Miracle-dz/UniFAR-FTUs huggingface dataset:
Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models

For a LLM to be trustworthy, its confidence level should be well-calibrated with its actual performance. While it is now common sense that LLM performances are greatly impacted by prompts, the confidence calibration in prompting LLMs has yet to be thoroughly explored. In this paper, we explore how different prompting strategies influence LLM confidence calibration and how it could be improved. We conduct extensive experiments on six prompting methods in the question-answering context and we observe that, while these methods help improve the expected LLM calibration, they also trigger LLMs to be over-confident when responding to some instances. Inspired by human cognition, we propose Fact-and-Reflection (FaR) prompting, which improves the LLM calibration in two steps. First, FaR elicits the known"facts"that are relevant to the input prompt from the LLM. And then it asks the model to"reflect"over them to generate the final answer. Experiments show that FaR prompting achieves significantly better calibration; it lowers the Expected Calibration Error by 23.5% on our multi-purpose QA tasks. Notably, FaR prompting even elicits the capability of verbally expressing concerns in less confident scenarios, which helps trigger retrieval augmentation for solving these harder instances.
Converse Barrier Certificates for Finite-time Safety Verification of Continuous-time Perturbed Deterministic Systems

In this paper, we investigate the problem of verifying the finite-time safety of continuous-time perturbed deterministic systems represented by ordinary differential equations in the presence of measurable disturbances. Given a finite time horizon, if the system is safe, it, starting from a compact initial set, will remain within an open and bounded safe region throughout the specified time horizon, regardless of the disturbances. The main contribution of this work is to uncover that there exists a time-dependent barrier certificate if and only if the system is safe. This barrier certificate satisfies the following conditions: negativity over the initial set at the initial time instant, non-negativity over the boundary of the safe set, and non-increasing behavior along the system dynamics over the specified finite time horizon. The existence problem is explored using a Hamilton-Jacobi differential equation, which has a unique Lipschitz viscosity solution.
Search for charged-lepton flavor violation in $\Upsilon(2S) \to \ell^\mp\tau^\pm$ ($\ell=e,\mu$) decays at Belle

We report a search for the charged-lepton flavor violation in $\Upsilon(2S) \to \ell^\mp\tau^\pm$ ($\ell=e,\mu$) decays using a $25~\fbi$ $\Upsilon(2S)$ sample collected by the Belle detector at the KEKB $e^{+}$$e^-$ asymmetric-energy collider. We find no evidence for a signal and set upper limits on the branching fractions ($\mathcal{B}$) at 90\% confidence level. We obtain the most stringent upper limits: $\mathcal{B}(\Ytomutau)<0.23 \times 10^{-6}$ and $\mathcal{B}(\Ytoetau)<1.12 \times 10^{-6}$.
Physics Informed Modeling of Ecosystem Respiration via Dynamic Mode Decomposition with Control Input

Ecosystem respiration (Reco) represents a major component of the global carbon cycle, and accurate characterization of its dynamics is essential for a comprehensive understanding of ecosystem-climate interactions and the impacts of climate extremes on the ecosystem. This paper presents a novel data-driven and physics-aware method for estimating Reco dynamics using the dynamic mode decomposition with control input (DMDc) technique, an emerging tool for analyzing nonlinear dynamical systems. The proposed model represents Reco as a state space model with an autonomous component and an exogenous control input. The control input can be any ecosystem driver(s), such as air temperature, soil temperature, or soil water content. This unique modeling approach allows controlled intervention to study the effects of different inputs on the system. Experimental results using Fluxnet2015 data show that the prediction accuracy of Reco dynamics achieved with DMDc is comparable to state-of-the-art methods, making it a promising tool for analyzing the dynamic behavior of different vegetation ecosystems on multi-temporal scales in response to different climatic drivers.
PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits

