Miracle-dz/UniFAR-FTUs huggingface dataset:
Physiological Changes of Wheat Varieties Under Water Deficit Condition

The experiment was carried out at the research field of the Department of Agronomy of Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur during November 2014 to March 2015 to assess and evaluate the physiological derivations of wheat varieties under water deficit condition. The experiment was laid out in a split plot design comprising two water regimes (irrigated or control and water stress) in main plot and three wheat varieties (BARI Gom 25, BARI Gom 26 and Sourav) in sub-plot with four replications. Surface irrigation was applied into the irrigated plots in total growing season but it was applied in water stress plots up to 21 days after sowing after that irrigation was stopped in water stress plots. It was revealed that studied parameters were significantly influenced by water regimes, variety and their interaction. The xylem exudation rate, light interception, SPAD value, leaf water potential, relative water content, water retention capacity was higher in irrigated condition where canopy temperature, water uptake capacity, water saturation deficit higher in water stress condition.The wheat var. BARI Gom 26 showed the highest PAR, SPAD value, leaf water potential, relative water content, water retention capacity where BARI Gom 25 exhibit lowest under water deficit condition. On the other hand, BARI Gom 25 showd the highest canopy temperature, water uptake capacity and water saturation deficit in water deficit condition. Therefore, considering the physiological performance and other characters BARI Gom 26 could be considered preferably for water shortage condition followed by Sourav where BARI Gom 25 was susceptible one. Bangladesh Agron. J. 2016 19(2): 105-114
Magmatic Processes at Variable Magma Supply along the Galápagos Spreading Center: Constraints from Single Eruptive Units

Petrological study of eruptive units in two locations along the Gal apagos Spreading Center provides insight into how the rate of magma supply affects mid-ocean ridge magmatic systems. Study areas with lower magma supply (95 W) and higher magma supply (92 W) have similar spreading rates (53 and 55 mm a), but differ by 30% in the time-averaged rate of magma supply (0 3 10 and 0 4 10 m km a) as a result of varying proximity to the Gal apagos hotspot. We use major and trace element compositions of glass and whole-rock samples, chemistry of mineral phases, and petrography to characterize parental magma variability, fractional crystallization and magma mixing in crustal reservoirs, and timescales of magmatic recharge relative to eruption. At the low magma supply study area, eruptible magma appears to be present only intermittently within the crust; magma recharge is probably infrequent, occurring with a periodicity of several hundred to one thousand years. The shallowest magma body in the crust is thought to be at 3 km below the seafloor, and lavas are restricted to a relatively limited compositional range (6 2–9 1 wt % MgO). Magmatic evolution at this location is probably dominated by processes occurring within a crystalrich mush, with limited subsequent residence in melt-dominated magma reservoirs. Eruptions here appear to be closely coupled to magmatic recharge events; lower MgO lavas have compositional trends controlled by mixing of lowand high-MgO magmas from compositionally distinct parents, and commonly contain both normally and reversely zoned crystals. In contrast, at the high magma supply study area, where a seismically imaged melt lens is located 1 7 km below the seafloor, fractional crystallization within a melt-rich magma reservoir results in a larger range in major element compositions of the erupted magmas (2 7–8 2 wt % MgO) with less variation in trace element concentrations or ratios. Temperatures within the melt lens over the last several hundred years have varied by at least 100 C (1070–1170 C); cooling rates within the melt lens are estimated to be greater than 0 5 C per year. Relatively low-MgO lavas have over-enrichments in Cl that are best explained by assimilation of brine associated with hydrothermal circulation within the overlying crust. Between magmatic recharge events, resident magma fractionates and feeds one or more low-volume fissure eruptions. Small bodies of magma may become isolated from the larger magmatic system in the crust, allowing more extreme degrees of fractionation, locally reaching basaltic andesite. This study demonstrates that persistent melt lenses at intermediate rates of magma supply need not be ‘steady state’. The variations in magma composition among eruptive episodes at each location allow us to assess the temporal variability in magma reservoir properties at ridge segments along the Gal apagos Spreading Center, in the context of regional variations in magma supply.
Location Sensitive Embedding for Knowledge Graph Reasoning

Embedding methods transform the knowledge graph into a continuous, low-dimensional space, facilitating inference and completion tasks. Existing methods are mainly divided into two types: translational distance models and semantic matching models. A key challenge in translational distance models is their inability to effectively differentiate between 'head' and 'tail' entities in graphs. To address this problem, a novel location-sensitive embedding (LSE) method has been developed. LSE innovatively modifies the head entity using relation-specific mappings, conceptualizing relations as linear transformations rather than mere translations. The theoretical foundations of LSE, including its representational capabilities and its connections to existing models, have been thoroughly examined. A more streamlined variant, LSE-d, which employs a diagonal matrix for transformations to enhance practical efficiency, is also proposed. Experiments conducted on four large-scale KG datasets for link prediction show that LSEd either outperforms or is competitive with state-of-the-art related works.
The Interplay of Spectral Efficiency, User Density, and Energy in Grant-based Access Protocols

