Miracle-dz/UniFAR-FTUs huggingface dataset:
A Finite Element Model for Hydro-thermal Convective Flow in a Porous Medium: Effects of Hydraulic Resistivity and Thermal Diffusivity

In this article, a finite element model is implemented to analyze hydro-thermal convective flow in a porous medium. The mathematical model encompasses Darcy's law for incompressible fluid behavior, which is coupled with a convection-diffusion-type energy equation to characterize the temperature in the porous medium. The current investigation presents an efficient, stable, and accurate finite element discretization for the hydro-thermal convective flow model. The well-posedness of the proposed discrete Galerkin finite element formulation is guaranteed due to the decoupling property and the linearity of the numerical method. Computational experiments confirm the optimal convergence rates for a manufactured solution. Several numerical results are obtained for the variations of the hydraulic resistivity and thermal diffusivity. In the present study, the bottom wall is maintained at a constant higher hot temperature while side vertical walls are thermally insulated and the top wall is maintained at a constant cold temperature. Heat transfer rates at the heated bottom wall are presented in terms of local Nusselt number. A linear variation in hydraulic resistivity and a quadratic variation in thermal diffusivity show an increase in the heat transfer rate.
Images and flares of geodesic hotspots around a Kerr black hole

In this study, we develop a numerical method to generate images on an observer's screen, formed by radiation from hotspots on any timelike orbits outside a black hole. This method uses the calculation of fractional numbers, enabling us not only to produce the overall image but also to distinguish between primary, secondary, and higher-order images. Building upon this, we compute the images of hotspots from eight potential types of geodesic timelike orbits outside a Kerr black hole, summarizing the properties of both the overall and individual order images. Furthermore, we calculate the centroid motion and lightcurve. Notably, we observe flare phenomena across all orbit types and classify these flares into three categories based on the Doppler and gravitational redshift effects.
Addressing Order Sensitivity of In-Context Demonstration Examples in Causal Language Models

In-context learning has become a popular paradigm in natural language processing. However, its performance can be significantly influenced by the order of in-context demonstration examples. In this paper, we found that causal language models (CausalLMs) are more sensitive to this order compared to prefix language models (PrefixLMs). We attribute this phenomenon to the auto-regressive attention masks within CausalLMs, which restrict each token from accessing information from subsequent tokens. This results in different receptive fields for samples at different positions, thereby leading to representation disparities across positions. To tackle this challenge, we introduce an unsupervised fine-tuning method, termed the Information-Augmented and Consistency-Enhanced approach. This approach utilizes contrastive learning to align representations of in-context examples across different positions and introduces a consistency loss to ensure similar representations for inputs with different permutations. This enhances the model's predictive consistency across permutations. Experimental results on four benchmarks suggest that our proposed method can reduce the sensitivity to the order of in-context examples and exhibit robust generalizability, particularly when demonstrations are sourced from a pool different from that used in the training phase, or when the number of in-context examples differs from what is used during training.
A Theoretical Result on the Inductive Bias of RNN Language Models

Recent work by Hewitt et al. (2020) provides a possible interpretation of the empirical success of recurrent neural networks (RNNs) as language models (LMs). It shows that RNNs can efficiently represent bounded hierarchical structures that are prevalent in human language. This suggests that RNNs' success might be linked to their ability to model hierarchy. However, a closer inspection of Hewitt et al.'s (2020) construction shows that it is not limited to hierarchical LMs, posing the question of what \emph{other classes} of LMs can be efficiently represented by RNNs. To this end, we generalize their construction to show that RNNs can efficiently represent a larger class of LMs: Those that can be represented by a pushdown automaton with a bounded stack and a generalized stack update function. This is analogous to an automaton that keeps a memory of a fixed number of symbols and updates the memory with a simple update mechanism. Altogether, the efficiency in representing a diverse class of non-hierarchical LMs posits a lack of concrete cognitive and human-language-centered inductive biases in RNNs.
Learning to See Through Dazzle