Despite the many use cases for large language models (LLMs) in creating personalized chatbots, there has been limited research on evaluating the extent to which the behaviors of personalized LLMs accurately and consistently reflect specific personality traits. We consider studying the behavior of LLM-based agents which we refer to as LLM personas and present a case study with GPT-3.5 and GPT-4 to investigate whether LLMs can generate content that aligns with their assigned personality profiles. To this end, we simulate distinct LLM personas based on the Big Five personality model, have them complete the 44-item Big Five Inventory (BFI) personality test and a story writing task, and then assess their essays with automatic and human evaluations. Results show that LLM personas' self-reported BFI scores are consistent with their designated personality types, with large effect sizes observed across five traits. Additionally, LLM personas' writings have emerging representative linguistic patterns for personality traits when compared with a human writing corpus. Furthermore, human evaluation shows that humans can perceive some personality traits with an accuracy of up to 80\%. Interestingly, the accuracy drops significantly when the annotators were informed of the AI's authorship.
LSPT: Long-term Spatial Prompt Tuning for Visual Representation Learning

Visual Prompt Tuning (VPT) techniques have gained prominence for their capacity to adapt pre-trained Vision Transformers (ViTs) to downstream visual tasks using specialized learnable tokens termed as prompts. Contemporary VPT methodologies, especially when employed with self-supervised vision transformers, often default to the introduction of new learnable prompts or gated prompt tokens predominantly sourced from the model's previous block. A pivotal oversight in such approaches is their failure to harness the potential of long-range previous blocks as sources of prompts within each self-supervised ViT. To bridge this crucial gap, we introduce Long-term Spatial Prompt Tuning (LSPT) - a revolutionary approach to visual representation learning. Drawing inspiration from the intricacies of the human brain, LSPT ingeniously incorporates long-term gated prompts. This feature serves as temporal coding, curbing the risk of forgetting parameters acquired from earlier blocks. Further enhancing its prowess, LSPT brings into play patch tokens, serving as spatial coding. This is strategically designed to perpetually amass class-conscious features, thereby fortifying the model's prowess in distinguishing and identifying visual categories. To validate the efficacy of our proposed method, we engaged in rigorous experimentation across 5 FGVC and 19 VTAB-1K benchmarks. Our empirical findings underscore the superiority of LSPT, showcasing its ability to set new benchmarks in visual prompt tuning performance.
Autonomous Integration of TSN-unaware Applications with QoS Requirements in TSN Networks

Modern industrial networks transport both best-effort and real-time traffic. Time-Sensitive Networking (TSN) was introduced by the IEEE TSN Task Group as an enhancement to Ethernet to provide high quality of service (QoS) for real-time traffic. In a TSN network, applications signal their QoS requirements to the network before transmitting data. The network then allocates resources to meet these requirements. However, TSN-unaware applications can neither perform this registration process nor profit from TSN's QoS benefits. The contributions of this paper are twofold. First, we introduce a novel network architecture in which an additional device autonomously signals the QoS requirements of TSN-unaware applications to the network. Second, we propose a processing method to detect real-time streams in a network and extract the necessary information for the TSN stream signaling. It leverages a Deep Recurrent Neural Network (DRNN) to detect periodic traffic, extracts an accurate traffic description, and uses traffic classification to determine the source application. As a result, our proposal allows TSN-unaware applications to benefit from TSNs QoS guarantees. Our evaluations underline the effectiveness of the proposed architecture and processing method.
Rolling vesicles: From confined rotational flows to surface-enabled motion

The interaction of surfaces in relative motion in wet environments is dominated by lubrication forces, which play a pivotal role in the dynamics of microscopic systems. Here, we develop motile vesicles that exploit lubrication forces to roll on substrates. The activity of the vesicle comes from the confined rotational flow generated by a driven rotating particle encapsulated within the vesicle by droplet-microfluidics. Lubrication forces driving vesicle rolling are controlled by membrane mechanics and its tribological properties. This provides the design principles for motile vesicles that exploit frictional forces to efficiently navigate through complex environments.
Asymmetry in Low-Rank Adapters of Foundation Models