We employ grant-based access with retransmissions for multiple users with small payloads, particularly at low spectral efficiency (SE). The radio resources are allocated via NOMA in the time into $T$ slots and frequency dimensions, with a measure of non-orthogonality $\eta$. Retransmissions are stored in a receiver buffer with a finite size $C_{\sf buf}$ and combined via HARQ, using Chase Combining (CC) and Incremental Redundancy (IR). We determine the best scaling for the SE (bits/rdof) and for the user density $J/n$, for a given number of users $J$ and a blocklength $n$, versus SNR ($\rho$) per bit, i.e., the ratio $E_b/N_0$, for the sum-rate optimal regime and when the interference is treated as noise (TIN), using a finite blocklength analysis. Contrasting the classical scheme (no retransmissions) with CC-NOMA, CC-OMA, and IR-OMA strategies in TIN and sum-rate optimal cases, the numerical results on the SE demonstrate that CC-NOMA outperforms, almost in all regimes, the other approaches. In the sum-rate optimal regime, the scalings of $J/n$ versus $E_b/N_0$ deteriorate with $T$, yet from the most degraded to the least, the ordering of the schemes is as (i) classical, (ii) CC-OMA, (iii) IR-OMA, and (iv) CC-NOMA, demonstrating the robustness of CC-NOMA. Contrasting TIN models at low $\rho$, the scalings of $J/n$ for CC-based models improve the best, whereas, at high $\rho$, the scaling of CC-NOMA is poor due to higher interference, and CC-OMA becomes prominent due to combining retransmissions and its reduced interference. The scaling results are applicable over a range of $\eta$, $T$, $C_{\sf buf}$, and $J$, at low received SNR. The proposed analytical framework provides insights into resource allocation in grant-based access and specific 5G use cases for massive URLLC uplink access.
Distributed Markov Chain Monte Carlo Sampling based on the Alternating Direction Method of Multipliers

Many machine learning applications require operating on a spatially distributed dataset. Despite technological advances, privacy considerations and communication constraints may prevent gathering the entire dataset in a central unit. In this paper, we propose a distributed sampling scheme based on the alternating direction method of multipliers, which is commonly used in the optimization literature due to its fast convergence. In contrast to distributed optimization, distributed sampling allows for uncertainty quantification in Bayesian inference tasks. We provide both theoretical guarantees of our algorithm's convergence and experimental evidence of its superiority to the state-of-the-art. For our theoretical results, we use convex optimization tools to establish a fundamental inequality on the generated local sample iterates. This inequality enables us to show convergence of the distribution associated with these iterates to the underlying target distribution in Wasserstein distance. In simulation, we deploy our algorithm on linear and logistic regression tasks and illustrate its fast convergence compared to existing gradient-based methods.
Synchformer: Efficient Synchronization from Sparse Cues

Our objective is audio-visual synchronization with a focus on 'in-the-wild' videos, such as those on YouTube, where synchronization cues can be sparse. Our contributions include a novel audio-visual synchronization model, and training that decouples feature extraction from synchronization modelling through multi-modal segment-level contrastive pre-training. This approach achieves state-of-the-art performance in both dense and sparse settings. We also extend synchronization model training to AudioSet a million-scale 'in-the-wild' dataset, investigate evidence attribution techniques for interpretability, and explore a new capability for synchronization models: audio-visual synchronizability.
Neural Network Training on Encrypted Data with TFHE

We present an approach to outsourcing of training neural networks while preserving data confidentiality from malicious parties. We use fully homomorphic encryption to build a unified training approach that works on encrypted data and learns quantized neural network models. The data can be horizontally or vertically split between multiple parties, enabling collaboration on confidential data. We train logistic regression and multi-layer perceptrons on several datasets.
Gravity and $T\bar{T}$ flows in higher dimensions

We study systems in arbitrary space-time dimensions where matter, deformed by irrelevant operators, is coupled to gravity in the Palatini formalism. We explore a different but dynamically equivalent perspective, wherein the deformation transitions from the matter action to the gravitational one or vice versa. This alternative viewpoint leads to the emergence of Ricci-based gravity theories, thus providing a higher-dimensional generalisation of the well-known equivalence between two-dimensional $\mathrm{T}\overline{\mathrm{T}}$ deformations and coupling to Jackiw-Teitelboim gravity. We conjecture that this dynamical equivalence has a wide range of validity, and we conduct a detailed analysis to support this claim, focusing on specific examples in $d=4$ dimensions. This analysis builds upon earlier results related to the metric interpretation of $\mathrm{T}\overline{\mathrm{T}}$-like perturbations and incorporates recent findings in the literature that employ the concept of reframing. We study instances where the emerging gravity models are of Born-Infeld and Starobinsky types.
TQCompressor: improving tensor decomposition methods in neural networks via permutations

