Miracle-dz/UniFAR-FTUs huggingface dataset:
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Tight hydrocarbon reservoirs require stimulation to improve the recovery of oil and gas resources. Hydraulic fracturing is a technique extensively employed in the oil and gas industry to generate fractures including primary and secondary fractures. To keep these fractures open, proppants are used. However, some of these ractures are very narrow for conventional proppants to penetrate and prop, hence smaller proppants called micro-proppants are required. These micro-proppants can improve the hydraulic conductivity of both, the primary fractures and the untouched microfractures, leading to enhanced oil and gas recovery. This paper presents a critical review on the progress of current micro-proppants models, technologies and field applications (sub 100mesh proppants) with a particular attention to micro-proppants placement in hydraulic and natural fractures. The impact of various factors on micro-proppant placement in the fractures is analyzed. These factors include proppant concentration (i.e., volume fraction of solid proppant), proppant size, fracturing fluid chemistry, and confining stress (causing proppant deformation and proppant embedment into the rock). This review concludes that using micro-proppants can improve the efficiency of the hydraulic fracturing treatment, leading to enhanced oil and gas production. ©
Efficient and Secure Group Key Management Scheme Based on Factorial Trees for Dynamic IoT Settings

The Internet of Things (IoT) extends Internet connectivity to resource-constrained devices such as sensors and actuators. It is prone to several security threats and attacks, due to which defence mechanisms such as encryption, message authentication codes, authentication, etc., need to be employed. Several IoT scenarios require secure communication to and data acquisition from multiple devices, which constitute a “group”. Also, in dynamic IoT scenarios, devices join or leave a group from time to time, due to which the group keys, which are used for multicasting information within the group, need to be efficiently updated. We propose a novel scheme, based on factorial trees and the Chinese Remainder Theorem, for efficient Group Key Management. Our proposed scheme prevents malicious users from accessing information from a group and efficiently updates the group keys when devices join or leave a group, while guaranteeing forward and backward secrecy. We evaluate the performance of the proposed scheme via extensive mathematical analysis and numerical computations, and show that it outperforms schemes proposed in prior work in terms of the communication and computation costs incurred by IoT devices.
Classification conjectures for Leavitt path algebras

The theory of Leavitt path algebras is intrinsically related, via graphs, to the theory of symbolic dynamics and $C^*$-algebras where the major classification programs have been a domain of intense research in the last 50 years. In this survey article, we gather together current lines of research in the classification of Leavitt path algebras, questions, conjectures, and some of the results about them that have been obtained so far.
Strongly vertex-reinforced jump process on graph with bounded degree

We study asymptotic behaviours of a non-linear vertex-reinforced jump process defined on an arbitrary infinite graph with bounded degree. We prove that if the reinforcement function $w$ is reciprocally integrable and strictly increasing, then the process visits only a finite number of vertices. In the case where $w$ is asymptotically equal to a super-linear polynomial, we show that the process eventually gets stuck on a star-shaped subgraph and there is exactly one vertex with unbounded local time.
Model Editing Can Hurt General Abilities of Large Language Models

One critical challenge that has emerged is the presence of hallucinations in the output of large language models (LLMs) due to false or outdated knowledge. Since retraining LLMs with updated information is resource-intensive, there has been a growing interest in model editing. However, current model editing methods, while effective in improving editing performance in various scenarios, often overlook potential side effects on the general abilities of LLMs. In this paper, we raise concerns that model editing inherently improves the factuality of the model, but may come at the cost of a significant degradation of these general abilities. Systematically, we analyze side effects by evaluating four popular editing methods on three LLMs across eight representative task categories. Extensive empirical research reveals that current model editing methods are difficult to couple well with LLMs to simultaneously improve the factuality and maintain the general abilities such as reasoning, question answering, etc. Strikingly, the use of a specific method to edit LLaMA-1 (7B) resulted in a drastic performance degradation to nearly 0 on all selected tasks with just a single edit. Therefore, we advocate for more research efforts to minimize the loss of general abilities acquired during LLM pre-training and to ultimately preserve them during model editing.
Improving the Robustness of Knowledge-Grounded Dialogue via Contrastive Learning

