Miracle-dz/UniFAR-FTUs huggingface dataset:
TextGenSHAP: Scalable Post-hoc Explanations in Text Generation with Long Documents

Large language models (LLMs) have attracted huge interest in practical applications given their increasingly accurate responses and coherent reasoning abilities. Given their nature as black-boxes using complex reasoning processes on their inputs, it is inevitable that the demand for scalable and faithful explanations for LLMs' generated content will continue to grow. There have been major developments in the explainability of neural network models over the past decade. Among them, post-hoc explainability methods, especially Shapley values, have proven effective for interpreting deep learning models. However, there are major challenges in scaling up Shapley values for LLMs, particularly when dealing with long input contexts containing thousands of tokens and autoregressively generated output sequences. Furthermore, it is often unclear how to effectively utilize generated explanations to improve the performance of LLMs. In this paper, we introduce TextGenSHAP, an efficient post-hoc explanation method incorporating LM-specific techniques. We demonstrate that this leads to significant increases in speed compared to conventional Shapley value computations, reducing processing times from hours to minutes for token-level explanations, and to just seconds for document-level explanations. In addition, we demonstrate how real-time Shapley values can be utilized in two important scenarios, providing better understanding of long-document question answering by localizing important words and sentences; and improving existing document retrieval systems through enhancing the accuracy of selected passages and ultimately the final responses.
CZL-CIAE: CLIP-driven Zero-shot Learning for Correcting Inverse Age Estimation

Zero-shot age estimation aims to learn feature information about age from input images and make inferences about a given person's image or video frame without specific sample data. The development of zero-shot age estimation can improve the efficiency and accuracy of various applications (e.g., age verification and secure access control, etc.), while also promoting research on multi-modal and zero-shot learning in the social media field. For example, zero-sample age estimation can be used to create social networks focused on specific age groups. However, existing methods mainly focus on supervised, labeled age estimation learning, and the prediction effect of zero-shot learning is very poor. To tackle the above issues, we propose a novel CLIP-driven Zero-shot Learning for Correcting Inverse Age Estimation (CZL-CIAE). Specifically, we first introduce the CLIP model to extract image features and text semantic information respectively, and map them into a highly semantically aligned high-dimensional feature space. Next, we designed a new Transformer architecture (i.e., FourierFormer) to achieve channel evolution and spatial interaction of images, and to fuse image and text semantic information. Finally, we introduce reversible age estimation, which uses end-to-end error feedback to reduce the error rate of age predictions. Through extensive experiments on multiple data sets, CZL-CIAE has achieved better age prediction results.
w(1+infinity) Algebra with a Cosmological Constant and the Celestial Sphere

It is shown that in the presence of a nonvanishing cosmological constant, Strominger's infinite-dimensional $\mathrm{w_{1+\infty}}$ algebra of soft graviton symmetries is modified in a simple way. The deformed algebra contains a subalgebra generating $ SO(1,4)$ or $SO(2,3)$ symmetry groups of $\text{dS}_4$ or $\text{AdS}_4$, depending on the sign of the cosmological constant. The transformation properties of soft gauge symmetry currents under the deformed $\mathrm{w_{1+\infty}}$ are also discussed.
SchurVINS: Schur Complement-Based Lightweight Visual Inertial Navigation System

Accuracy and computational efficiency are the most important metrics to Visual Inertial Navigation System (VINS). The existing VINS algorithms with either high accuracy or low computational complexity, are difficult to provide the high precision localization in resource-constrained devices. To this end, we propose a novel filter-based VINS framework named SchurVINS, which could guarantee both high accuracy by building a complete residual model and low computational complexity with Schur complement. Technically, we first formulate the full residual model where Gradient, Hessian and observation covariance are explicitly modeled. Then Schur complement is employed to decompose the full model into ego-motion residual model and landmark residual model. Finally, Extended Kalman Filter (EKF) update is implemented in these two models with high efficiency. Experiments on EuRoC and TUM-VI datasets show that our method notably outperforms state-of-the-art (SOTA) methods in both accuracy and computational complexity. The experimental code of SchurVINS is available at https://github.com/bytedance/SchurVINS.
Physics simulation capabilities of LLMs

