Miracle-dz/UniFAR-FTUs huggingface dataset:
Word Order and World Knowledge

Word order is an important concept in natural language, and in this work, we study how word order affects the induction of world knowledge from raw text using language models. We use word analogies to probe for such knowledge. Specifically, in addition to the natural word order, we first respectively extract texts of six fixed word orders from five languages and then pretrain the language models on these texts. Finally, we analyze the experimental results of the fixed word orders on word analogies and show that i) certain fixed word orders consistently outperform or underperform others, though the specifics vary across languages, and ii) the Wov2Lex hypothesis is not hold in pre-trained language models, and the natural word order typically yields mediocre results. The source code will be made publicly available at https://github.com/lshowway/probing_by_analogy.
Path Tracking using Echoes in an Unknown Environment: the Issue of Symmetries and How to Break Them

This paper deals with the problem of reconstructing the path of a vehicle in an unknown environment consisting of planar structures using sound. Many systems in the literature do this by using a loudspeaker and microphones mounted on a vehicle. Symmetries in the environment lead to solution ambiguities for such systems. We propose to resolve this issue by placing the loudspeaker at a fixed location in the environment rather than on the vehicle. The question of whether this will remove ambiguities regardless of the environment geometry leads to a question about breaking symmetries that can be phrased in purely mathematical terms. We solve this question in the affirmative if the geometry is in dimension three or bigger, and give counterexamples in dimension two. Excluding the rare situations where the counterexamples arise, we also give an affirmative answer in dimension two. Our results lead to a simple path reconstruction algorithm for a vehicle carrying four microphones navigating within an environment in which a loudspeaker at a fixed position emits short bursts of sounds. This algorithm could be combined with other methods from the literature to construct a path tracking system for vehicles navigating within a potentially symmetric environment.
SceneCraft: An LLM Agent for Synthesizing 3D Scene as Blender Code

This paper introduces SceneCraft, a Large Language Model (LLM) Agent converting text descriptions into Blender-executable Python scripts which render complex scenes with up to a hundred 3D assets. This process requires complex spatial planning and arrangement. We tackle these challenges through a combination of advanced abstraction, strategic planning, and library learning. SceneCraft first models a scene graph as a blueprint, detailing the spatial relationships among assets in the scene. SceneCraft then writes Python scripts based on this graph, translating relationships into numerical constraints for asset layout. Next, SceneCraft leverages the perceptual strengths of vision-language foundation models like GPT-V to analyze rendered images and iteratively refine the scene. On top of this process, SceneCraft features a library learning mechanism that compiles common script functions into a reusable library, facilitating continuous self-improvement without expensive LLM parameter tuning. Our evaluation demonstrates that SceneCraft surpasses existing LLM-based agents in rendering complex scenes, as shown by its adherence to constraints and favorable human assessments. We also showcase the broader application potential of SceneCraft by reconstructing detailed 3D scenes from the Sintel movie and guiding a video generative model with generated scenes as intermediary control signal.
Characterization of Wireless Channel Semantics: A New Paradigm

Recently, deep learning enabled semantic communications have been developed to understand transmission content from semantic level, which realize effective and accurate information transfer. Aiming to the vision of sixth generation (6G) networks, wireless devices are expected to have native perception and intelligent capabilities, which associate wireless channel with surrounding environments from physical propagation dimension to semantic information dimension. Inspired by these, we aim to provide a new paradigm on wireless channel from semantic level. A channel semantic model and its characterization framework are proposed in this paper. Specifically, a channel semantic model composes of status semantics, behavior semantics and event semantics. Based on actual channel measurement at 28 GHz, as well as multi-mode data, example results of channel semantic characterization are provided and analyzed, which exhibits reasonable and interpretable semantic information.
Understanding Energy Level Structure Using Quantum Rubik's Cube

