Miracle-dz/UniFAR-FTUs huggingface dataset:
Graphologue: Exploring Large Language Model Responses with Interactive Diagrams

Large language models (LLMs) have recently soared in popularity due to their ease of access and the unprecedented ability to synthesize text responses to diverse user questions. However, LLMs like ChatGPT present significant limitations in supporting complex information tasks due to the insufficient affordances of the text-based medium and linear conversational structure. Through a formative study with ten participants, we found that LLM interfaces often present long-winded responses, making it difficult for people to quickly comprehend and interact flexibly with various pieces of information, particularly during more complex tasks. We present Graphologue, an interactive system that converts text-based responses from LLMs into graphical diagrams to facilitate information-seeking and question-answering tasks. Graphologue employs novel prompting strategies and interface designs to extract entities and relationships from LLM responses and constructs node-link diagrams in real-time. Further, users can interact with the diagrams to flexibly adjust the graphical presentation and to submit context-specific prompts to obtain more information. Utilizing diagrams, Graphologue enables graphical, non-linear dialogues between humans and LLMs, facilitating information exploration, organization, and comprehension.
The JCMT BISTRO Survey: Studying the Complex Magnetic Field of L43

We present observations of polarized dust emission at 850 μm from the L43 molecular cloud, which sits in the Ophiuchus cloud complex. The data were taken using SCUBA-2/POL-2 on the James Clerk Maxwell Telescope as a part of the BISTRO large program. L43 is a dense ( NH2∼1022 –1023 cm−2) complex molecular cloud with a submillimeter-bright starless core and two protostellar sources. There appears to be an evolutionary gradient along the isolated filament that L43 is embedded within, with the most evolved source closest to the Sco OB2 association. One of the protostars drives a CO outflow that has created a cavity to the southeast. We see a magnetic field that appears to be aligned with the cavity walls of the outflow, suggesting interaction with the outflow. We also find a magnetic field strength of up to ∼160 ± 30 μG in the main starless core and up to ∼90 ± 40 μG in the more diffuse, extended region. These field strengths give magnetically super- and subcritical values, respectively, and both are found to be roughly trans-Alfvénic. We also present a new method of data reduction for these denser but fainter objects like starless cores.
PS-FedGAN: An Efficient Federated Learning Framework Based on Partially Shared Generative Adversarial Networks For Data Privacy

Federated Learning (FL) has emerged as an effective learning paradigm for distributed computation owing to its strong potential in capturing underlying data statistics while preserving data privacy. However, in cases of practical data heterogeneity among FL clients, existing FL frameworks still exhibit deficiency in capturing the overall feature properties of local client data that exhibit disparate distributions. In response, generative adversarial networks (GANs) have recently been exploited in FL to address data heterogeneity since GANs can be integrated for data regeneration without exposing original raw data. Despite some successes, existing GAN-related FL frameworks often incur heavy communication cost and also elicit other privacy concerns, which limit their applications in real scenarios. To this end, this work proposes a novel FL framework that requires only partial GAN model sharing. Named as PS-FedGAN, this new framework enhances the GAN releasing and training mechanism to address heterogeneous data distributions across clients and to strengthen privacy preservation at reduced communication cost, especially over wireless networks. Our analysis demonstrates the convergence and privacy benefits of the proposed PS-FEdGAN framework. Through experimental results based on several well-known benchmark datasets, our proposed PS-FedGAN shows great promise to tackle FL under non-IID client data distributions, while securing data privacy and lowering communication overhead.
Stability, bounded generation and strong boundedness

We provide bounds linear in the rank for the generalized conjugacy diameters, introduced by Kedra, Libman and Martin, for the special linear and symplectic groups defined over the rings of integers of global fields by way of using certain stability considerations familiar from classical algebraic K-theory. This determines the growth rates of these generalized conjugacy diameters.
How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain, and Cross-domain Settings