Knowledge-grounded dialogue (KGD) learns to generate an informative response based on a given dialogue context and external knowledge (e.g., knowledge graphs; KGs). Recently, the emergence of large language models (LLMs) and pre-training techniques has brought great success to knowledge-grounded dialogue. However, when building KGD systems in real applications, there are various real-world noises that are inevitable to face. For example, the dialogue context might involve perturbations such as misspellings and abbreviations. In addition, KGs typically suffer from incompletion and also might contain erroneous and outdated facts. Such real-world noises pose a challenge to the robustness of KGD systems and hinder their applications in the real world. In this paper, we propose an entity-based contrastive learning framework for improving the robustness of KGD. Specifically, we make use of the entity information in a KGD sample to create both its positive and negative samples which involve semantic-irrelevant and semantic-relevant perturbations, respectively. The contrastive learning framework ensures the KGD model is aware of these two types of perturbations, thus could generate informative responses with the potentially noisy inputs in real applications. Experimental results on three widely-used benchmark datasets show that our method achieves new state-of-the-art performance in terms of automatic evaluation scores, verifying its effectiveness and potentiality. Furthermore, we show that our method is able to generate better responses than comparison models in both the noisy and the few-shot settings.
MST: Adaptive Multi-Scale Tokens Guided Interactive Segmentation

Interactive segmentation has gained significant attention for its application in human-computer interaction and data annotation. To address the target scale variation issue in interactive segmentation, a novel multi-scale token adaptation algorithm is proposed. By performing top-k operations across multi-scale tokens, the computational complexity is greatly simplified while ensuring performance. To enhance the robustness of multi-scale token selection, we also propose a token learning algorithm based on contrastive loss. This algorithm can effectively improve the performance of multi-scale token adaptation. Extensive benchmarking shows that the algorithm achieves state-of-the-art (SOTA) performance, compared to current methods. An interactive demo and all reproducible codes will be released at https://github.com/hahamyt/mst.
Estimating Text Similarity based on Semantic Concept Embeddings

Due to their ease of use and high accuracy, Word2Vec (W2V) word embeddings enjoy great success in the semantic representation of words, sentences, and whole documents as well as for semantic similarity estimation. However, they have the shortcoming that they are directly extracted from a surface representation, which does not adequately represent human thought processes and also performs poorly for highly ambiguous words. Therefore, we propose Semantic Concept Embeddings (CE) based on the MultiNet Semantic Network (SN) formalism, which addresses both shortcomings. The evaluation on a marketing target group distribution task showed that the accuracy of predicted target groups can be increased by combining traditional word embeddings with semantic CEs.
Jahn-Teller driven quadrupolar ordering and spin-orbital dimer formation in GaNb$_{4}$Se$_{8}$

The lacunar spinel GaNb$_4$Se$_8$ is a tetrahedral cluster Mott insulator where spin-orbit coupling on molecular orbitals and Jahn-Teller energy scales are competitive. GaNb$_4$Se$_8$ undergoes a structural and anti-polar ordering transition at T$_Q$ = 50 K that corresponds to a quadrupolar ordering of molecular orbitals on Nb$_4$ clusters. A second transition occurs at T$_M$ = 29 K, where local distortions on the Nb$_4$ clusters rearrange. We present a single crystal x-ray diffraction investigation these phase transitions and solve the crystal structure in the intermediate T$_M$<T<T$_Q$ and low T<T$_M$ temperature phases. The intermediate phase is a primitive cubic P2$_1$3 structure with a staggered arrangement of Nb4 cluster distortions. A symmetry mode analysis reveals that the transition at TQ is continuous and described by a single Jahn-Teller active amplitude mode. In the low temperature phase, the symmetry of Nb$_4$ clusters is further reduced and the unit cell doubles into an orthorhombic P2$_1$2$_1$2$_1$ space group. Nb$_4$ clusters rearrange through this transition to form a staggered arrangement of intercluster dimers, suggesting a valence bond solid magnetic state.
SonicVisionLM: Playing Sound with Vision Language Models