[Abridged abstract] Large Language Models (LLMs) can solve some undergraduate-level to graduate-level physics textbook problems and are proficient at coding. Combining these two capabilities could one day enable AI systems to simulate and predict the physical world. We present an evaluation of state-of-the-art (SOTA) LLMs on PhD-level to research-level computational physics problems. We condition LLM generation on the use of well-documented and widely-used packages to elicit coding capabilities in the physics and astrophysics domains. We contribute $\sim 50$ original and challenging problems in celestial mechanics (with REBOUND), stellar physics (with MESA), 1D fluid dynamics (with Dedalus) and non-linear dynamics (with SciPy). Since our problems do not admit unique solutions, we evaluate LLM performance on several soft metrics: counts of lines that contain different types of errors (coding, physics, necessity and sufficiency) as well as a more"educational"Pass-Fail metric focused on capturing the salient physical ingredients of the problem at hand. As expected, today's SOTA LLM (GPT4) zero-shot fails most of our problems, although about 40\% of the solutions could plausibly get a passing grade. About $70-90 \%$ of the code lines produced are necessary, sufficient and correct (coding \&physics). Physics and coding errors are the most common, with some unnecessary or insufficient lines. We observe significant variations across problem class and difficulty. We identify several failure modes of GPT4 in the computational physics domain. Our reconnaissance work provides a snapshot of current computational capabilities in classical physics and points to obvious improvement targets if AI systems are ever to reach a basic level of autonomy in physics simulation capabilities.
Theoretical Bounds for Noise Filtration using Low-Rank Tensor Approximations

Low-rank tensor approximation error bounds are proposed for the case of noisy input data that depend on low-rank representation type, rank and the dimensionality of the tensor. The bounds show that high-dimensional low-rank structured approximations provide superior noise-filtering properties compared to matrices with the same rank and total element count.
Dark Matter Interpretation of Neutron Multiplicity Anomalies

Subterrestrial neutron spectra show weak but consistent anomalies at multiplicities ~100 and above [1-3]. The data of the available measurements are of low statistical significance [4] but indicate an excess of events not correlated with the muon flux. The origin of the anomalies remains ambiguous but could be a signature of WIMP annihilation-like interaction with a Pb target. In this paper, we outline a model consistent with this hypothesis, the extended Standard Model (SM) approach called the Radiation Gauge Model (RGM) [5]. The RGM identifies scalar neutrino-antineutrino wave function components of WIMP Dark Matter (DM) responsible for the weak interaction leading to annihilation with ordinary matter. The model assigns neutrino-nucleon(target) charged current (CC) transitions to the observed anomalies. If the existence of the anomalies is confirmed and the model interpretation is positively verified, this will be the first terrestrial indirect detection of DM.
Informative Priors Improve the Reliability of Multimodal Clinical Data Classification

Machine learning-aided clinical decision support has the potential to significantly improve patient care. However, existing efforts in this domain for principled quantification of uncertainty have largely been limited to applications of ad-hoc solutions that do not consistently improve reliability. In this work, we consider stochastic neural networks and design a tailor-made multimodal data-driven (M2D2) prior distribution over network parameters. We use simple and scalable Gaussian mean-field variational inference to train a Bayesian neural network using the M2D2 prior. We train and evaluate the proposed approach using clinical time-series data in MIMIC-IV and corresponding chest X-ray images in MIMIC-CXR for the classification of acute care conditions. Our empirical results show that the proposed method produces a more reliable predictive model compared to deterministic and Bayesian neural network baselines.
Transformers are uninterpretable with myopic methods: a case study with bounded Dyck grammars

Interpretability methods aim to understand the algorithm implemented by a trained model (e.g., a Transofmer) by examining various aspects of the model, such as the weight matrices or the attention patterns. In this work, through a combination of theoretical results and carefully controlled experiments on synthetic data, we take a critical view of methods that exclusively focus on individual parts of the model, rather than consider the network as a whole. We consider a simple synthetic setup of learning a (bounded) Dyck language. Theoretically, we show that the set of models that (exactly or approximately) solve this task satisfy a structural characterization derived from ideas in formal languages (the pumping lemma). We use this characterization to show that the set of optima is qualitatively rich; in particular, the attention pattern of a single layer can be ``nearly randomized'', while preserving the functionality of the network. We also show via extensive experiments that these constructions are not merely a theoretical artifact: even after severely constraining the architecture of the model, vastly different solutions can be reached via standard training. Thus, interpretability claims based on inspecting individual heads or weight matrices in the Transformer can be misleading.
A New Upper Bound For the Growth Factor in Gaussian Elimination with Complete Pivoting

The growth factor in Gaussian elimination measures how large the entries of an LU factorization can be relative to the entries of the original matrix. It is a key parameter in error estimates, and one of the most fundamental topics in numerical analysis. We produce an upper bound of $n^{0.2079 \ln n +0.91}$ for the growth factor in Gaussian elimination with complete pivoting -- the first improvement upon Wilkinson's original 1961 bound of $2 \, n ^{0.25\ln n +0.5}$.
GIVT: Generative Infinite-Vocabulary Transformers