Large language models (LLMs) with in-context learning have demonstrated remarkable capability in the text-to-SQL task. Previous research has prompted LLMs with various demonstration-retrieval strategies and intermediate reasoning steps to enhance the performance of LLMs. However, those works often employ varied strategies when constructing the prompt text for text-to-SQL inputs, such as databases and demonstration examples. This leads to a lack of comparability in both the prompt constructions and their primary contributions. Furthermore, selecting an effective prompt construction has emerged as a persistent problem for future research. To address this limitation, we comprehensively investigate the impact of prompt constructions across various settings and provide insights into prompt constructions for future text-to-SQL studies.
Mode-Aware Continual Learning for Conditional Generative Adversarial Networks

The main challenge in continual learning for generative models is to effectively learn new target modes with limited samples while preserving previously learned ones. To this end, we introduce a new continual learning approach for conditional generative adversarial networks by leveraging a mode-affinity score specifically designed for generative modeling. First, the generator produces samples of existing modes for subsequent replay. The discriminator is then used to compute the mode similarity measure, which identifies a set of closest existing modes to the target. Subsequently, a label for the target mode is generated and given as a weighted average of the labels within this set. We extend the continual learning model by training it on the target data with the newly-generated label, while performing memory replay to mitigate the risk of catastrophic forgetting. Experimental results on benchmark datasets demonstrate the gains of our continual learning approach over the state-of-the-art methods, even when using fewer training samples.
Prevailing Triaxial Shapes in Heavy Nuclei Driven by Nuclear Tensor Force

Virtually any object can rotate. While the rotation provides various intriguing physics cases, the primary picture for atomic nuclei was simple. Rotational bands have been observed for many nuclei, and their basic picture is considered to have been established in 1950s. We, however, show that this traditional picture is superseded with a novel picture arising from basic characteristics of the nuclear forces. In the traditional view, as stressed by Aage Bohr in his Nobel lecture, most of heavy nuclei are like axially-symmetric prolate ellipsoids (i.e., with two shorter axes of equal length), rotating about one of the short axes, like a rod. In the present picture, however, in many cases, the lengths of these three axes are all different, called triaxial. The triaxial shape yields more complex rotations, which actually reproduce experimental data as shown by state-of-the-art Configuration Interaction calculations, on supercomputers. The key to differentiate the two pictures is the nuclear tensor force, which is known to produce the shell evolution in exotic nuclei, a major agenda of Rare-Isotope physics. We now demonstrate that the same tensor force generates, in many nuclei, the triaxiality, fading the prolate-ellipsoid dominance away. The tensor force is a direct consequence of one $\pi$ meson exchange between nucleons, and the present finding is regarded as the first explicit or visible case that elementary particles directly affect nuclear shapes. The importance of the explicit tensor force is in the same line as Weinberg's modeling of nuclear forces. This feature makes the new picture robust, and may cast challenges for other many-body systems having forces similar to the tensor force. Substantial impacts on superheavy nuclei and fission are anticipated. This study sheds lights on the earlier suggestion of dominant triaxiality by Davydov, a Ukrainian physicist.
Size Distributions Reveal Regime Transition of Lake Systems Under Different Dominant Driving Forces

Power law size distribution is found to associate with fractal, self‐organized behaviors and patterns of complex systems. Such distribution also emerges from natural lakes, with potentially important links to the dynamics of lake systems. But the driving mechanism that generates and shapes this feature in lake systems remains unclear. Moreover, the power law itself was found inadequate for fully describing the size distribution of lakes, due to deviations at the two ends of size range. Based on observed and simulated lakes in China's 11 hydro‐climatic zones, we established a conceptual model for lake systems, which covers the whole size range of lake size distribution and reveals the underlying driving mechanism. The full lake size distribution is composed of three components, with three phases featured by exponential, stretched‐exponential and power law distribution. The three phases represent system states with successively increasing degrees of heterogeneity and orderliness, and more importantly, indicate the dominance of exogenic and endogenic forces, respectively. As the dominant driving force changes from endogenic to exogenic, a phase transition occurs with lake size distribution shifted from power law to stretched‐exponential and further to exponential distribution. Apart from compressing the power law phase, exogenic force also increases its scaling exponent, driving the corresponding lake size power spectrum into the regime of “blue noise. ” During this process, the autocorrelation function of the lake system diverges with a possibility of going to infinity, indicating the loss of system resilience.
The Many Holes of Preferential Attachment – Asymptotics of the Expected Betti Numbers of Preferential Attachment Clique Complexes

