Miracle-dz/UniFAR-FTUs huggingface dataset:
Element Abundances and the Physics of Solar Energetic Particles

Acceleration and transport of solar energetic particles (SEPs) causes their abundances, measured at constant velocity, to be enhanced or suppressed as a function of each ion's magnetic rigidity, and hence its atomic mass-to-charge ratio A/Q. Ion charges, in turn, depend upon source electron temperature. In small"impulsive"SEP events, arising from solar jets, acceleration during magnetic reconnection causes steep power-law abundance enhancements. These impulsive SEP events can have 1000-fold enhancements of heavy elements from sources at ~2.5 MK, and similar enhancements of 3He/4He and of streaming electrons that drive type-III radio bursts. Gamma-ray lines show that solar flares also accelerate 3He-rich ions, but their electrons and ions remain trapped on magnetic loops so they dissipate their energy as X-rays, gamma-rays, heat, and light. "Gradual"SEPs accelerated at shock waves, driven by fast coronal mass ejections (CMEs), can show power-law abundance enhancements or depressions, even with see ions from the ambient solar corona. In addition, shocks can reaccelerate seed particles from residual impulsive SEPs with their pre-existing signature heavy-ion enhancements. Different patterns of abundance often show that heavy elements are dominated by a different source from that of H and He. Nevertheless, the SEP abundances averaged over many large events define the abundances of the corona itself, which is found to differ from the solar photosphere as a function of the first ionization potential (FIP) since ions, with FIP<10 eV, are driven upward by forces of electromagnetic waves which neutral atoms, with FIP>10 eV, cannot feel. Thus, SEPs provide a measurement of element abundances in the solar corona, distinct from the solar wind, and may even better define the photosphere for some elements.
Linear-size Suffix Tries and Linear-size CDAWGs Simplified and Improved

The linear-size suffix tries (LSTries) [Crochemore et al., TCS 2016] are a version of suffix trees in which the edge labels are single characters, yet are able to perform pattern matching queries in optimal time. Instead of explicitly storing the input text, LSTries have some extra non-branching internal nodes called type-2 nodes. The extended techniques are then used in the linear-size compact directed acyclic word graphs (LCDAWGs) [Takagi et al. SPIRE 2017], which can be stored with $O(el(T)+er(T))$ space (i.e. without the text), where $el(T)$ and $er(T)$ are the numbers of left- and right-extensions of the maximal repeats in the input text string $T$, respectively. In this paper, we present simpler alternatives to the aforementioned indexing structures, called the simplified LSTries (simLSTries) and the simplified LCDAWGs (simLCDAWGs), in which most of the type-2 nodes are removed. In particular, our simLCDAWGs require only $O(er(T))$ space and work on a weaker model of computation (i.e. the pointer machine). This contrasts the $O(er(T))$-space CDAWG representation of [Belazzougui \&Cunial, SPIRE 2017], which works on the word RAM.
Semantic Draw Engineering for Text-to-Image Creation

Text-to-image generation is conducted through Generative Adversarial Networks (GANs) or transformer models. However, the current challenge lies in accurately generating images based on textual descriptions, especially in scenarios where the content and theme of the target image are ambiguous. In this paper, we propose a method that utilizes artificial intelligence models for thematic creativity, followed by a classification modeling of the actual painting process. The method involves converting all visual elements into quantifiable data structures before creating images. We evaluate the effectiveness of this approach in terms of semantic accuracy, image reproducibility, and computational efficiency, in comparison with existing image generation algorithms.
On the stabilization of a virtual element method for an acoustic vibration problem

In this paper we introduce an abstract setting for the convergence analysis of the virtual element approximation of an acoustic vibration problem. We discuss the effect of the stabilization parameters and remark that in some cases it is possible to achieve optimal convergence without the need of any stabilization. This statement is rigorously proved for lowest order triangular element and supported by several numerical experiments.
Information bath and learning processes along geodesics

Learning is a fundamental characteristic of living systems, enabling them to comprehend their environments and make informed decisions. These decision-making processes are inherently influenced by available information about their surroundings and specific objectives. An intriguing perspective is that each process could be optimal under a given set of conditions. Here, a geometric method is introduced for analytically exploring such a perspective. The probability distribution describing the state of the composite system of the environment, termed the information bath, and a decision-maker is discussed on the basis of the entropic concept. This enables one to study the system in analogy with thermodynamics. Learning processes are expressed as the changes of parameters contained in the distribution. For a geometric interpretation of the processes, the manifold endowed with the Fisher-Rao metric as the Riemannian metric is considered. This framework allows one to conceptualize the optimality of each process as a state change along a geodesic curve on the manifold, which is referred to here as geodesic learning. Then, the bivariate Gaussian model is presented for illustrating this approach, and its rigorous solution is obtained. There, the correspondence between learning and cooling is made manifest. Evolution of the entropy is evaluated for geodesic learning. The findings are then interpreted in the context of adaptation. Geodesic learning plays a role of the case of reference and may give novel insights into the mechanics of learning in living systems.
Prompt-based Multi-interest Learning Method for Sequential Recommendation

