Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Long-Term Application of Inorganic and Organics on Soil Aggregate Fractions in Double Rice Cropping System

Analyses of physical structure of soil are usually done using physical fractionation methods, which are based on the premise that the association of the soil particles and their spatial arrangement play a key role in the function of SOM (Gregorich et al., 2006). The aggregation is a means to both protect and conserve soil organic carbon (SOC) and allow the stored organic matter to function as a reservoir of plant nutrients. Crop cultivation is known to adversely affect the distribution and stability of soil aggregates and reduces SOC stock in soils (Six et al., 2000; Kong et al., 2005). The impacts of cultivation on C stock have commonly been observed to be restricted mostly to surface soils and/or to root zone depth (Paustian et al., 1997b). However, different crop species have different effects on soil aggregation and C accumulation with varying soil depth. Altering soil physicochemical properties by management practices may increase one or more of the protective attributes which ultimately increases C in soils provided C inputs to soil do not decrease.
Global relevance of MGRS growth standards: the case of India

The most common measures of childhood undernutrition are based on anthropometric measures such as height-for-age (stunting/chronic undernutrition) and weight-for-height (wasting/acute undernutrition). It is well recognised that the determinants of undernutrition are multiple, including food intake, dietary diversity, health, sanitation and women’s status. Currently, most countries across the world including India use the globally accepted WHO-Multicentre Growth Reference Study (MGRS) growth standards (2006) for the purposes of measurement as well as for evaluating progress on these metrics. However, there is some discussion on the universal relevance of these standards, and in the Indian context, whether these standards overestimate the prevalence of stunting, considering differences in genetic potential for growth. This is especially relevant in the context of increasing burden of obesity and non-communicable diseases in India. Based on a detailed review of literature, policy documents and expert inputs, this review paper discusses the relevance of the WHO growth standards for height/stunting, in the context of India. Issues discussed related to the MGRS methodology include pooling of data and intersite and intrasite variability, opting for standards as opposed to references, and external validity. Other issues related to plasticity of stunting and the influence of maternal heights are also discussed, in the context of analysing the appropriateness of using universal growth standards. Based on the review, it is recommended that the current standards may continue to be used until a newer global standard is established through a similar study.
The reduction of sleep-like perilesional cortical dynamics underlies clinical recovery in stroke

Introduction. Recent studies have shown that, following brain injury, sleep-like cortical dynamics intrude into wakefulness, potentially contributing to brain network disruption and behavioral deficits. Aim. We employ TMS in combination with EEG to detect these dynamics and assess their impact on brain networks and clinical evolution in awake stroke patients. Methods. Twelve patients with subacute unilateral ischemic cortical stroke underwent a longitudinal study with two assessments (t0 and t1), including clinical evaluation using the National Institutes of Health Stroke Scale (NIHSS) and TMS-EEG recordings targeting perilesional and contralesional cortical sites. Parameters such as slow wave amplitude (SWa), high-frequency power (HFp) suppression, and the Perturbational Complexity Index-state transition (PCIst) were analyzed to quantify sleep-like cortical dynamics and their network-level consequences. Results. Results demonstrated a significant clinical improvement (NIHSS score: 7.16 +/- 0.73 at t0, 4.33 +/- 0.74 at t1; W=78, P<0.001). Perilesional SWa and HFp suppression decreased significantly at t1 compared to t0 (T(11)=3.05, P=0.01 and T(11)=-3.39, P<0.01, respectively), along with recovery of PCIst values (T(11)=-2.35, P=0.04). Importantly, both the dissipation of sleep-like perilesional cortical dynamics and the recovery of network-level interactions correlated with patients' clinical improvement (Spearman rho=0.62, P=0.03; rho=-0.68, P=0.01, respectively). Conclusion. These findings underscore the potential of TMS-EEG as an objective measure of neurological evolution and suggest targeting sleep-like cortical dynamics as a viable strategy for post-stroke neuromodulation and rehabilitation.
Predicting Empathic Accuracy from User-Designer Interviews