This study combines the quantum Rubik's Cube matrix with the Benalcazar Bernevig Hughes model, defines a matrix algorithm based on the reverse process of convolution, and constructs an expression for the quantum Rubik's Cube matrix and Hamiltonian. Furthermore, in order to make the operation of the quantum Rubik's Cube matrix clearer, we use a Josephus ring to draw a topological graph of the Rubik's Cube expansion. This article uses a quantum Rubik's Cube to calculate energy level transitions of electrons, and shows that its operation corresponds to path integration. The band dispersion is obtained. This work provides new ideas and methods for calculating Hamiltonians and studying energy level structure.
AcME-AD: Accelerated Model Explanations for Anomaly Detection

Pursuing fast and robust interpretability in Anomaly Detection is crucial, especially due to its significance in practical applications. Traditional Anomaly Detection methods excel in outlier identification but are often black-boxes, providing scant insights into their decision-making process. This lack of transparency compromises their reliability and hampers their adoption in scenarios where comprehending the reasons behind anomaly detection is vital. At the same time, getting explanations quickly is paramount in practical scenarios. To bridge this gap, we present AcME-AD, a novel approach rooted in Explainable Artificial Intelligence principles, designed to clarify Anomaly Detection models for tabular data. AcME-AD transcends the constraints of model-specific or resource-heavy explainability techniques by delivering a model-agnostic, efficient solution for interoperability. It offers local feature importance scores and a what-if analysis tool, shedding light on the factors contributing to each anomaly, thus aiding root cause analysis and decision-making. This paper elucidates AcME-AD's foundation, its benefits over existing methods, and validates its effectiveness with tests on both synthetic and real datasets. AcME-AD's implementation and experiment replication code is accessible in a public repository.
Theoretical Prediction of Low-Energy Experimental Photoelectron Spectra of Al$_n$Ni$^{-}$ Clusters (n=1-13)

Context Mixed-metal clusters have long been studied because of their peculiar properties and how they change with cluster size, composition and charge state and their potential roles in catalysis. The characterization of these clusters is therefore a very important issue. One of the main experimental tools for characterizing their electronic structure is photoelectron spectroscopy. Theoretical computation completes the task by fully determining the structural properties and matching the theoretical predictions to the measured spectra. We present density functional theory computations of the structural, magnetic and electronic properties of negatively charged mixed Al$_{n}$Ni$^{-}$ clusters with up to 13 Al atoms. The lowest energy structures of the anionic clusters with up to 7 atoms are also found to be low energy isomers of the neutral counterparts found in the literature. The 13-atom cluster is found to be a quartet and a perfect icosahedron. The predicted photoelectron spectra are also presented and can be used to interpret future experimental data. We also presented indicators that can be used to determine the potential of these systems for single atom catalysis. These indicators point to smaller clusters to be more reactive as the gap between the Fermi energy and the center of the d-band increases with cluster size and that Ni occupy and internal site for n=11-13. We speculate that reactivity can be enhanced if one adds an additional Ni atom. Methods The DFT calculations were performed using the Becke exchange and Perdew-Wang/91 correlation functionals (BPW91), a DFT-optimized all-electron basis set for the aluminum atom and the Stuttgart small core pseudopotential for the Ni atom. All of the computations used the Gaussian 03 software.
Interseismic Strain Accumulation on Faults Beneath Los Angeles, California