Machine vision is susceptible to laser dazzle, where intense laser light can blind and distort its perception of the environment through oversaturation or permanent damage to sensor pixels. Here we employ a wavefront-coded phase mask to diffuse the energy of laser light and introduce a sandwich generative adversarial network (SGAN) to restore images from complex image degradations, such as varying laser-induced image saturation, mask-induced image blurring, unknown lighting conditions, and various noise corruptions. The SGAN architecture combines discriminative and generative methods by wrapping two GANs around a learnable image deconvolution module. In addition, we make use of Fourier feature representations to reduce the spectral bias of neural networks and improve its learning of high-frequency image details. End-to-end training includes the realistic physics-based synthesis of a large set of training data from publicly available images. We trained the SGAN to suppress the peak laser irradiance as high as $10^6$ times the sensor saturation threshold - the point at which camera sensors may experience damage without the mask. The trained model was evaluated on both a synthetic data set and data collected from the laboratory. The proposed image restoration model quantitatively and qualitatively outperforms state-of-the-art methods for a wide range of scene contents, laser powers, incident laser angles, ambient illumination strengths, and noise characteristics.
Marginal Independence and Partial Set Partitions

We establish a bijection between marginal independence models on $n$ random variables and split closed order ideals in the poset of partial set partitions. We also establish that every discrete marginal independence model is toric in cdf coordinates. This generalizes results of Boege, Petrovic, and Sturmfels and Drton and Richardson, and provides a unified framework for discussing marginal independence models.
Fast Adversarial Attacks on Language Models In One GPU Minute

In this paper, we introduce a novel class of fast, beam search-based adversarial attack (BEAST) for Language Models (LMs). BEAST employs interpretable parameters, enabling attackers to balance between attack speed, success rate, and the readability of adversarial prompts. The computational efficiency of BEAST facilitates us to investigate its applications on LMs for jailbreaking, eliciting hallucinations, and privacy attacks. Our gradient-free targeted attack can jailbreak aligned LMs with high attack success rates within one minute. For instance, BEAST can jailbreak Vicuna-7B-v1.5 under one minute with a success rate of 89% when compared to a gradient-based baseline that takes over an hour to achieve 70% success rate using a single Nvidia RTX A6000 48GB GPU. Additionally, we discover a unique outcome wherein our untargeted attack induces hallucinations in LM chatbots. Through human evaluations, we find that our untargeted attack causes Vicuna-7B-v1.5 to produce ~15% more incorrect outputs when compared to LM outputs in the absence of our attack. We also learn that 22% of the time, BEAST causes Vicuna to generate outputs that are not relevant to the original prompt. Further, we use BEAST to generate adversarial prompts in a few seconds that can boost the performance of existing membership inference attacks for LMs. We believe that our fast attack, BEAST, has the potential to accelerate research in LM security and privacy. Our codebase is publicly available at https://github.com/vinusankars/BEAST.
Active Level Set Estimation for Continuous Search Space with Theoretical Guarantee

A common problem encountered in many real-world applications is level set estimation where the goal is to determine the region in the function domain where the function is above or below a given threshold. When the function is black-box and expensive to evaluate, the level sets need to be found in a minimum set of function evaluations. Existing methods often assume a discrete search space with a finite set of data points for function evaluations and estimating the level sets. When applied to a continuous search space, these methods often need to first discretize the space which leads to poor results while needing high computational time. While some methods cater for the continuous setting, they still lack a proper guarantee for theoretical convergence. To address this problem, we propose a novel algorithm that does not need any discretization and can directly work in continuous search spaces. Our method suggests points by constructing an acquisition function that is defined as a measure of confidence of the function being higher or lower than the given threshold. A theoretical analysis for the convergence of the algorithm to an accurate solution is provided. On multiple synthetic and real-world datasets, our algorithm successfully outperforms state-of-the-art methods.
Notes on the Exact RG equation and the Wheeler-DeWitt equation

In this note, in the context of the AdS/CFT correspondence, the holographic derivation of the Wilsonian effective action is proposed. Then, the exact RG equation in the boundary theory is derived from the Wheeler-DeWitt equation of the bulk, following the suggestion of arXiv:hep-th/9912012,arXiv:hep-th/9912018, and arXiv:1010.1264. The relationship between the exact RG and Stochastic Quantization is briefly discussed.
Minimal Models for Altermagnetism