We introduce TQCompressor, a novel method for neural network model compression with improved tensor decompositions. We explore the challenges posed by the computational and storage demands of pre-trained language models in NLP tasks and propose a permutation-based enhancement to Kronecker decomposition. This enhancement makes it possible to reduce loss in model expressivity which is usually associated with factorization. We demonstrate this method applied to the GPT-2$_{small}$. The result of the compression is TQCompressedGPT-2 model, featuring 81 mln. parameters compared to 124 mln. in the GPT-2$_{small}$. We make TQCompressedGPT-2 publicly available. We further enhance the performance of the TQCompressedGPT-2 through a training strategy involving multi-step knowledge distillation, using only a 3.1% of the OpenWebText. TQCompressedGPT-2 surpasses DistilGPT-2 and KnGPT-2 in comparative evaluations, marking an advancement in the efficient and effective deployment of models in resource-constrained environments.
GeoDecoder: Empowering Multimodal Map Understanding

This paper presents GeoDecoder, a dedicated multimodal model designed for processing geospatial information in maps. Built on the BeitGPT architecture, GeoDecoder incorporates specialized expert modules for image and text processing. On the image side, GeoDecoder utilizes GaoDe Amap as the underlying base map, which inherently encompasses essential details about road and building shapes, relative positions, and other attributes. Through the utilization of rendering techniques, the model seamlessly integrates external data and features such as symbol markers, drive trajectories, heatmaps, and user-defined markers, eliminating the need for extra feature engineering. The text module of GeoDecoder accepts various context texts and question prompts, generating text outputs in the style of GPT. Furthermore, the GPT-based model allows for the training and execution of multiple tasks within the same model in an end-to-end manner. To enhance map cognition and enable GeoDecoder to acquire knowledge about the distribution of geographic entities in Beijing, we devised eight fundamental geospatial tasks and conducted pretraining of the model using large-scale text-image samples. Subsequently, rapid fine-tuning was performed on three downstream tasks, resulting in significant performance improvements. The GeoDecoder model demonstrates a comprehensive understanding of map elements and their associated operations, enabling efficient and high-quality application of diverse geospatial tasks in different business scenarios.
ICAR-Central Arid Zone Research Institute, Jodhpur: Erosion Processes and Desertification in the Thar Desert of India

In early and mid-twentieth century, there were speculations and apprehension that the Thar Desert is spreading towards east and it may cover large fertile areas of present day Punjab, Haryana, west Uttar Pradesh and east Rajasthan. Therefore, the Government of India established a Desert Afforestation Research Station at Jodhpur, Rajasthan in 1952 mainly to focus on sand dune stabilization and shelterbelt plantations to reduce wind erosion. In 1957, it was reorganized as Desert Afforestation and Soil Conservation Station. Just after two years (in 1959), it was restructured as the Central Arid Zone Research Institute (CAZRI).
Research on the application of loop quantum theory model in black hole quantum information

An important reason why it is currently difficult to unify relativity theory and quantum theory is the quantum information paradox. The information engulfment pointed out by general relativity violates the principles of quantum mechanics. An important reason why the industry does not have a clear understanding of this phenomenon is the current lack of a theoretically solvable cosmological model. Based on the complete model of loop quantum theory, this article solves different levels of Hamiltonian constraint models and simulates black hole information transfer dynamics, especially at extreme points, from analytical results to step-by-step quantum corrections, and attempts to compare the performance of different physical models in simulating quantum Advantages during information transmission. Our study shows that even second-order expansions are sufficient to distinguish differences in dynamics at the black hole extremes, but to truly identify a model that has the potential to describe quantum information transfer mechanisms and is significantly different from other models, the theoretical analytical solution should at least extend to Level three and above. In addition, the research results such as computational simulation methods and related conclusions cited and improved in this article can provide certain theoretical support and new insights for the research prospects of general relativity loop quantum cosmology and the intersection of quantum information and quantum fields.
Moving beyond Deletions: Program Simplification via Diverse Program Transformations