There has been a growing interest in the task of generating sound for silent videos, primarily because of its practicality in streamlining video post-production. However, existing methods for video-sound generation attempt to directly create sound from visual representations, which can be challenging due to the difficulty of aligning visual representations with audio representations. In this paper, we present SonicVisionLM, a novel framework aimed at generating a wide range of sound effects by leveraging vision language models. Instead of generating audio directly from video, we use the capabilities of powerful vision language models (VLMs). When provided with a silent video, our approach first identifies events within the video using a VLM to suggest possible sounds that match the video content. This shift in approach transforms the challenging task of aligning image and audio into more well-studied sub-problems of aligning image-to-text and text-to-audio through the popular diffusion models. To improve the quality of audio recommendations with LLMs, we have collected an extensive dataset that maps text descriptions to specific sound effects and developed temporally controlled audio adapters. Our approach surpasses current state-of-the-art methods for converting video to audio, resulting in enhanced synchronization with the visuals and improved alignment between audio and video components. Project page: https://yusiissy.github. io/SonicVisionLM.github. io/
Universal Consistency of Wide and Deep ReLU Neural Networks and Minimax Optimal Convergence Rates for Kolmogorov-Donoho Optimal Function Classes

In this paper, we prove the universal consistency of wide and deep ReLU neural network classifiers trained on the logistic loss. We also give sufficient conditions for a class of probability measures for which classifiers based on neural networks achieve minimax optimal rates of convergence. The result applies to a wide range of known function classes. In particular, while most previous works impose explicit smoothness assumptions on the regression function, our framework encompasses more general settings. The proposed neural networks are either the minimizers of the logistic loss or the $0$-$1$ loss. In the former case, they are interpolating classifiers that exhibit a benign overfitting behavior.
Effective pruning of web-scale datasets based on complexity of concept clusters

Utilizing massive web-scale datasets has led to unprecedented performance gains in machine learning models, but also imposes outlandish compute requirements for their training. In order to improve training and data efficiency, we here push the limits of pruning large-scale multimodal datasets for training CLIP-style models. Today's most effective pruning method on ImageNet clusters data samples into separate concepts according to their embedding and prunes away the most prototypical samples. We scale this approach to LAION and improve it by noting that the pruning rate should be concept-specific and adapted to the complexity of the concept. Using a simple and intuitive complexity measure, we are able to reduce the training cost to a quarter of regular training. By filtering from the LAION dataset, we find that training on a smaller set of high-quality data can lead to higher performance with significantly lower training costs. More specifically, we are able to outperform the LAION-trained OpenCLIP-ViT-B32 model on ImageNet zero-shot accuracy by 1.1p. p. while only using 27.7% of the data and training compute. Despite a strong reduction in training cost, we also see improvements on ImageNet dist. shifts, retrieval tasks and VTAB. On the DataComp Medium benchmark, we achieve a new state-of-the-art Imagehttps://info.arxiv. org/help/prep#commentsNet zero-shot accuracy and a competitive average zero-shot accuracy on 38 evaluation tasks.
When Do Two Distributions Yield the Same Expected Euler Characteristic Curve in the Thermodynamic Limit?

Given a probability distribution $F$ on $\mathbb{R}^d$ with density $f$, consider a sample $X_n$ of $n$ points sampled from $F$ i.i. d. We study the Euler characteristic of the union of balls $\bigcup\limits_{x\in X_n} \overline{B}_{r_n}(x)$ in the thermodynamic limit. That is, as $n\to\infty$, we let $r_n\to 0$ such that $nr_n^d$ approaches a finite, non-zero limit. We express the limiting expected Euler characteristic of $F$ as an integral transform of that of the uniform distribution on $[0,1]^d$. This allows us to investigate under what conditions two distributions yield the same expected Euler characteristic in the thermodynamic limit. It turns out that this happens if and only if the two distributions have the same excess mass transform. Our proofs are constructive and enable new explicit calculations of expected Euler characteristics in low dimensions.
Geometrical frustration in nonlinear mechanics of screw dislocation