Altermagnets feature vanishing net magnetization, like antiferromagnets, but exhibit time-reversal symmetry breaking and momentum-dependent spin-split band structures. Motivated by the prevalence of altermagnetic materials with non-symmorphic symmetry-dictated band degeneracies, we provide realistic minimal models for altermagnetism by constructing tight-binding models for nonsymmorphic space groups with a sublattice defined by two magnetic atoms. These models can be applied to monoclinic, orthorhombic, tetragonal, rhombohedral, hexagonal, and cubic materials and can describe d-wave, g-wave, and i-wave altermagnetism. By examining the altermagnetic susceptibility and mean field instabilities within a Hubbard model we reveal that these models have altermagnetic ground states and yield a Berry curvature that is linear in the spin-orbit coupling. We apply our models to RuO$_2$, MnF$_2$, FeSb$_2$, $\kappa$-Cl, CrSb, and MnTe.
Some Lower Bounds on the Reach of an Algebraic Variety

Separation bounds are a fundamental measure of the complexity of solving a zero-dimensional system as it measures how difficult it is to separate its zeroes. In the positive dimensional case, the notion of reach takes its place. In this paper, we provide bounds on the reach of a smooth algebraic variety in terms of several invariants of interest: the condition number, Smale's $\gamma$ and the bit-size. We also provide probabilistic bounds for random algebraic varieties under some general assumptions.
Diversity of Pteroptyx Fireflies (Coleoptera: Lampyridae) and Their Display Trees at Klias Peninsula, Sabah, Malaysia

Congregating fireflies are among the most marvellous organisms that fill the mangrove forests with â€œliving fireworksâ€. These dainty creatures emit bioluminescence to attract mates by nightfall. Fireflies from the genus Pteroptyx aggregate on mangrove trees lighting up the riverine of several places in Malaysia, and this natural phenomenon provides local communities with lucrative business opportunity. Unsustainable management can bring disastrous consequences to the natural population of these graceful beetles. Without information on the current status of Pteroptyx fireflies in Klias Peninsula, any variation of this population in the future would be untraceable. This study was carried out to document the diversity of Pteroptyx fireflies and to identify their display trees in Klias Peninsula which is known as a firefly watching hotspot in Sabah. A total of four firefly species are found in Klias Peninsula, namely Pteroptyx tener Olivier, Pteroptyx malaccae Gorham, Pteroptyx valida Olivier, and Pteroptyx bearni Ballantyne. Pteroptyx tener is the dominant species in both Weston and Garama rivers while Pteroptyx bearni is the dominant species in Teratak River. The overall sex ratio on the display trees in Klias Peninsula was significantly biased towards the male. Seven mangrove species were selected as display trees by the congregating fireflies, particularly Excoecaria indica L. (Family Euphorbiaceae), Hibiscus tiliaceus, Nypa fruticans, Rhizophora apiculata, Avicennia alba, Excoecaria agallocha L. , and Sonneratia alba J. Smith (Family Lythraceae)-IUCN Red List. Information on the display tree species provides indispensable information for conservation and rehabilitation purposes while information on the Pteroptyx firefly species composition and abundance can be used as reference to track the spatial and temporal variation of firefly communities.
LSTP: Language-guided Spatial-Temporal Prompt Learning for Long-form Video-Text Understanding

Despite progress in video-language modeling, the computational challenge of interpreting long-form videos in response to task-specific linguistic queries persists, largely due to the complexity of high-dimensional video data and the misalignment between language and visual cues over space and time. To tackle this issue, we introduce a novel approach called Language-guided Spatial-Temporal Prompt Learning (LSTP). This approach features two key components: a Temporal Prompt Sampler (TPS) with optical flow prior that leverages temporal information to efficiently extract relevant video content, and a Spatial Prompt Solver (SPS) that adeptly captures the intricate spatial relationships between visual and textual elements. By harmonizing TPS and SPS with a cohesive training strategy, our framework significantly enhances computational efficiency, temporal understanding, and spatial-temporal alignment. Empirical evaluations across two challenging tasks--video question answering and temporal question grounding in videos--using a variety of video-language pretrainings (VLPs) and large language models (LLMs) demonstrate the superior performance, speed, and versatility of our proposed LSTP paradigm.
Exit Ripple Effects: Understanding the Disruption of Socialization Networks Following Employee Departures