We introduce generative infinite-vocabulary transformers (GIVT) which generate vector sequences with real-valued entries, instead of discrete tokens from a finite vocabulary. To this end, we propose two surprisingly simple modifications to decoder-only transformers: 1) at the input, we replace the finite-vocabulary lookup table with a linear projection of the input vectors; and 2) at the output, we replace the logits prediction (usually mapped to a categorical distribution) with the parameters of a multivariate Gaussian mixture model. Inspired by the image-generation paradigm of VQ-GAN and MaskGIT, where transformers are used to model the discrete latent sequences of a VQ-VAE, we use GIVT to model the unquantized real-valued latent sequences of a $\beta$-VAE. In class-conditional image generation GIVT outperforms VQ-GAN (and improved variants thereof) as well as MaskGIT, and achieves performance competitive with recent latent diffusion models. Finally, we obtain strong results outside of image generation when applying GIVT to panoptic segmentation and depth estimation with a VAE variant of the UViM framework
Bitstream Organization for Parallel Entropy Coding on Neural Network-based Video Codecs

Video compression systems must support increasing bandwidth and data throughput at low cost and power, and can be limited by entropy coding bottlenecks. Efficiency can be greatly improved by parallelizing coding, which can be done at much larger scales with new neural-based codecs, but with some compression loss related to data organization. We analyze the bit rate overhead needed to support multiple bitstreams for concurrent decoding, and for its minimization propose a method for compressing parallel-decoding entry points, using bidirectional bitstream packing, and a new form of jointly optimizing arithmetic coding termination. It is shown that those techniques significantly lower the overhead, making it easier to reduce it to a small fraction of the average bitstream size, like, for example, less than 1% and 0.1% when the average number of bitstream bytes is respectively larger than 95 and 1,200 bytes.
Jacobian schemes arising from hypersurface arrangements in $\mathbb P^n$

Freeness is an important property of a hypersurface arrangement, although its presence is not well understood. A hypersurface arrangement in $\PP^n$ is free if $S/J$ is Cohen-Macaulay (CM), where $S = K[x_0,\ldots,x_n]$ and $J$ is the Jacobian ideal. We study two related unmixed ideals: $J^{top}$, the intersection of height two primary components, and $\sqrt{J}$, the radical. Under mild hypotheses, we show that these ideals are CM. This establishes a full generalization of an earlier result with Schenck from hyperplane arrangements to hypersurface arrangements. If the hypotheses fail for an arrangement in projective $3$-space, the Hartshorne-Rao module measures the failure of CMness. We establish consequences for the even liaison classes of $J^{top}$ and $\sqrt{J}$.
Self Generated Wargame AI: Double Layer Agent Task Planning Based on Large Language Model

The large language models represented by ChatGPT have a disruptive impact on the field of artificial intelligence. But it mainly focuses on natural language processing, speech recognition, machine learning and natural language understanding. This paper innovatively applies the large language model to the field of intelligent decision-making, places the large language model in the decision-making center, and constructs an agent architecture with the large language model as the core. Based on this, it further proposes a two-layer agent task planning, issues and executes decision commands through the interaction of natural language, and carries out simulation verification through the wargame simulation environment. Through the game confrontation simulation experiment, it is found that the intelligent decision-making ability of the large language model is significantly stronger than the commonly used reinforcement learning AI and rule AI, and the intelligence, understandability and generalization are all better. And through experiments, it was found that the intelligence of the large language model is closely related to prompt. This work also extends the large language model from previous human-computer interaction to the field of intelligent decision-making, which has important reference value and significance for the development of intelligent decision-making.
Flattening of the quantum effective potential in fermionic theories

We present methods to constrain fermionic condensates on the level of the path integral, which grant access to the quantum effective potential in the infinite volume limit. In the case of a spontaneously broken symmetry, this potential possesses a manifestly flat region, which is inaccessible to the standard approach on the lattice. However, by constraining the appropriate order parameters such as the chiral condensate, one is then able to probe the flat region. We demonstrate our method of constraining fermionic condensates in the 2-dimensional Gross-Neveu model, which exhibits a spontaneously broken chiral symmetry. We show how the potential flattens for increasing volume and that the flat region is dominated by inhomogeneous field configurations.
On the Effectiveness of Large Language Models in Domain-Specific Code Generation

Large language models (LLMs) such as ChatGPT have shown remarkable capabilities in code generation. Despite the great achievement, they rely on enormous training data to acquire a broad spectrum of open-domain knowledge. Besides, their evaluation revolves around open-domain benchmarks like HumanEval, which primarily consist of programming contests. Therefore, it is hard to fully characterize the intricacies and challenges associated with particular domains (e.g., web, game, and math). In this paper, we conduct an in-depth study of the LLMs in domain-specific code generation. Our results demonstrate that LLMs exhibit sub-optimal performance in generating domain-specific code, due to their limited proficiency in utilizing domain-specific libraries. We further observe that incorporating API knowledge as prompts can empower LLMs to generate more professional code. Based on these findings, we further investigate how to efficiently incorporate API knowledge into the code generation process. We experiment with three strategies for incorporating domain knowledge, namely, external knowledge inquirer, chain-of-thought prompting, and chain-of-thought fine-tuning. We refer to these strategies as a new code generation approach called DomCoder. Experimental results show that all strategies of DomCoder lead to improvement in the effectiveness of domain-specific code generation under certain settings. The results also show that there is still ample room for further improvement, based on which we suggest possible future works.
When it Rains, it Pours: Modeling Media Storms and the News Ecosystem