The existence of stress singularities and reliance on linear approximations pose significant challenges in comprehending the stress field generation mechanism around dislocations. This study employs differential geometry and calculus of variations to mathematically model and numerically analyse screw dislocations. The kinematics of the dislocation are expressed by the diffeomorphism of the Riemann--Cartan manifold, which includes both the Riemannian metric and affine connection. The modelling begins with a continuous distribution of dislocation density, which is transformed into torsion $\tau$ through the Hodge duality. The plasticity functional is constructed by applying the Helmholtz decomposition to bundle isomorphism, which is equivalent to the Cartan first structure equation for the intermediate configuration $\mathcal{B}$. The current configuration is derived by the elastic embedding of $\mathcal{B}$ into the standard Euclidean space $\mathbb{R}^3$. The numerical analysis reveals the elastic stress fields effectively eliminate the singularity along the dislocation line and exhibit excellent conformity with Volterra's theory beyond the dislocation core. Geometrical frustration is the direct source of dislocation stress fields, as demonstrated through the multiplicative decomposition of deformation gradients. By leveraging the mathematical properties of the Riemann--Cartan manifold, we demonstrate that the Ricci curvature determines the symmetry of stress fields. These results substantiate a long-standing mathematical hypothesis: the duality between stress and curvature.
A new cure rate model with discrete and multiple exposures

Cure rate models are mostly used to study data arising from cancer clinical trials. Its use in the context of infectious diseases has not been explored well. In 2008, Tournoud and Ecochard first proposed a mechanistic formulation of cure rate model in the context of infectious diseases with multiple exposures to infection. However, they assumed a simple Poisson distribution to capture the unobserved pathogens at each exposure time. In this paper, we propose a new cure rate model to study infectious diseases with discrete multiple exposures to infection. Our formulation captures both over-dispersion and under-dispersion with respect to the count on pathogens at each time of exposure. We also propose a new estimation method based on the expectation maximization algorithm to calculate the maximum likelihood estimates of the model parameters. We carry out a detailed Monte Carlo simulation study to demonstrate the performance of the proposed model and estimation algorithm. The flexibility of our proposed model also allows us to carry out a model discrimination. For this purpose, we use both likelihood ratio test and information-based criteria. Finally, we illustrate our proposed model using a recently collected data on COVID-19.
Explaining the Power of Topological Data Analysis in Graph Machine Learning

Topological Data Analysis (TDA) has been praised by researchers for its ability to capture intricate shapes and structures within data. TDA is considered robust in handling noisy and high-dimensional datasets, and its interpretability is believed to promote an intuitive understanding of model behavior. However, claims regarding the power and usefulness of TDA have only been partially tested in application domains where TDA-based models are compared to other graph machine learning approaches, such as graph neural networks. We meticulously test claims on TDA through a comprehensive set of experiments and validate their merits. Our results affirm TDA's robustness against outliers and its interpretability, aligning with proponents' arguments. However, we find that TDA does not significantly enhance the predictive power of existing methods in our specific experiments, while incurring significant computational costs. We investigate phenomena related to graph characteristics, such as small diameters and high clustering coefficients, to mitigate the computational expenses of TDA computations. Our results offer valuable perspectives on integrating TDA into graph machine learning tasks.
Circumgalactic Environments Around Distant Quasars 3C 9 and 4C 05.84

We present results from the Quasar hosts Unveiled by high Angular Resolution Techniques survey studying the circumgalactic medium (CGM) by observing rest-frame UV emission lines Lyα, C iv, and He ii around two radio-loud quasars, 3C 9 (z = 2.02) and 4C 05.84 (z = 2.32), using the Keck Cosmic Web Imager. We detect large-scale Lyα nebulae around both quasars with projected diameters ∼100 kpc, with spatially resolved, embedded 15–30 kpc He ii and C iv nebulae around both quasars as well as kinematically distinct He ii and C iv nebulae at a physical separation of ∼15 kpc from both quasars. Observations of Hα, Hβ, and [O iii] emission using Keck MOSFIRE spectroscopically confirm that the Lyα nebulae extend to companion galaxies and that these quasars are in a protogroup/protocluster environment. We confirm that the He ii and C iv emission is kinematically and spatially coincident with the companion galaxies. We estimate the virial masses of the companion galaxies, their metallicities, and star formation rates, and investigate the sources of ionization. We measure the dynamical mass of the host dark matter halos and estimate that the dark matter halos of these systems will grow to a mass of 2 × 1014 M ⊙ (3C 9) and 2 × 1013 M ⊙ (4C 05.84) by z = 0. The combined CGM and companion galaxies observations indicate Lyα substructure can indicate the presence of companion galaxies in the CGM.
U-Folds From Geodesics in Moduli Space