Amidst growing uncertainty and frequent restructurings, the impacts of employee exits are becoming one of the central concerns for organizations. Using rich communication data from a large holding company, we examine the effects of employee departures on socialization networks among the remaining coworkers. Specifically, we investigate how network metrics change among people who historically interacted with departing employees. We find evidence of ``breakdown"in communication among the remaining coworkers, who tend to become less connected with fewer interactions after their coworkers' departure. This effect appears to be moderated by both external factors, such as periods of high organizational stress, and internal factors, such as the characteristics of the departing employee. At the external level, periods of high stress correspond to greater communication breakdown; at the internal level, however, we find patterns suggesting individuals may end up better positioned in their networks after a network neighbor's departure. Overall, our study provides critical insights into managing workforce changes and preserving communication dynamics in the face of employee exits.
Differential Galois Groups of Differential Central Simple Algebras and their Projective Representations

Let $F$ be a $\delta-$field (differential field) of characteristic zero with an algebraically closed field of constants $F^\delta$, $A$ be a $\delta-F-$central simple algebra, $K$ be a Picard-Vessiot extension for the $\delta-F-$module $A$ and $\mathscr G(K|F)$ be the $\delta-$Galois group of $K$ over $F. $ We prove that a $\delta-$field extension $L$ of $F,$ having $F^\delta$ as its field of constants, splits the $\delta-F-$central simple algebra $A$ if and only if the $\delta-$field $K$ embeds in $L. $ We then extend the theory of $\delta-F-$matrix algebras over a $\delta-$field $F,$ put forward by Magid&Juan (2008), to arbitrary $\delta-F-$central simple algebras. In particular, we establish a natural bijective correspondence between the isomorphism classes of $\delta-F-$central simple algebras of dimension $n^2$ over $F$ that are split by the $\delta-$field $K$ and the classes of inequivalent representations of the algebraic group $\mathscr G(K|F)$ in $\mathrm{PGL}_n(F^\delta). $ We show that $\mathscr G(K|F)$ is a reductive or a solvable algebraic group if and only if $A$ has certain kinds of $\delta-$right ideals.
Homogeneous spaces in Hartree-Fock-Bogoliubov theory

We study the action of Bogoliubov transformations on admissible generalized one-particle density matrices arising in Hartree-Fock-Bogoliubov theory. We show that the orbits of this action are reductive homogeneous spaces, and we give several equivalences that characterize when they are embedded submanifolds of natural ambient spaces. We use Lie theoretic arguments to prove that these orbits admit an invariant symplectic form. If, in addition, the operators in the orbits have finite spectrum, or infinite spectrum and trivial kernel, then we obtain that the orbits are actually K\"ahler homogeneous spaces.
Age, origin and tectonothermal modification of the Falun pyritic Zn-Pb-Cu-(Au-Ag) sulphide deposit, Bergslagen, Sweden

The Falun pyritic Zn-Pb-Cu-(Au-Ag) sulphide deposit, situated in the Palaeoproterozoic (1.9–1.8 Ga) Bergslagen lithotectonic unit in the south-western part of the Fennoscandian Shield, is one of th .
Hybrid Physics-Based and Data-Driven Modeling of Vascular Bifurcation Pressure Differences

Reduced-order models (ROMs) allow for the simulation of blood flow in patient-specific vasculatures without the high computational cost and wait time associated with traditional computational fluid dynamics (CFD) models. Unfortunately, due to the simplifications made in their formulations, ROMs can suffer from significantly reduced accuracy. One common simplifying assumption is the continuity of static or total pressure over vascular junctions. In many cases, this assumption has been shown to introduce significant error. We propose a model to account for this pressure difference, with the ultimate goal of increasing the accuracy of cardiovascular ROMs. Our model successfully uses a structure common in existing ROMs in conjunction with machine-learning techniques to predict the pressure difference over a vascular bifurcation. We analyze the performance of our model on steady and transient flows, testing it on three bifurcation cohorts representing three different bifurcation geometric types. We also compare the efficacy of different machine-learning techniques and two different model modalities.
Effect of date of sowing and cutting intervals on yield attribute, yield, quality and soil fertility of Lucerne [ Medicago sativa L.] under North Gujarat agroclimatic conditions