The preferential attachment model is a natural and popular random graph model for a growing network that contains very well-connected “hubs”. We study the higher-order connectivity of such a network by investigating the topological properties of its clique complex. We concentrate on the expected Betti numbers, a sequence of topological invariants of the complex related to the numbers of holes of diﬀerent dimensions. We determine the asymptotic growth rates of the expected Betti numbers, and prove that the expected Betti number at dimension 1 grows linearly fast, while those at higher dimensions grow sublinearly fast. Our theoretical results are illustrated by simulations.
PointGPT: Auto-regressively Generative Pre-training from Point Clouds

Large language models (LLMs) based on the generative pre-training transformer (GPT) have demonstrated remarkable effectiveness across a diverse range of downstream tasks. Inspired by the advancements of the GPT, we present PointGPT, a novel approach that extends the concept of GPT to point clouds, addressing the challenges associated with disorder properties, low information density, and task gaps. Specifically, a point cloud auto-regressive generation task is proposed to pre-train transformer models. Our method partitions the input point cloud into multiple point patches and arranges them in an ordered sequence based on their spatial proximity. Then, an extractor-generator based transformer decoder, with a dual masking strategy, learns latent representations conditioned on the preceding point patches, aiming to predict the next one in an auto-regressive manner. Our scalable approach allows for learning high-capacity models that generalize well, achieving state-of-the-art performance on various downstream tasks. In particular, our approach achieves classification accuracies of 94.9% on the ModelNet40 dataset and 93.4% on the ScanObjectNN dataset, outperforming all other transformer models. Furthermore, our method also attains new state-of-the-art accuracies on all four few-shot learning benchmarks.
Rates on Yaglom’s limit for Galton-Watson processes in a varying environment

A Galton-Watson process in a varying environment is a discrete time branching process where the offspring distributions vary among generations. It is known that in the critical case, these processes present a Yaglom limit, that is, a suitable normalization of the process conditioned on non-extinction converges in distribution to a standard exponential random variable. In this manuscript, we provide the rate of convergence of the Yaglom limit with respect to the Wasserstein metric.
Differentially Private Adapters for Parameter Efficient Acoustic Modeling

In this work, we devise a parameter-efficient solution to bring differential privacy (DP) guarantees into adaptation of a cross-lingual speech classifier. We investigate a new frozen pre-trained adaptation framework for DP-preserving speech modeling without full model fine-tuning. First, we introduce a noisy teacher-student ensemble into a conventional adaptation scheme leveraging a frozen pre-trained acoustic model and attain superior performance than DP-based stochastic gradient descent (DPSGD). Next, we insert residual adapters (RA) between layers of the frozen pre-trained acoustic model. The RAs reduce training cost and time significantly with a negligible performance drop. Evaluated on the open-access Multilingual Spoken Words (MLSW) dataset, our solution reduces the number of trainable parameters by 97.5% using the RAs with only a 4% performance drop with respect to fine-tuning the cross-lingual speech classifier while preserving DP guarantees.
How to Quickly Detect a Change while Sleeping Almost all the Time