Geodetic data show that the Los Angeles metropolitan area is undergoing 8–9 mm/year of north‐south tectonic shortening associated with the Big Bend of the San Andreas Fault. This shortening has been linked to multiple damaging twentieth century thrust earthquakes as well as possible Mw ≥ 7.0 Holocene thrust events beneath central Los Angeles. To better characterize this seismic hazard, we assess how this shortening is being accommodated by interseismic strain accumulation on subsurface faults, incorporating detailed seismology‐ and geology‐based models of fault geometry and the low‐stiffness Los Angeles sedimentary basin. We find that strain accumulation on local strike‐slip faults likely contributes no more than 1–2 mm/year of the shortening. We formally invert the geodetic data for the pattern of interseismic strain accumulation on the north dipping Sierra Madre, Puente Hills, and Compton thrust faults and a master decollement. We explore the impact of the assumed material model, strain accumulation on faults to the west and east, and other model assumptions. We infer that the three faults slip at 3–4 mm/year over the long term and are currently partially or fully locked and accruing interseismic strain on their upper sections. This locking implies an annual deficit of seismic moment, 1.6 + 1.3/−0.5 × 1017 Nm/year in total, which is presumably balanced over the long‐term average by the moment released in earthquakes. The depth distribution of moment deficit accumulation rate matches that of seismicity rates in Los Angeles to first order, in part, because the models incorporate the blind nature of the Puente Hills and Compton Faults.
Margin Discrepancy-based Adversarial Training for Multi-Domain Text Classification

Multi-domain text classification (MDTC) endeavors to harness available resources from correlated domains to enhance the classification accuracy of the target domain. Presently, most MDTC approaches that embrace adversarial training and the shared-private paradigm exhibit cutting-edge performance. Unfortunately, these methods face a non-negligible challenge: the absence of theoretical guarantees in the design of MDTC algorithms. The dearth of theoretical underpinning poses a substantial impediment to the advancement of MDTC algorithms. To tackle this problem, we first provide a theoretical analysis of MDTC by decomposing the MDTC task into multiple domain adaptation tasks. We incorporate the margin discrepancy as the measure of domain divergence and establish a new generalization bound based on Rademacher complexity. Subsequently, we propose a margin discrepancy-based adversarial training (MDAT) approach for MDTC, in accordance with our theoretical analysis. To validate the efficacy of the proposed MDAT method, we conduct empirical studies on two MDTC benchmarks. The experimental results demonstrate that our MDAT approach surpasses state-of-the-art baselines on both datasets.
DyPyBench: A Benchmark of Executable Python Software

Python has emerged as one of the most popular programming languages, extensively utilized in domains such as machine learning, data analysis, and web applications. Python's dynamic nature and extensive usage make it an attractive candidate for dynamic program analysis. However, unlike for other popular languages, there currently is no comprehensive benchmark suite of executable Python projects, which hinders the development of dynamic analyses. This work addresses this gap by presenting DyPyBench, the first benchmark of Python projects that is large scale, diverse, ready to run (i.e., with fully configured and prepared test suites), and ready to analyze (by integrating with the DynaPyt dynamic analysis framework). The benchmark encompasses 50 popular opensource projects from various application domains, with a total of 681k lines of Python code, and 30k test cases. DyPyBench enables various applications in testing and dynamic analysis, of which we explore three in this work: (i) Gathering dynamic call graphs and empirically comparing them to statically computed call graphs, which exposes and quantifies limitations of existing call graph construction techniques for Python. (ii) Using DyPyBench to build a training data set for LExecutor, a neural model that learns to predict values that otherwise would be missing at runtime. (iii) Using dynamically gathered execution traces to mine API usage specifications, which establishes a baseline for future work on specification mining for Python. We envision DyPyBench to provide a basis for other dynamic analyses and for studying the runtime behavior of Python code.
Dynamic Operational Planning in Warfare: A Stochastic Game Approach to Military Campaigns

We study a two-player discounted zero-sum stochastic game model for dynamic operational planning in military campaigns. At each stage, the players manage multiple commanders who order military actions on objectives that have an open line of control. When a battle over the control of an objective occurs, its stochastic outcome depends on the actions and the enabling support provided by the control of other objectives. Each player aims to maximize the cumulative number of objectives they control, weighted by their criticality. To solve this large-scale stochastic game, we derive properties of its Markov perfect equilibria by leveraging the logistics and military operational command and control structure. We show the consequential isotonicity of the optimal value function with respect to the partially ordered state space, which in turn leads to a significant reduction of the state and action spaces. We also accelerate Shapley's value iteration algorithm by eliminating dominated actions and investigating pure equilibria of the matrix game solved at each iteration. We demonstrate the computational value of our equilibrium results on a case study that reflects representative operational-level military campaigns with geopolitical implications. Our analysis reveals a complex interplay between the game's parameters and dynamics in equilibrium, resulting in new military insights for campaign analysts.
Mutation Analysis with Execution Taints