An experiment was carried out at the Agronomy Instructional Farm, C. P. College of Agriculture, Sardarkrushinagar Dantiwada Agricultural University, Sardarkrushinagar during rabi season of 2011-12 to find out the optimum date of sowing and cutting interval for Lucerne crop under north Gujarat agroclimatic conditions. The results of yield attributing characters indicated that significantly higher green leaf weight was recorded by 10th November sowing and 30 days cutting interval after common cut in the mean values of all cuts. Similarly, the mean green stem weight, green forage yield per plant and dry forage yield per plant was significantly higher by sowing the crop on 10th November and 30 days cutting interval after common cut and it was followed by 20th November sowing and 30 days cutting interval. Whereas, numerically higher crude protein content was noted by 20th November sowing and 25 days cutting interval in the mean values of all cuts. The effect of different date of sowing and cutting intervals on available nitrogen, phosphorous and potash was found non-significant after harvesting of the crop.
Frequently Using Passwords Increases Their Memorability – A False Assumption or Reality?

Password memorability is a significant problem that is getting worse as the numbers grow. As a direct result of memory limitations, adopted insecure password practices have substantial consequences as organizations lose millions to security breaches and helpdesk costs. IS research has examined memory theories to increase the memorability of passwords. However, in our research we have discovered some anomalous findings. It is commonly known that more frequently and recently recalled information is more easily remembered (assumed for password recall also); our previously collected objective data revealed no effect on password recall. This study will strive to confirm whether or not password memorability is affected by the frequency of password use and the time between use. If this study confirms our previous results, then this suggests that future IS research should look to other factors to increase password memorability and security, than just directly applying memory theories to the password problem.
Mesozoic High- and Low-SiO2 Adakites and A-Type Granites in the Lower Yangtze River Belt, Eastern China: Implications for Petrogenesis and Metallogeny

The Lower Yangtze River Belt (LYRB) is one of the important magmatic and metallogenic belts in China and hosts abundant Mesozoic calc-alkaline magmatic rocks and economic mineral deposits. Anqing orefield in the southwestern of the LYRB received less attention during the last two decades. Here, we present an integrated study of whole-rock major and trace elements, zircon U-Pb dating and Lu-Hf isotopes on late Mesozoic adakites and A-type granites from the Anqing orefield. The adakites emplaced during 138–136 Ma and can be further subdivided into two types: high-SiO2 adakites (HSA) with SiO2 60 wt % from the Zongpu intrusion, and low-SiO2 adakites (LSA) 60 wt % from the Yueshan intrusion. The rocks display mid- to high-K calc-alkaline features and have consistent arc-like trace element characteristics with enrichment in LREE and LILE, and depletion in HREE and HFSE. The distinct zircon εHf(t) values for the LSA (from −27 to −20) and HSA (from −15 to −5) preclude a magma mixing model, yet suggest a subduction-related setting with partial melting of the subducted slab and overlying metasomatic mantle wedge. The A-type granites dated at 124 Ma from the Dalongshan intrusion characterized by LILE and LREE enrichment and slightly negative Eu anomalies, with lower MgO, CaO but higher K2O and Na2O contents. Their zircon εHf(t) values and geochemical features suggest that the parent magma was produced by the partial melting of Neoproterozoic crustal components, followed by variable degrees of fractional crystallization under a back-arc extensional setting, together with minor juvenile crust input. The adakites and A-type granites investigated in this study record a tectonic transition from compressive to extensional setting during 138–124 Ma. The transitional magmatic pulses are associated with distinct metallogenic signature with the adakites hosting copper deposits and the A-type granites linked to uranium mineralization.