We exploit the presence of moduli fields in the ${\rm AdS}_3\times { S}^3\times CY_2$, where $CY_2=T^4$ or $K3$, solution to Type IIB superstring theory, to construct a U-fold solution with geometry ${\rm AdS}_2\times S^1\times {\rm S}^3\times CY_2$. This is achieved by giving a non-trivial dependence of the moduli fields in ${\rm SO}(4,n)/{\rm SO}(4)\times {\rm SO}(n)$ ($n=4$ for $CY_2=T^4$ and $n=20$ for $CY_2=K3$ ), on the coordinate $\eta$ of a compact direction $S^1$ along the boundary of ${\rm AdS}_3$, so that these scalars, as functions of $\eta$, describe a geodesic on the corresponding moduli space. The back-reaction of these evolving scalars on spacetime amounts to a splitting of ${\rm AdS}_3$ into ${\rm AdS}_2\times S^1$ with a non-trivial monodromy along $S^1$ defined by the geodesic. Choosing the monodromy matrix in ${\rm SO}(4,n;\,\mathbb{Z})$, this supergravity solution is conjectured to be a consistent superstring background. We generalize this construction starting from an ungauged theory in $D=2d$, $d$ odd, describing scalar fields non-minimally coupled to $(d-1)$-forms and featuring solutions with topology ${\rm AdS}_d\times S^d$, and moduli scalar fields. We show, in this general setting, that giving the moduli fields a geodesic dependence on the $\eta $ coordinate of an $S^1$ at the boundary of ${\rm AdS}_d$ is sufficient to split this space into ${\rm AdS}_{d-1}\times S^1$, with a monodromy along $S^1$ defined by the starting and ending points of the geodesic. This mechanism seems to be at work in the known J-fold solutions in $D=10$ Type IIB theory and hints towards the existence of similar solutions in the Type IIB theory compactified on $CY_2$. We argue that the holographic dual theory on these backgrounds is a 1+0 CFT on an interface in the 1+1 theory at the boundary of the original ${\rm AdS}_3$.
Astrobiological Potential of Venus Atmosphere Chemical Anomalies and Other Unexplained Cloud Properties.

Long-standing unexplained Venus atmosphere observations and chemical anomalies point to unknown chemistry but also leave room for the possibility of life. The unexplained observations include several gases out of thermodynamic equilibrium (e.g., tens of ppm O2, the possible presence of PH3 and NH3, SO2 and H2O vertical abundance profiles), an unknown composition of large, lower cloud particles, and the "unknown absorber(s). " Here we first review relevant properties of the venusian atmosphere and then describe the atmospheric chemical anomalies and how they motivate future astrobiology missions to Venus.
Ion temperature gradient mode mitigation by energetic particles, mediated by forced-driven zonal flows

In this work, we use the global electromagnetic and electrostatic gyro kinetic approaches to investigate the effects of zonal flows forced-driven by Alfv\'en modes due to their excitation by energetic particles (EPs), on the dynamics of ITG (Ion temperature gradient) instabilities. The equilibrium of the 92416 JET tokamak shot is considered. The linear and nonlinear Alfv\'en modes dynamics, as well as the zonal flow dynamics, are investigated and their respective radial structures and saturation levels are reported. ITG dynamics in the presence of the zonal flows excited by these Alfv\'en modes are also investigated. We find that, the zonal flows forced-driven by Alfv\'en modes can significantly impact the ITG dynamics. A zonal flow amplitude scan reveals the existence of an inverse relation between the zonal flow amplitude and the ITG growth rate. These results show that, forced-driven zonal flows can be an important indirect part of turbulence mitigation due to the injection of energetic particles.