Mutation analysis is one of the most effective, but costly means of assessing the ability of software test suites to prevent bugs. Traditional mutation analysis involves producing and evaluating syntactic variants of the original to check whether the test suite under evaluation is capable of distinguishing between the variant and the original in terms of behavior. Evaluating each mutant separately means a large amount of redundant computation, both between the original program and mutants, and also between different mutants. Previous work explored numerous means of removing redundancy. However, some amount of redundancy has remained especially in the post-mutation phase. In this paper, we propose execution taints--A novel technique that repurposes dynamic data-flow taints for mutation analysis. Our technique is the only technique that can remove the redundancy in post-mutation phase, achieving better efficiency in mutation analysis. We further leverage memoization to eliminate redundant execution between program variants.
FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked RDMA Transmission

Communication overhead is a significant bottleneck in federated learning (FL), which has been exaggerated with the increasing size of AI models. In this paper, we propose FedRDMA, a communication-efficient cross-silo FL system that integrates RDMA into the FL communication protocol. To overcome the limitations of RDMA in wide-area networks (WANs), FedRDMA divides the updated model into chunks and designs a series of optimization techniques to improve the efficiency and robustness of RDMA-based communication. We implement FedRDMA atop the industrial federated learning framework and evaluate it on a real-world cross-silo FL scenario. The experimental results show that \sys can achieve up to 3.8$\times$ speedup in communication efficiency compared to traditional TCP/IP-based FL systems.
Thermoelectric Transport in Weyl Semimetal BaMnSb2: a First-Principles Study

Topological materials are often associated with exceptional thermoelectric properties. Orthorhombic BaMnSb2 is a topological semimetal consisting of alternating layers of Ba, Sb, and MnSb. A recent experiment demonstrates that BaMnSb2 has a low thermal conductivity and modest thermopower, promising as a thermoelectric material. Through first-principles calculations with Coulomb repulsion and spin-orbit coupling included, we studied the electronic structure, phononic structure, and thermoelectric transport properties of BaMnSb2 in depth. We find that BaMnSb2 exhibits a low lattice thermal conductivity, owing to the scattering of the acoustic phonons with low-frequency optical modes. Using the linearized Boltzmann transport theory with a constant relaxation time approximation, the thermopower is further calculated and an intriguing goniopolar transport behavior, which is associated with both n-type and p-type conduction along separate transport directions simultaneously, is observed. We propose that the figure of merit can be enhanced via doping in which electrical conductivity is decreased while the thermopower remains undiminished. BaMnSb2 is a potential platform for elucidating complex band structure effects and topological phenomena, paving the way to explore rich physics in low-dimensional systems.
N-representable one-electron reduced density matrix reconstruction with frozen core electrons.

Recent advances in quantum crystallography have shown that, beyond conventional charge density refinement, a one-electron reduced density matrix (1-RDM) satisfying N-representability conditions can be reconstructed using jointly experimental X-ray structure factors and directional Compton profiles (DCP) through semidefinite programming. So far, such reconstruction methods for 1-RDM, not constrained to idempotency, have been tested only on a toy model system (CO2). In this work, a new method is assessed on crystalline urea [CO(NH2)2] using static (0 K) and dynamic (50 K) artificial experimental data. An improved model, including symmetry constraints and frozen core-electron contribution, is introduced to better handle the increasing system complexity. Reconstructed 1-RDMs, deformation densities and DCP anisotropy are analysed, and it is demonstrated that the changes in the model significantly improve the reconstruction quality, even when there is insufficient information and data corruption. The robustness of the model and the strategy are thus shown to be well adapted to address the reconstruction problem from actual experimental scattering data.
The Probability to Hit Every Bin with a Linear Number of Balls