Measuring empathy as a natural language processing task has often been limited to a subjective measure of how well individuals respond to each other in emotive situations. Cognitive empathy, or an individual’s ability to accurately assess another individual’s thoughts, remains a more novel task. In this paper, we explore natural language processing techniques to measure cognitive empathy using paired sentence data from design interviews. Our findings show that an unsupervised approach based on similarity of vectors from a Large Language Model is surprisingly promising, while adding supervision does not necessarily improve the performance. An analysis of the results highlights potential reasons for this behaviour and gives directions for future work in this space.
Story Co-telling Dialogue Generation based on Multi-Agent Reinforcement Learning and Story Highlights

Retelling a story is one way to develop narrative skills in students, but it may present some challenges for English as Second Language (ESL) students who are learning new stories and vocabularies at the same time. The goal of this research is to develop a dialogue module for story co-telling for ESL students in order to help students to co-narrate an English story and enhance their narrative skills. However, story co-telling is a relatively underexplored and novel task. In order to understand the story content and select the right plot to continue the story co-telling based on the current dialogue, we utilize open domain information extraction techniques to construct a knowledge graph, and adopt multi-agent reinforcement learning methods to train two agents to select relevant facts from the knowledge graph and generate responses, jointly accomplishing the task of story co-telling. Compared to models that reply on chronological order, our model improves the performance from 67.0% to 70.8% through self-training with reward evaluation, achieving an increase of approximately 3.8%.
MCASP: Multi-Modal Cross Attention Network for Stock Market Prediction

Stock market prediction is considered a complex task due to the non-stationary and volatile nature of the stock markets. With the increasing amount of online data, various information sources have been analyzed to understand the underlying patterns of the price movements. However, most existing works in the literature mostly focus on either the intra-modality information within each input data type, or the inter-modal relationships among the input modalities. Different from these, in this research, we propose a novel Multi-Modal Cross Attention Network for Stock Market Prediction (MCASP) by capturing both modality-specific features and the joint influence of each modality in a unified framework. We utilize financial news, historical market data and technical indicators to predict the movement direction of the market prices. After processing the input modalities with three separate deep networks, we first construct a self-attention network that utilizes multiple Transformer models to capture the intra-modal information. Then we design a novel cross-attention network that processes the inputs in pairs to exploit the cross-modal and joint information of the modalities. Experiments with real world datasets for S&P500 index forecast and the prediction of five individual stocks, demonstrate the effectiveness of the proposed multi-modal design over several state-of-the-art baseline models.
A Prompt in the Right Direction: Prompt Based Classification of Machine-Generated Text Detection

The goal of ALTA 2023 Shared Task is to distinguish between human-authored text and synthetic text generated by Large Language Models (LLMs). Given the growing societal concerns surrounding LLMs, this task addresses the urgent need for robust text verification strategies. In this paper, we describe our method, a fine-tuned Falcon-7B model with incorporated label smoothing into the training process. We applied model prompting to samples with lower confidence scores to enhance prediction accuracy. Our model achieved a statistically significant accuracy of 0.991.
Natural Language Processing for Clinical Text

Learning from real-world clinical data has potential to promote the quality of care, improve the efficiency of healthcare systems, and support clinical research. As a large proportion of clinical information is recorded only in unstructured free-text format, applying NLP to process and understand the vast amount of clinical text generated in clinical encounters is essential. However, clinical text is known to be highly ambiguous, it contains complex professional terms requiring clinical expertise to understand and annotate, and it is written in different clinical contexts with distinct purposes. All these factors together make clinical NLP research both rewarding and challenging. In this tutorial, we will discuss the characteristics of clinical text and provide an overview of some of the tools and methods used to process it. We will also present a real-world example to show the effectiveness of different NLP methods in processing and understanding clinical text. Finally, we will discuss the strengths and limitations of large language models and their applications, evaluations, and extensions in clinical NLP.
Chat Disentanglement: Data for New Domains and Methods for More Accurate Annotation