Parameter-efficient fine-tuning optimizes large, pre-trained foundation models by updating a subset of parameters; in this class, Low-Rank Adaptation (LoRA) is particularly effective. Inspired by an effort to investigate the different roles of LoRA matrices during fine-tuning, this paper characterizes and leverages unexpected asymmetry in the importance of low-rank adapter matrices. Specifically, when updating the parameter matrices of a neural network by adding a product $BA$, we observe that the $B$ and $A$ matrices have distinct functions: $A$ extracts features from the input, while $B$ uses these features to create the desired output. Based on this observation, we demonstrate that fine-tuning $B$ is inherently more effective than fine-tuning $A$, and that a random untrained $A$ should perform nearly as well as a fine-tuned one. Using an information-theoretic lens, we also bound the generalization of low-rank adapters, showing that the parameter savings of exclusively training $B$ improves the bound. We support our conclusions with experiments on RoBERTa, BART-Large, LLaMA-2, and ViTs.
Massive parallelization and performance enhancement of an immersed boundary method based unsteady flow solver

High-fidelity simulations of unsteady fluid flow are now possible with advancements in high-performance computing hardware and software frameworks. Since computational fluid dynamics (CFD) computations are dominated by linear algebraic routines, they can be significantly accelerated through massive parallelization on graphics processing units (GPUs). Thus, GPU implementation of high-fidelity CFD solvers is essential in reducing the turnaround time for quicker design space exploration. In the present work, an immersed boundary method (IBM) based in-house flow solver has been ported to the GPU using OpenACC, a compiler directive-based heterogeneous parallel programming framework. Out of various GPU porting pathways available, OpenACC was chosen because of its minimum code intrusion, low development time, and striking similarity with OpenMP, a similar directive-based shared memory programming framework. A detailed validation study and performance analysis of the parallel solver implementations on the CPU and GPU are presented. The GPU implementation shows a speedup up to the order $O(10)$ over the CPU parallel version and up to the order $O(10^2)$ over the serial code. The GPU implementation also scales well with increasing mesh size owing to the efficient utilization of the GPU processor cores.
How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation

Deep Learning is the state-of-the-art technology for segmenting brain tumours. However, this requires a lot of high-quality data, which is difficult to obtain, especially in the medical field. Therefore, our solutions address this problem by using unconventional mechanisms for data augmentation. Generative adversarial networks and registration are used to massively increase the amount of available samples for training three different deep learning models for brain tumour segmentation, the first task of the BraTS2023 challenge. The first model is the standard nnU-Net, the second is the Swin UNETR and the third is the winning solution of the BraTS 2021 Challenge. The entire pipeline is built on the nnU-Net implementation, except for the generation of the synthetic data. The use of convolutional algorithms and transformers is able to fill each other's knowledge gaps. Using the new metric, our best solution achieves the dice results 0.9005, 0.8673, 0.8509 and HD95 14.940, 14.467, 17.699 (whole tumour, tumour core and enhancing tumour) in the validation set.
Think Big, Generate Quick: LLM-to-SLM for Fast Autoregressive Decoding

Large language models (LLMs) have become ubiquitous in practice and are widely used for generation tasks such as translation, summarization and instruction following. However, their enormous size and reliance on autoregressive decoding increase deployment costs and complicate their use in latency-critical applications. In this work, we propose a hybrid approach that combines language models of different sizes to increase the efficiency of autoregressive decoding while maintaining high performance. Our method utilizes a pretrained frozen LLM that encodes all prompt tokens once in parallel, and uses the resulting representations to condition and guide a small language model (SLM), which then generates the response more efficiently. We investigate the combination of encoder-decoder LLMs with both encoder-decoder and decoder-only SLMs from different model families and only require fine-tuning of the SLM. Experiments with various benchmarks show substantial speedups of up to $4\times$, with minor performance penalties of $1-2\%$ for translation and summarization tasks compared to the LLM.
Research and Constructing Carbon Emission Prediction Model in China Electricity Industry Based on Energy Internet