Most events in the world receive at most brief coverage by the news media. Occasionally, however, an event will trigger a media storm, with voluminous and widespread coverage lasting for weeks instead of days. In this work, we develop and apply a pairwise article similarity model, allowing us to identify story clusters in corpora covering local and national online news, and thereby create a comprehensive corpus of media storms over a nearly two year period. Using this corpus, we investigate media storms at a new level of granularity, allowing us to validate claims about storm evolution and topical distribution, and provide empirical support for previously hypothesized patterns of influence of storms on media coverage and intermedia agenda setting.
Decay of the Campanile limestone used as building material in Tudela Cathedral (Navarra, Spain)

Abstract A characterization is presented of the building materials used in the Cathedral of Tudela, as well as of the different forms of decay, with the aim of establishing the cause and mechanisms of this decay. The Cathedral of Tudela was built mainly with Campanile limestone from the upper Miocene. The campanile limestone is a wackestone, with a terrigenous content of 2.6% and 1.5–2.5% of organic matter. After a detailed investigation of all the different forms of stone decay, our conclusion is that the main type of damage affecting Campanile limestone has morphologies similar to a mechanical fracture with breakages of convex surfaces and resulting very sharp edges. The process of decay is caused by the expansion of the rock during the drying process, which has a very rapid and aggressive effect on the rock. Laboratory tests showed that through extreme drying in the presence of a magnesium sulphate solution, the salt crystallization inside the stone generates a strength greater than the tensile strength of the stone, thus causing a fracture and the loosening of rock fragments. The materials introduced in recent restorations (sandstone and Portland cement) provide the necessary magnesium for the development of this weathering in Campanile limestone.
Nano vs Resistant Tuberculosis: Taking the Lung Route

Tuberculosis (TB) is among the top 10 infectious diseases worldwide. It is categorized among the leading killer diseases that are the reason for the death of millions of people globally. Although a standardized treatment regimen is available, non-adherence to treatment has increased multi-drug resistance (MDR) and extensive drug-resistant (XDR) TB development. Another challenge is targeting the death of TB reservoirs in the alveoli via conventional treatment. TB Drug resistance may emerge as a futuristic restraint of TB with the scarcity of effective Anti-tubercular drugs. The paradigm change towards nano-targeted drug delivery systems is mostly due to the absence of effective therapy and increased TB infection recurrent episodes with MDR. The emerging field of nanotechnology gave an admirable opportunity to combat MDR and XDR via accurate diagnosis with effective treatment. The new strategies targeting the lung via the pulmonary route may overcome the new incidence of MDR and enhance patient compliance. Therefore, this review highlights the importance and recent research on pulmonary drug delivery with nanotechnology along with prevalence, the need for the development of nanotechnology, beneficial aspects of nanomedicine, safety concerns of nanocarriers, and clinical studies.
Demo: Video over Synchronous Flooding with OSFv6

In recent years, a considerable body of literature has shown how Synchronous Flooding (SF)-based communication can provide reliability and latency guarantees in multi-hop networks. However, to-date, the technique has failed to gain traction outside of the research community. While disap-pointing, this is perhaps not surprising. Many of the key works in SF have focused on improving performance gains or proposing tailored protocols, with little thought to the end-user or wider networking ecosystem. This demo addresses this gap and presents OSFv6 , an IPv6-compatible extension to the Open Synchronous Flooding (OSF) framework, that connects SF networks to the wider IP ecosystem and allows users to seamlessly run socket applications between host machines. We benchmark OSFv6 across the four BLE physical layers and show that, using the 2 Mbps physical layer (PHY), OSFv6 can achieve up to 250 kbps at 3 hops . Importantly, we use OSFv6 to transmit a live video stream between two hosts – demonstrating the application for SF networks outside of use-cases envisioned in current literature.
UK monetary and fiscal policy since the Great Recession-an evaluation

This paper explores the economic impacts of the Bank of England’s quantitative easing policy, implemented as a response to the global financial crisis. Using an open economy Dynamic Stochastic General Equilibrium (DSGE) model, we demonstrate that monetary policy can remain effective even when nominal interest rates have reached the zero lower bound. We estimate and test the model using the indirect inference method, and our simulations indicate that a nominal GDP targeting rule implemented through money supply could be the most effective monetary policy regime. Additionally, our analysis suggests that a robust, active fiscal policy regime with nominal GDP targeting could significantly enhance economic stabilization efforts. ∗