Conversation disentanglement is the task of taking a log of intertwined conversations from a shared channel and breaking the log into individual conversations. The standard datasets for disentanglement are in a single domain and were annotated by linguistics experts with careful training for the task. In this paper, we introduce the first multi-domain dataset and a study of annotation by people without linguistics expertise or extensive training. We experiment with several variations in interfaces, conducting user studies with domain experts and crowd workers. We also test a hypothesis from prior work that link-based annotation is more accurate, finding that it actually has comparable accuracy to set-based annotation. Our new dataset will support the development of more useful systems for this task, and our experimental findings suggest that users are capable of improving the usefulness of these systems by accurately annotating their own data.
Primate thalamic nuclei select abstract rules and shape prefrontal dynamics

Flexible behavior depends on abstract rules to generalize beyond specific instances, and outcome monitoring to adjust actions. Cortical circuits are posited to read out rules from high-dimensional representations of task-relevant variables in prefrontal cortex (PFC). We instead hypothesized that converging inputs from PFC, directly or via basal ganglia (BG), enable primate-specific thalamus to select rules. To test this, we simultaneously measured spiking activity across PFC and two connected thalamic nuclei of monkeys applying rules. Abstract rule information first appeared in the ventroanterior thalamus (VA) – the main thalamic hub between BG and PFC. The mediodorsal thalamus (MD) also represented rule information before PFC, which persisted after rule cues were removed, to help maintain activation of relevant posterior PFC cell ensembles. MD, a major recipient of midbrain dopamine input, was first to represent information about behavioral outcomes. This persisted after the trial (also in PFC). A PFC-BG-thalamus model reproduced key findings, and thalamic-lesion modeling disrupted PFC rule representations. These results suggest a revised view of the neural basis of flexible behavior in primates, featuring a central role for thalamus in selecting high-level cognitive information from PFC and implementing post-error behavioral adjustments, and of the functional organization of PFC along its anterior-posterior dimension.
MIT Open Access Articles The Highly Conserved Bacterial RNase YbeY Is Essential in Vibrio cholerae, Playing a Critical Role in Virulence, Stress Regulation, and RNA Processing

YbeY, a highly conserved protein, is an RNase in E. coli and plays key roles in both processing of the critical 3 9 end of 16 S rRNA and in 70 S ribosome quality control under stress. These central roles account for YbeY’s inclusion in the postulated minimal bacterial genome. However, YbeY is not essential in E. coli although loss of ybeY severely sensitizes it to multiple physiological stresses. Here, we show that YbeY is an essential endoribonuclease in Vibrio cholerae and is crucial for virulence, stress regulation, RNA processing and ribosome quality control, and is part of a core set of RNases essential in most representative pathogens. To understand its function, we analyzed the rRNA and ribosome profiles of a V. cholerae strain partially depleted for YbeY and other RNase mutants associated with 16 S rRNA processing; our results demonstrate that YbeY is also crucial for 16 S rRNA 3 9 end maturation in V. cholerae and that its depletion impedes subunit assembly into 70 S ribosomes. YbeY’s importance to V. cholerae pathogenesis was demonstrated by the complete loss of mice colonization and biofilm formation, reduced cholera toxin production, and altered expression levels of virulence-associated small RNAs of a V. cholerae strain partially depleted for YbeY. Notably, the ybeY genes of several distantly related pathogens can fully complement an E. coli D ybeY strain under various stress conditions, demonstrating the high conservation of YbeY’s activity in stress regulation. Taken together, this work provides the first comprehensive
Combining Transformer and CNN for Super-Resolution of Animal Fiber Microscopy Images