The deep integration with energy technology and Internet technology provides the possibility of reducing environmental pollution and solving the problem of energy shortage. In the context of energy Internet, this paper analyzes the influencing factors of carbon emission in the electricity industry, constructs a carbon emission prediction model in China electricity industry based on the ridge estimation and multiple linear regression method, and carries out simulated analysis and accuracy test of the prediction model by bringing in the relevant data. The results show that the power supply structure, economic development, electricity consuming structure and electricity loss have a promoting effect on the carbon emission, while the implementation of power demand side management and the industrial structure have an inhibiting effect. Goodness of fit of the prediction model is up to 99.4%.
On the Whitney distortion extension problem for C m ( R n ) and C ∞ ( R n ) and its applications to interpolation and alignment of data in R n

Let n, m ≥ 1 , U ⊂ R n open. In this paper we provide a sharp solution to the following Whitney distortion extension problems: (a) Let φ : U → R n be a C m map. If E ⊂ U is compact (with some geometry) and the restriction of φ to E is an almost isometry with small distortion, how to decide when there exists a C m ( R n ) one-to-one and onto almost isometry Φ : R n → R n with small distortion which agrees with φ in a neighborhood of E and a Euclidean motion A : R n → R n away from E . (b) Let φ : U → R n be C ∞ map. If E ⊂ U is compact (with some geometry) and the restriction of φ to E is an almost isometry with small distortion, how to decide when there exists a C ∞ ( R n ) one-to-one and onto almost isometry Φ : R n → R n with small distortion which agrees with φ in a neighborhood of E and a Euclidean motion A : R n → R n away from E . Our results complement those of [14, 15] where there, E is a ﬁnite set. In this case, the problem above is also a problem of interpolation and alignment of data in R n .
3D Localization of Microparticles from Holographic Images using Neural Networks

Digital in-line holography is a versatile and reliable imaging technique for characterizing the size and spatial distribution of particles in flows. It maps 3D particle snapshots into a 2D image – a hologram. The analysis bottleneck of in-line holographic imaging is the immense cost of computation and manual effort that must be invested. Here we propose a learning-based approach to extract the size and 3D spatial distribution of objects from holograms. We outperform the standard propagation based method in terms of detection rate, precision and processing time on a small sensor aperture and at 1 / 4 of the original resolution. Our method performs with an F1 score of 0.91 in suspensions with more than 60 particles / cm 3 , which is an 18 percent performance boost in comparison to propagation-based software commonly used in practice. At the same time, our method is also several orders of magnitude faster, eliminating the computational bottleneck.
Joint 3D inversion of gravity and MT data using Gramian constraints: A case study from Yellowstone

The joint inversion of multiple geophysical datasets can produce more reliable models than the inversion of a single dataset; however, the implementation of the joint inversion can be challenging. There are two main approaches — the petrophysical approach and the structural approach. We detail both approaches based on a simultaneous joint inversion algorithm using the integral equation method coupled with a regularized conjugate gradient inversion. The development presented here is based on the use of Gramian constraints in a joint parametric functional to enforce structural similarity between jointly inverted density and resistivity models using either a direct correlation of the model parameters or a correlation of the gradients of the models. We demonstrate the effectiveness of the developed method and algorithm with two synthetic model studies and a case study of the joint inversion of gravity and magnetotelluric (MT) data acquired over Yellowstone National Park.
Effect Of Brand Equity On Consumer Purchase Intention With Special Reference To Two-Wheeler Industry