Change-point detection procedures usually operate under full sampling; all observations are available until when the change is declared. Is this necessary? This paper addresses this question and investigates the minimum sampling rate needed in order to detect a transient change as efficiently as under full sampling. The problem is cast into a Bayesian setup where the change, assumed to be of fixed known duration $n$, starts at a time $\nu$ that is uniformly distributed within time interval $\{1,2,\ldots,A_n=2^{\alpha n}\}$ for some known parameter $\alpha>0$. It is assumed that observations are i.i. d. $P_0$ except during the change period where it is i.i. d. $P_1$. It is shown that, for any fixed $\alpha \in (0,D(P_1||P_0))$, where $D(P_1||P_0)$ denotes the relative entropy between $P_1$ and $P_0$, as long as the sampling rate is of order $\omega(1/n)$ the change can be detected with the same minimum, linear in $n$, delay as under full sampling, in the limit of vanishing false-alarm probability as $n\to \infty$. This is achieved by means of a new multi-zoom detection procedure with the following freatures. It agressively rejects and skips samples generated according to $P_0$ while it sharply increases the sampling rate when a change occurs thereby minimizing detection delay. Conversely, if $\alpha>D(P_1||P_0) $ or if the sampling rate is $o(1/n)$ the delay is $\Theta(A_n=2^{\alpha n})$.
A large $|\eta|$ approach to single field inflation

Single field models of inflation capable to produce primordial black holes usually require a significant departure from the standard, perturbative slow-roll regime. In fact, in many of these scenarios, the size of the slow-roll parameter $|\eta|$ becomes larger than one during a short phase of inflationary evolution. In order to develop an analytical control on these systems, we explore the limit of $|\eta|$ large, and promote $1/|\eta|$ to a small quantity to be used for perturbative expansions. Formulas simplify, and we obtain analytic expressions for the two and three point functions of curvature fluctuations, which share some of the features found in realistic inflationary models generating primordial black holes. We study one-loop corrections in this framework: we discuss criteria for adsorbing ultraviolet divergences into the available parameters, leaving log-enhanced infrared contributions of controllable size.
Two-Screen Scattering in CRAFT FRBs

Temporal broadening is a commonly observed property of fast radio bursts (FRBs), associated with turbulent media which cause radiowave scattering. Similarly to dispersion, scattering is an important probe of the media along the line of sight to an FRB source, such as the circum-burst or circum-galactic mediums (CGM). Measurements of characteristic scattering times alone are insufficient to constrain the position of the dominant scattering media along the line of sight. However, where more than one scattering screen exists, Galactic scintillation can be leveraged to form strong constraints. We quantify the scattering and scintillation in 10 FRBs with 1) known host galaxies and redshifts and 2) captured voltage data enabling high-time resolution analysis. We find strong evidence for two screens in three cases. For FRBs 20190608B and 20210320C, we find evidence for scattering screens less than approximately 16.7 and 3000 kpc respectively, from their sources, consistent with the scattering occurring in the circum-burst environment, the host ISM (inter-stellar medium) or the CGM. For FRB 20201124A we find a low modulation index that evolves over the burst's scattering tail, indicating the presence of a scattering screen $\approx9$ kpc from the host, and excluding the circum-burst environment from potential scattering sites. By assuming that pulse broadening is contributed by the host galaxy ISM or circum-burst environment, the lack of observed scintillation in four FRBs in our sample suggests that existing models may be poor estimators of scattering times associated with the Milky Way's ISM, similar to the anomalously low scattering observed for FRB 20201124A.
Antiferromagnetic-ferromagnetic homostructures with Dirac magnons in the van der Waals magnet 
CrI3

Van der Waals (vdW) Dirac magnon system CrI$_3$, a potential host of topological edge magnons, orders ferromagnetically (FM) (T$_C=61$ K) in the bulk, but antiferromagnetic (AFM) order has been observed in nanometer thick flakes, attributed to monoclinic (M) type stacking. We report neutron scattering measurements on a powder sample where the usual transition to the rhombohedral (R) phase was inhibited for a majority of the structure. Elastic measurements (and the opening of a hysteresis in magnetization data on a pressed pellet) showed that an AFM transition is clearly present below $\sim$50 K, coexisting with the R-type FM order. Inelastic measurements showed a decrease in magnon energy compared to the R phase, consistent with a smaller interlayer magnetic coupling in M-type stacking. A gap remains at the Dirac point, suggesting that the same nontrivial magnon topology reported for the R phase may be present in the M phase as well.
Multimodal Web Navigation with Instruction-Finetuned Foundation Models

