C9 aroma substances. Climate significantly affected aroma accumulation during fruit ripening. Precipitation was the key factor affecting the content of C6 substances in the early ripening stage, mainly affecting the accumulation of bound hexenol. At the late stage of maturity, air temperature had a great influence on the content of free substances such as hexanol and hexanoic acid. These results provide a basis for follow-up research to analyze the flavor and quality of processed Japanese apricot fruit, explore the effects of climate factors on Japanese apricot fruit and its products, and identify the production region of raw materials and processed products for flavor evaluation

Mild traumatic brain injury is associated with effect of inflammation on structural changes of default mode network in those developing chronic pain

BACKGROUND: Mild traumatic brain injury (mTBI) has a higher prevalence (more than 50%) of developing chronic posttraumatic headache (CPTH) compared with moderate or severe TBI. However, the underlying neural mechanism for CPTH remains unclear. This study aimed to investigate the inflammation level and cortical volume changes in patients with acute PTH (APTH) and further examine their potential in identifying patients who finally developed CPTH at follow-up. METHODS: Seventy-seven mTBI patients initially underwent neuropsychological measurements, 9-plex panel of serum cytokines and MRI scans within 7 days post-injury (T-1) and 54 (70.1%) of patients completed the same protocol at a 3-month follow-up (T-2). Forty-two matched healthy controls completed the same protocol at T-1 once. RESULTS: At baseline, mTBI patients with APTH presented significantly increased GM volume mainly in the right dorsal anterior cingulate cortex (dACC) and dorsal posterior cingulate cortex (dPCC), of which the dPCC volume can predict much worse impact of headache on patients\u27 lives by HIT-6 (β = 0.389, P = 0.007) in acute stage. Serum levels of C-C motif chemokine ligand 2 (CCL2) were also elevated in these patients, and its effect on the impact of headache on quality of life was partially mediated by the dPCC volume (mean [SE] indirect effect, 0.088 [0.0462], 95% CI, 0.01-0.164). Longitudinal analysis showed that the dACC and dPCC volumes as well as CCL2 levels had persistently increased in patients developing CPTH 3 months postinjury. CONCLUSION: The findings suggested that structural remodelling of DMN brain regions were involved in the progression from acute to chronic PTH following mTBI, which also mediated the effect of inflammation processes on pain modulation. TRIAL REGISTRATION: ClinicalTrial.gov ID: NCT02868684 ; registered 16 August 2016