Assume that $2n$ balls are thrown independently and uniformly at random into $n$ bins. We consider the unlikely event $E$ that every bin receives at least one ball, showing that $\Pr[E] = \Theta(b^n)$ where $b \approx 0.836$. Note that, due to correlations, $b$ is not simply the probability that any single bin receives at least one ball. More generally, we consider the event that throwing $\alpha n$ balls into $n$ bins results in at least $d$ balls in each bin.
Macroscale brain states support the control of semantic cognition

Understanding how the human brain adapts to varying cognitive demands is crucial in neuroscience. Here, we examined how networks involved in controlled semantic retrieval reconfigure themselves to generate neurocognitive states appropriate to different task contexts. We parametrically varied the demands of two semantic tasks - global association and feature matching judgments - and contrasted these effects of cognitive control with those of non-semantic tasks. We then characterized these effects on the cortical surface and within a whole-brain state space, anchored by the top three dimensions of intrinsic connectivity. Our results revealed that demanding semantic association tasks elicited more activation in the anterior regions of the prefrontal and temporal cortex. In contrast, difficult semantic feature matching tasks produced more posterior activation, aligning closely with regions engaged during multiple demanding non-semantic tasks. In both semantic feature matching and non-semantic contexts, the difficulty effects were situated towards the controlled end of a dimension capturing functional separation between cognitive control and default mode regions. Conversely, in semantic association tasks, the difficulty effects elicited similar responses across both cognitive control and default mode networks. Furthermore, controlled association and non-semantic control were located towards the heteromodal end of a heteromodal- unimodal dimension, while semantic feature matching involved a brain state that was more visual and unimodal. These findings demonstrate that a variety of brain states underpin controlled cognition. Specifically, cognitive control regions interact with heteromodal semantic knowledge system to identify contextually relevant conceptual overlaps (e.g., associating ’DOG’ with ’BEACH’), and separate from heteromodal memory regions for modality-specific conceptual overlaps (e.g., connecting ’DALMATIAN’ with ’BLACK AND WHITE’).
Comparison of monomeric variants of StayGold

StayGold is a bright and highly photostable fluorescent protein (FP) that forms an obligate dimer, thereby limiting its application as a soluble marker. On the basis of the structural information of this FP, we disrupted the dimerization to generate a monomeric variant, mStayGold, which inherits both the extremely high photostability and the high practical brightness of StayGold, for molecular fusion and membrane-targeting applications. Meanwhile, two other research groups have independently monomerized StayGold using different strategies. As a result, multiple StayGold monomers are currently available, creating confusion in the research community. In the present study, we investigated the three basic properties—photostability, brightness, and dispersibility—of these monomers by performing detailed side-by-side comparisons. This study highlights the difficulties of StayGold monomerization with emphasis on the tradeoff between photostability and brightness in FP technology.
A platform for the recombinant production of Group A Streptococcus glycoconjugate vaccines

Strep A is a human-exclusive bacterial pathogen killing annually more than 500,000 patients, and no current licensed vaccine exists. Strep A bacteria are highly diverse, but all produce an essential, abundant, and conserved surface carbohydrate, the Group A Carbohydrate, which contains a rhamnose polysaccharide (RhaPS) backbone. RhaPS is a validated universal vaccine candidate in a glycoconjugate prepared by chemical conjugation of the native carbohydrate to a carrier protein. We engineered the Group A Carbohydratte biosynthesis pathway to enable recombinant production using the industry standard route to couple RhaPS to selected carrier proteins within E. coli cells. The structural integrity of the produced recombinant glycoconjugate vaccines was confirmed by NMR spectroscopy and mass spectrometry. Purified RhaPS glycoconjugates elicited carbohydrate-specific antibodies in mice and rabbits and bound to the surface of multiple Strep A strains of diverse M-types, confirming the recombinantly produced RhaPS glycoconjugates as valuable vaccine candidates.