Multi-interest learning method for sequential recommendation aims to predict the next item according to user multi-faceted interests given the user historical interactions. Existing methods mainly consist of two modules: the multi-interest extraction module that learns user multi-interest embeddings to capture the user multi-interests, and the multi-interest weight prediction module that learns the weight of each interest for aggregating the learned multi-interest embeddings to derive the user embedding, used for predicting the user rating to an item. Despite their effectiveness, existing methods have two key limitations: 1) they directly feed the user interactions into the two modules, while ignoring their different learning objectives, and 2) they merely consider the centrality of the user interactions to learn the user multi-interests, while overlooking their dispersion. To tackle these limitations, we propose a prompt-based multi-interest learning method (PoMRec), where specific prompts are inserted into user interactions to make them adaptive to different learning objectives of the two modules. Moreover, we utilize both the mean and variance embeddings of user interactions to derive the user multi-interest embeddings for comprehensively model the user multi-interests. We conduct extensive experiments on two public datasets, and the results verify that our proposed PoMRec outperforms the state-of-the-art multi-interest learning methods.
Chain of LoRA: Efficient Fine-tuning of Language Models via Residual Learning

Fine-tuning is the primary methodology for tailoring pre-trained large language models to specific tasks. As the model's scale and the diversity of tasks expand, parameter-efficient fine-tuning methods are of paramount importance. One of the most widely used family of methods is low-rank adaptation (LoRA) and its variants. LoRA encodes weight update as the product of two low-rank matrices. Despite its advantages, LoRA falls short of full-parameter fine-tuning in terms of generalization error for certain tasks. We introduce Chain of LoRA (COLA), an iterative optimization framework inspired by the Frank-Wolfe algorithm, to bridge the gap between LoRA and full parameter fine-tuning, without incurring additional computational costs or memory overheads. COLA employs a residual learning procedure where it merges learned LoRA modules into the pre-trained language model parameters and re-initilize optimization for new born LoRA modules. We provide theoretical convergence guarantees as well as empirical results to validate the effectiveness of our algorithm. Across various models (OPT and llama-2) and seven benchmarking tasks, we demonstrate that COLA can consistently outperform LoRA without additional computational or memory costs.
LANGUAGES OF INDIA AND INDIA AS A LINGUISTIC AREA

1. India represents six distinct language families spread over a large region and spoken by more than one billion speakers. Though the exact figure of number of languages is not very clear partly because of the fuzzy demarcation between 'language' and 'dialect' question and partly because of shifting language loyalty of the people. However, a rough estimate is that there are more than 1600 languages spoken in the present India. The government of India reports only 122 and recognizes as 'scheduled/official' 1 languages only 22 drawn largely from the Indo-Aryan and Dravidian language family stocks. Consult the Census 2001. A large number of languages are endangered (196 according to the UNESCO). 2. The inclusion or exclusion of languages from the VIII schedule does neither depend on the quantum of its speakers nor it is based on the ideology of fundamental rights. Unfortunately, it is not based on the principle of equality of opportunity or on the ideology of national integration or invasive assimilation. Had it been so a large number of languages from Austro-Asiatic and Tibeto- Burman branch would have found their place in the schedule and Sanskrit with its mere 2500 speakers (reported to have doubled by 1991) would have not.
Optimal National policies towards multinationals when local regions can choose between firm-specific and non-firm-specific policies

This paper looks at a country's optimal central-government optimal policy in a setting where its two identical local jurisdictions compete to attract footloose multinationals to their sites, and where the considered multinationals strictly prefer this country to the rest of the world. For the sake of realism the model allows the local jurisdictions to choose between firm-specific and non-firm-specific policies. We show that the implementation of the jurisdictional firm-specific policy is weakly welfare dominant. Hence the frequent calls for the central government to ban the former type of policies go against the advice of this paper.
DeepPhysiNet: Bridging Deep Learning and Atmospheric Physics for Accurate and Continuous Weather Modeling