The images of cashmere and wool fibers used for scientific research in the textile field are mostly acquired manually under an optical microscope. However, due to the interference of microscope quality, shooting environment, focal length selection, acquisition techniques and other factors, the quality of the obtained photographs tends to have a low resolution, and it is difficult to display the fine fiber texture structure and scale details. To address the above problems, a lightweight super-resolution reconstruction algorithm with multi-scale hierarchical screening is proposed. Specifically, firstly, a hybrid module incorporating SwinTransformer and enhanced channel attention is proposed to extract the global features and obtain the important localization among them, in addition, a multi-scale hierarchical screening filtering module is proposed based on the residual model, which amplifies the feature information focusing on high-frequency regions by splitting the channels to allow the model to adaptively weight the features according to the feature weights and amplifies the feature information focusing on high-frequency regions. Finally, the global average pooling attention module integrates and weights the high-frequency features again to enhance details such as edges and textures. A large number of experiments show that compared with other state-of-the-art algorithms, the proposed method significantly improves the image quality on the fiber dataset, and at the same time proves the effectiveness of the proposed method at all scales in five public datasets, occupies less memory parameters than SwinIR, and not only improves the PSNR and SSIM, but also reduces the parameters compared with the light-weight ESRT.
Tight Indistinguishability Bounds for the XOR of Independent Random Permutations by Fourier Analysis

The XOR of two independent permutations (XoP) is a well-known construction for achieving security beyond the birthday bound when implementing a pseudorandom function using a block cipher (i.e., a pseudorandom permutation). The idealized construction (where the permutations are uniformly chosen and independent) and its variants have been extensively analyzed over nearly 25 years. The best-known information-theoretic indistinguishability bound for the XoP construction (due to Dutta, Nandi and Saha [IEEE Trans. Inf. Theory]) is about q 2 / 2 2 n , where q is the number of queries and n is the block length. The XoP construction has also been recently analyzed in the multi-user setting and the best known bound for it (by Chen, Choi, and Lee [CRYPTO’23]) is about √ uq 2max / 2 2 n , where u is the number of users and q max is the number of queries per user. A generalization of the XoP construction outputs the XOR of r ≥ 2 independent permutations, and has also received significant attention. In this paper, we improve all previous bounds obtained in the literature for the (generalized) XoP construction when q > 2 n/ 2 (assuming q < 2 n / 2). Specifically, for the basic XoP construction with r = 2, we obtain an indistinguishability bound of q/ 2 1 . 5 n in the single-user setting and √ uq max / 2 1 . 5 n in the multi-user setting. Hence, if q max = Θ (2 n ) (and q max < 2 n / 2), then our bound of √ uq max / 2 1 . 5 n implies that the adversary’s advantage remains negligible as long as u = o (2 n ). On the other hand, with the previous bound of √ uq 2max / 2 2 n , the adversary’s advantage may already be a
Ch-Oracle A Unified Statistical Framework for Churn Prediction

The User churn stands as a consequential challenge within the realm of online services, posing a substantial threat to the vitality and financial viability of such services. Traditionally, endeavors in churn prediction have transformed the issue into a binary classification task, wherein users are categorized as either churned or non-churned. More recently, a shift towards a more pragmatic approach has been witnessed in the domain of online services, wherein the focus has transitioned from predicting a binary churn label to anticipating the users' return times. This method, aligning more closely with the dynamics of real-world online services, involves the model predicting the specific time of user return at each temporal step, eschewing the simplistic churn label. Nevertheless, antecedent works within this paradigm have grappled with issues of limited generality and imposing computational complexities. This paper introduces ChOracle, an innovative oracle that prognosticates user churn by modeling user return times through the amalgamation of Temporal Point Processes and Recurrent Neural Networks. Furthermore, our approach incorporates latent variables into the proposed recurrent neural network, effectively capturing the latent user loyalty to the system. An efficient approximate variational algorithm, leveraging backpropagation through time, is developed for the purpose of learning parameters within the proposed RNN.
Introducing a Vibrotactile-Based Interface for Conveying Robot-Intended Handover Position to Human