The progress of autonomous web navigation has been hindered by the dependence on billions of exploratory interactions via online reinforcement learning, and domain-specific model designs that make it difficult to leverage generalization from rich out-of-domain data. In this work, we study data-driven offline training for web agents with vision-language foundation models. We propose an instruction-following multimodal agent, WebGUM, that observes both webpage screenshots and HTML pages and outputs web navigation actions, such as click and type. WebGUM is trained by jointly finetuning an instruction-finetuned language model and a vision encoder with temporal and local perception on a large corpus of demonstrations. We empirically demonstrate this recipe improves the agent's ability of grounded multimodal perception, HTML comprehension, and multi-step reasoning, outperforming prior works by a significant margin. On the MiniWoB, we improve over the previous best offline methods by more than 45.8%, even outperforming online-finetuned SoTA, humans, and GPT-4-based agent. On the WebShop benchmark, our 3-billion-parameter model achieves superior performance to the existing SoTA, PaLM-540B. Furthermore, WebGUM exhibits strong positive transfer to the real-world planning tasks on the Mind2Web. We also collect 347K high-quality demonstrations using our trained models, 38 times larger than prior work, and make them available to promote future research in this direction.
Late-Constraint Diffusion Guidance for Controllable Image Synthesis

Diffusion models, either with or without text condition, have demonstrated impressive capability in synthesizing photorealistic images given a few or even no words. These models may not fully satisfy user need, as normal users or artists intend to control the synthesized images with specific guidance, like overall layout, color, structure, object shape, and so on. To adapt diffusion models for controllable image synthesis, several methods have been proposed to incorporate the required conditions as regularization upon the intermediate features of the diffusion denoising network. These methods, known as early-constraint ones in this paper, have difficulties in handling multiple conditions with a single solution. They intend to train separate models for each specific condition, which require much training cost and result in non-generalizable solutions. To address these difficulties, we propose a new approach namely late-constraint: we leave the diffusion networks unchanged, but constrain its output to be aligned with the required conditions. Specifically, we train a lightweight condition adapter to establish the correlation between external conditions and internal representations of diffusion models. During the iterative denoising process, the conditional guidance is sent into corresponding condition adapter to manipulate the sampling process with the established correlation. We further equip the introduced late-constraint strategy with a timestep resampling method and an early stopping technique, which boost the quality of synthesized image meanwhile complying with the guidance. Our method outperforms the existing early-constraint methods and generalizes better to unseen condition. Our code would be available.
Effect of preharvest spray of NAA, GA3 and Urea on fruiting, fruit quality and yield of ber (Zizyphus mauritiana Lamk.) cv. Banarasi Karaka

An experiment was conducted to assess the effect of preharvest sprays of NAA, GA3 and Urea on fruiting, fruit quality and yield of ber cv. Banarsi Karaka at the Horticulture Garden, Department of Horticulture, C.S. Azad, University of Agriculture and Technology, Kanpur during 200S-09. The experiment was planned in a randomized block design with nine treatments replicated thrice, viz. T1 (control), T2 (NAA 15 ppm), T3 (NAA 25 ppm), T4 (GA3 20 ppm), T5 (GA3 30 ppm), Ts(Urea 1.5%), T7 (Urea 2.0%), T8 (T2 +T4 +T6) and T9 (13 +T5 +T7). Foliar sprays of 25 and 30 ppm NAA and GA3 in conjunction with 2% urea produced significantly bigger sized fruits measuring 3.93 cm in length and 2.85 cm in width with maximum TSS (16.60° B) and sugar content (11.20%) as against the minimum of 2.00 cm, 3.00 cm, 13.8°B and 8.52% values, respectively recorded under control. The maximum fruit yield amounted to 147.00 kgltree in comparison to 55.0 kg of control.