Brand equity is the added value for a product which keeps it differentiated from other competing products. It acts a competitive tool for the manufacturers which helps in preference and repurchase of the brand also helps in improving the purchase intentions of the customer. The study is focused on finding out the relationship between the brand equity and the purchase intention of the customer. The research will also aim at studying the four elements of brand equity i.e. brand loyalty, brand image, brand awareness and perceived quality and look up to whether these elements have relationship with the purchase intention of the customer. The study is exploratory in nature. In this study only secondary data is collected and among the secondary data, literature reviews are collected for the purpose of the study. Through the review of literature the researcher will be able to make a comparison between the existing studies and the new study and it will lead to a theoretical conclusion of the study. Surveys are not conducted in this study so primary data is not collected thereby no sampling design is used for the research. Data analysis and data interpretations are not used in this study. Data analysis and data interpretation is mainly used for the research and studies that are done on the basis of primary data. From the study it is found out that the elements of brand equity (brand loyalty, brand image, brand awareness and perceived quality) have a causal relationship with customer purchase intention. In closer study it was found that these four elements also positively affect the purchase intention of a two-wheeler customer. This suggests that brand equity and its elements provoke a customer to go for a brand with higher brand value considering the market conditions. So marketers must improve their brand equity to increase the industrial users which results to more consumption of their product and frequency of purchase.
HardBlare: an eﬀicient hardware-assisted DIFC for non-modified embedded processors

Information Flow Control is a security mechanisms that provides security guarantees about information propagation. Other security mechanisms such as access control or cryptography can be used to limit the dissemination of conﬁdential information and the modiﬁcation of high integrity contents. However, they do not enforce end-to-end properties. They cannot control the dissemination of information once ﬁle access is allowed or the data is decrypted. In this context, HardBlare proposes a software/hardware codesign methodology to ensure that security properties are preserved all allong the execution of the system but also during ﬁles storage. The general context of HardBlare is to address Dynamic Information Flow Control (DIFC) that generally consists in attaching marks (also known as tags) to denote the type of information that are saved or generated within the system.
The Efficient Big Data Research Technologies, Opportunities, and Challenges

Big Data has gained much attention from the academia and the IT industry. In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the boundary range. Currently, over 2 billion people worldwide are connected to the Internet, and over 5 billion individuals own mobile phones. By 2020, 50 billion devices are expected to be connected to the Internet. At this point, predicted data production will be 44 times greater than that in 2009. As information is transferred and shared at light speed on optic fiber and wireless networks, the volume of data and the speed of market growth increase. However, the fast growth rate of such large data generates numerous challenges, such as the rapid growth of data, transfer speed, diverse data, and security. Nonetheless, Big Data is still in its infancy stage, and the domain has not been reviewed in general. Hence, this study comprehensively surveys and classifies the various attributes of Big Data, including its nature, definitions, rapid growth rate, volume, management, analysis, and security. This study also proposes a data life cycle that uses the technologies and terminologies of Big Data. Future research directions in this field are determined based on opportunities and several open issues in Big Data domination. These research directions facilitate the exploration of the domain and the development of optimal techniques to address Big Data.
A comparison of NDVI and EVI in the DisTrad model for thermal sub-pixel mapping in densely vegetated areas: a case study in Southern China

ABSTRACT The DisTrad (Disaggregation Procedure for Radiometric Surface Temperature) model shows limited applicability for sub-pixel mapping of thermal remote-sensing images in densely vegetated areas due to the phenomenon of normalized difference vegetation index (NDVI) saturation. In this article, we compared the effect of NDVI and enhanced vegetation index (EVI) in the DisTrad model for thermal sub-pixel mapping in densely vegetated areas due to their different sensitivity in densely vegetated areas. Taking Ganzhou in Southern China as an example, we produced 250-m thermal remote-sensing images from a 1000-m image using 250-m NDVI and EVI data. After comparing with the synchronous 90-m thermal image from advanced spaceborne thermal emission and reflection radiometer, we found that the EVI can achieve a better result than NDVI in densely vegetated areas.