Accurate weather forecasting holds significant importance to human activities. Currently, there are two paradigms for weather forecasting: Numerical Weather Prediction (NWP) and Deep Learning-based Prediction (DLP). NWP utilizes atmospheric physics for weather modeling but suffers from poor data utilization and high computational costs, while DLP can learn weather patterns from vast amounts of data directly but struggles to incorporate physical laws. Both paradigms possess their respective strengths and weaknesses, and are incompatible, because physical laws adopted in NWP describe the relationship between coordinates and meteorological variables, while DLP directly learns the relationships between meteorological variables without consideration of coordinates. To address these problems, we introduce the DeepPhysiNet framework, incorporating physical laws into deep learning models for accurate and continuous weather system modeling. First, we construct physics networks based on multilayer perceptrons (MLPs) for individual meteorological variable, such as temperature, pressure, and wind speed. Physics networks establish relationships between variables and coordinates by taking coordinates as input and producing variable values as output. The physical laws in the form of Partial Differential Equations (PDEs) can be incorporated as a part of loss function. Next, we construct hyper-networks based on deep learning methods to directly learn weather patterns from a large amount of meteorological data. The output of hyper-networks constitutes a part of the weights for the physics networks. Experimental results demonstrate that, upon successful integration of physical laws, DeepPhysiNet can accomplish multiple tasks simultaneously, not only enhancing forecast accuracy but also obtaining continuous spatiotemporal resolution results, which is unattainable by either the NWP or DLP.
Plug-and-Play Transformer Modules for Test-Time Adaptation

Parameter-efficient tuning (PET) methods such as LoRA, Adapter, and Visual Prompt Tuning (VPT) have found success in enabling adaptation to new domains by tuning small modules within a transformer model. However, the number of domains encountered during test time can be very large, and the data is usually unlabeled. Thus, adaptation to new domains is challenging; it is also impractical to generate customized tuned modules for each such domain. Toward addressing these challenges, this work introduces PLUTO: a Plug-and-pLay modUlar Test-time domain adaptatiOn strategy. We pre-train a large set of modules, each specialized for different source domains, effectively creating a ``module store''. Given a target domain with few-shot unlabeled data, we introduce an unsupervised test-time adaptation (TTA) method to (1) select a sparse subset of relevant modules from this store and (2) create a weighted combination of selected modules without tuning their weights. This plug-and-play nature enables us to harness multiple most-relevant source domains in a single inference call. Comprehensive evaluations demonstrate that PLUTO uniformly outperforms alternative TTA methods and that selecting $\leq$5 modules suffice to extract most of the benefit. At a high level, our method equips pre-trained transformers with the capability to dynamically adapt to new domains, motivating a new paradigm for efficient and scalable domain adaptation.
An Effective Theory for Graphene Nanoribbons with Junctions

Graphene nanoribbons are a promising candidate for fault-tolerant quantum electronics. In this scenario, qubits are realised by localised states that can emerge on junctions in hybrid ribbons formed by two armchair nanoribbons of different widths. We derive an effective theory based on a tight-binding ansatz for the description of hybrid nanoribbons and use it to make accurate predictions of the energy gap and nature of the localisation in various hybrid nanoribbon geometries. We use quantum Monte Carlo simulations to demonstrate that the effective theory remains applicable in the presence of Hubbard interactions. We discover, in addition to the well known localisations on junctions, which we call `Fuji', a new type of `Kilimanjaro' localisation smeared out over a segment of the hybrid ribbon. We show that Fuji localisations in hybrids of width $N$ and $N+2$ armchair nanoribbons occur around symmetric junctions if and only if $N\pmod3=1$, while edge-aligned junctions never support strong localisation. This behaviour cannot be explained relying purely on the topological $Z_2$ invariant, which has been believed the origin of the localisations to date.
High-precision Voice Search Query Correction via Retrievable Speech-text Embedings

Automatic speech recognition (ASR) systems can suffer from poor recall for various reasons, such as noisy audio, lack of sufficient training data, etc. Previous work has shown that recall can be improved by retrieving rewrite candidates from a large database of likely, contextually-relevant alternatives to the hypothesis text using nearest-neighbors search over embeddings of the ASR hypothesis text to correct and candidate corrections. However, ASR-hypothesis-based retrieval can yield poor precision if the textual hypotheses are too phonetically dissimilar to the transcript truth. In this paper, we eliminate the hypothesis-audio mismatch problem by querying the correction database directly using embeddings derived from the utterance audio; the embeddings of the utterance audio and candidate corrections are produced by multimodal speech-text embedding networks trained to place the embedding of the audio of an utterance and the embedding of its corresponding textual transcript close together. After locating an appropriate correction candidate using nearest-neighbor search, we score the candidate with its speech-text embedding distance before adding the candidate to the original n-best list. We show a relative word error rate (WER) reduction of 6% on utterances whose transcripts appear in the candidate set, without increasing WER on general utterances.
How Dark is Dark Energy?