: We aim to enhance interaction speed and reduce the need for visual attention to the robot in handover tasks between humans and robots. We introduce a vibrotactile interface that conveys the robot-intended handover position directly onto the human’s forearm. The results of the experiment conducted on the participants indicate that with our proposed interface, they were able to receive objects faster while maintaining the same level of precision, all while reducing their observation of robot movements. Subjective feedback also suggests a decrease in mental workload when using our proposed interface.
Varied patterns of catch-up in child growth : Evidence from Young Lives Journal Item

The development of human capabilities for many disadvantaged children around the world depends on growth recovery (‘catch-up growth’). Here we develop a novel framework that allows different types of catch-up growth to be classified and estimated. We distinguish between catch-up in the mean of a group toward that of a healthy reference population versus catch-up within the group. We show these different growth types can be tested in a unified setting using a latent growth framework. We apply the results to four developing countries, using longitudinal data on 7,641 children collected over the period 2002-2013. The results show catch-up growth rates are generally modest but vary significantly between countries, and that local environmental factors are material to variation in child growth trajectories. The paper discusses the benefits of the new framework versus current methods, shows that the method is feasible, and suggests they call for intervention designs that are sensitive to community and country contexts. JEL classification: I12, J13
Uncertainty in the projected Antarctic contribution to sea level due to internal climate variability

Identifying and quantifying irreducible and reducible uncertainties in the Antarctic Ice Sheet response to future climate change is essential for guiding mitigation and adaptation policy decision. However, the impact of the irreducible internal climate variability, resulting from processes intrinsic to the climate system, remains poorly understood and quantified. Here, we characterise both the atmospheric and oceanic internal climate variability in a selection of three CMIP6 models (UKESM1-0-LL, IPSL-CM6A-LR and MPI-ESM1.2-HR) and estimate their impact on the Antarctic contribution to sea level 5 over the 21 st century under the SSP2-4.5 scenario. To achieve this, we use a standalone ice-sheet model driven by the ocean through parameterised basal melting and by the atmosphere through emulated surface mass balance estimates. Internal climate variability affects the Antarctic contribution to changes in sea level until 2100 by 45% to 93% depending on the CMIP6 model. This may be a low estimate as the internal climate variability in the CMIP models is likely underestimated. For all the three climate models and for most Antarctic regions, the effect of atmospheric internal climate variability on the surface mass 10 balance overwhelms the effect of oceanic internal climate variability on the dynamical ice-sheet mass loss by more than a factor of 3. The atmospheric internal climate variability is similar in the three CMIP6 models analysed in this study. Conversely, the amplitude of oceanic internal climate variability around Antarctica strongly depends on the climate model as underestimated convection, due to either biases in the sea-ice behaviour or in the ocean stratification, leads to weak mid-depth ocean variability. We then issue recommendations for future ice-sheet projections: use several members in the run and in its initialisation, favor 15 50-year averages to correct or weight simulations over the present-day period, and couple ice-sheet and climate models
O N THE EXPRESSIVE POWER OF DEEP NEURAL NETWORKS

We study the expressive power of deep neural networks before and after training. Considering neural nets after random initialization, we show that three natural measures of expressivity all display an exponential dependence on the depth of the network. We prove, theoretically and experimentally, that all of these measures are in fact related to a fourth quantity, trajectory length. This quantity grows exponentially in the depth of the network, and is responsible for the depth sensitivity observed. These results translate to consequences for networks during and after training. They suggest that parameters earlier in a network have greater influence on its expressive power – in particular, given a layer, its influence on expressivity is determined by the remaining depth of the network after that layer. This is verified with experiments on MNIST and CIFAR-10. We also explore the effect of training on the input-output map, and find that it trades off between the stability and expressivity of the input-output map.