To reduce the complexity of software, Developers manually simplify program (known as developer-induced program simplification in this paper) to reduce its code size yet preserving its functionality but manual simplification is time-consuming and error-prone. To reduce manual effort, rule-based approaches (e.g., refactoring) and deletion-based approaches (e.g., delta debugging) can be potentially applied to automate developer-induced program simplification. However, as there is little study on how developers simplify programs in Open-source Software (OSS) projects, it is unclear whether these approaches can be effectively used for developer-induced program simplification. Hence, we present the first study of developer-induced program simplification in OSS projects, focusing on the types of program transformations used, the motivations behind simplifications, and the set of program transformations covered by existing refactoring types. Our study of 382 pull requests from 296 projects reveals that there exist gaps in applying existing approaches for automating developer-induced program simplification. and outlines the criteria for designing automatic program simplification techniques. Inspired by our study and to reduce the manual effort in developer-induced program simplification, we propose SimpT5, a tool that can automatically produce simplified programs (semantically-equivalent programs with reduced source lines of code). SimpT5 is trained based on our collected dataset of 92,485 simplified programs with two heuristics: (1) simplified line localization that encodes lines changed in simplified programs, and (2)checkers that measure the quality of generated programs. Our evaluation shows that SimpT5 are more effective than prior approaches in automating developer-induced program simplification.
Two Stones Hit One Bird: Bilevel Positional Encoding for Better Length Extrapolation

In this work, we leverage the intrinsic segmentation of language sequences and design a new positional encoding method called Bilevel Positional Encoding (BiPE). For each position, our BiPE blends an intra-segment encoding and an inter-segment encoding. The intra-segment encoding identifies the locations within a segment and helps the model capture the semantic information therein via absolute positional encoding. The inter-segment encoding specifies the segment index, models the relationships between segments, and aims to improve extrapolation capabilities via relative positional encoding. Theoretical analysis shows this disentanglement of positional information makes learning more effective. The empirical results also show that our BiPE has superior length extrapolation capabilities across a wide range of tasks in diverse text modalities.
Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detection

Self-supervised methods have gained prominence in time series anomaly detection due to the scarcity of available annotations. Nevertheless, they typically demand extensive training data to acquire a generalizable representation map, which conflicts with scenarios of a few available samples, thereby limiting their performance. To overcome the limitation, we propose \textbf{AnomalyLLM}, a knowledge distillation-based time series anomaly detection approach where the student network is trained to mimic the features of the large language model (LLM)-based teacher network that is pretrained on large-scale datasets. During the testing phase, anomalies are detected when the discrepancy between the features of the teacher and student networks is large. To circumvent the student network from learning the teacher network's feature of anomalous samples, we devise two key strategies. 1) Prototypical signals are incorporated into the student network to consolidate the normal feature extraction. 2) We use synthetic anomalies to enlarge the representation gap between the two networks. AnomalyLLM demonstrates state-of-the-art performance on 15 datasets, improving accuracy by at least 14.5\% in the UCR dataset.
Research on the Variation of Vegetation NDVI and Its Relationship with Climatic Factors in Northern China

Based on the remote sensing data, NOAA/AVHRR NDVI and meteorological data of 56 meteorological stations,temperature and precipitation, during 1982-2006 in the Northern China, the inter-annual variability of vegetation NDVI, temperature and precipitation and the response of NDVI to the temperature and precipitation were analyzed from monthly, seasonal and growing season time scales. From the monthly, the inter-annual change of vegetation NDVI and temperature were mainly increased, while the precipitation was mainly reduced. In view of the season, the spatial distribution of NDVI in spring was consistent with the distribution in autumn, but contrast with the distribution in summer. The time series NDVI in spring and autumn were increased, and the decreased trend in summer was weak. The rate of temperature increasing in spring was the fastest, and the summer followed. The precipitation in spring and autumn were induced, while the precipitation in summer was increased. In the growing season, the vegetation NDVI showed an increasing trend, the temperature increased 0.48 ℃ every 10 years(P0.01), while the trend of precipitation was not obvious. The vegetation NDVI in March, April and November was mainly affected by the temperature. The correlation coefficients between NDVI and temperature of the season and growing season were greater than the correlation coefficients between NDVI and precipitation. The order of the correlation coefficients between NDVI and temperature was: growing season spring autumn summer, while the order of the correlation coefficients between NDVI and precipitation was: springgrowing seasonsummer autumn.
Strategic Usage in a Multi-Learner Setting

Real-world systems often involve some pool of users choosing between a set of services. With the increase in popularity of online learning algorithms, these services can now self-optimize, leveraging data collected on users to maximize some reward such as service quality. On the flipside, users may strategically choose which services to use in order to pursue their own reward functions, in the process wielding power over which services can see and use their data. Extensive prior research has been conducted on the effects of strategic users in single-service settings, with strategic behavior manifesting in the manipulation of observable features to achieve a desired classification; however, this can often be costly or unattainable for users and fails to capture the full behavior of multi-service dynamic systems. As such, we analyze a setting in which strategic users choose among several available services in order to pursue positive classifications, while services seek to minimize loss functions on their observations. We focus our analysis on realizable settings, and show that naive retraining can still lead to oscillation even if all users are observed at different times; however, if this retraining uses memory of past observations, convergent behavior can be guaranteed for certain loss function classes. We provide results obtained from synthetic and real-world data to empirically validate our theoretical findings.