The nature of dark energy is one of the fundamental problems in cosmology. Introduced to explain the apparent acceleration of the Universe's expansion, its origin remains to be determined. In this paper, we illustrate a result that may significantly impact understanding the relationship between dark energy and structure formation in the late-epoch Universe. Our analysis exploits a scale-dependent energy functional, initially developed for image visualization, to compare the physical and geometrical data that distinct cosmological observers register on their celestial spheres. In the presence of late-epoch gravitational structures, this functional provides a non-perturbative technique that allows the standard Friedmann-Lema\^itre-Robertson-Walker (FLRW) observer to evaluate a measurable, scale-dependent difference between the idealized FLRW past light cone and the physical light cone. From the point of view of the FLRW observer, this difference manifests itself as a redshift-dependent correction $\Lambda^{(corr)}(z)$ to the FLRW cosmological constant $\Lambda^{(FLRW)}$. At the scale where cosmological expansion couples with the local virialized dynamics of gravitational structures, we get $\Lambda^{(corr)}(z)\sim 10^{-52}\,m^{-2}$, indicating that the late-epoch structures induce an effective cosmological constant that is of the same order of magnitude as the assumed value of the FLRW cosmological constant, a result that may lead to an interpretative shift in the very role of dark energy.
DepressionEmo: A novel dataset for multilabel classification of depression emotions

Emotions are integral to human social interactions, with diverse responses elicited by various situational contexts. Particularly, the prevalence of negative emotional states has been correlated with negative outcomes for mental health, necessitating a comprehensive analysis of their occurrence and impact on individuals. In this paper, we introduce a novel dataset named DepressionEmo designed to detect 8 emotions associated with depression by 6037 examples of long Reddit user posts. This dataset was created through a majority vote over inputs by zero-shot classifications from pre-trained models and validating the quality by annotators and ChatGPT, exhibiting an acceptable level of interrater reliability between annotators. The correlation between emotions, their distribution over time, and linguistic analysis are conducted on DepressionEmo. Besides, we provide several text classification methods classified into two groups: machine learning methods such as SVM, XGBoost, and Light GBM; and deep learning methods such as BERT, GAN-BERT, and BART. The pretrained BART model, bart-base allows us to obtain the highest F1- Macro of 0.76, showing its outperformance compared to other methods evaluated in our analysis. Across all emotions, the highest F1-Macro value is achieved by suicide intent, indicating a certain value of our dataset in identifying emotions in individuals with depression symptoms through text analysis. The curated dataset is publicly available at: https://github.com/abuBakarSiddiqurRahman/DepressionEmo.
Determination of Strain Patterns Across the Root-Stem Transition Zone in Trees

Determination of Strain Patterns Across the Root-Stem Transition Zone in Trees Kenneth Edward Beezley Trees are subjected to mechanical stressors, induced via dynamic (wind) and static (gravitational) forces, and must properly acclimate during their lifespan or face premature mortality. Resource allocation as a stress response is vital for tree survival, and can include the shedding of woody parts, adaptive growth, hormonal signaling cascades, and the initiation of energy transfer mechanisms. The strain resulting from stress intercepted by the canopy and transferred throughout the tree is of significant importance, not only for tree survival, but for the safety and well-being of the human population found in close proximity. As trees age and adapt to stressors and an ever changing environment, material properties also change that can influence tree stability, of which arborist and tree managers should account for. To test the function of tree orientation to an applied load, static load tests were conducted on 15 mature pin oak trees (Quercus palustris Muenchh.). Static load tests were applied to tilt the trees 0.1 o from natural position to concentrate strain at the root-stem transition zone (RSTZ) and nondestructively mapped resulting strain patterns with an ARAMIS digital image correlation (DIC) system. Using bark as a surrogate for strain on the xylem, strain resulting from static loading was mapped with the DIC system from the main stem into the structural root system. Strain patterns were analyzed across the RSTZ in the leeward, tangential, and windward directions. Results indicate that mean maximum strain magnitudes are similar at the RSTZ on the leeward (0.088) and windward sides (0.090) and lower on the tangential direction (0.058). The leeward strain was comprised of 4% tensile strain and 96% compressive strain and windward strain was comprised of 95% tensile strain and 5% compressive strain. Mean maximum strain for the tangential orientation constituted 65% tensile strain and 35% compressive strain. The changes of proportional strains found on the tangential RTSZ orientation is poorly understood and often disregarded in tree biomechanical studies. This information could prove to be beneficial to the arboricultural and plant science communities to better understand how trees manage loading events and to further enhance tree risk assessment and root zone management protocols.