Miracle-dz/UniFAR-FTUs huggingface dataset:
Seismic energy propagation in highly scattering environments and constraints on lunar interior structure from the scattered signals of the Apollo passive seismic experiment

Meteoroid impacts over hundreds of millions to billions of years can produce a highly fractured and heterogeneous megaregolith layer on planetary bodies such as the Moon that lack effective surface recycling mechanisms. The energy from seismic events occurring on these bodies undergoes scattering in the fractured layer(s) and this process generates extensive coda wave trains that follow major seismic wave arrivals. These long coda trains can obscure the secondary crustal, mantle or core phases that are often crucial in assessing the interior structure of these planetary bodies when using more traditional seismological analyses. However, the decay properties of these codas are affected by the interior velocity, intrinsic attenuation and scattering structure of the planet or moon. As such, these decay properties can contain valuable information regarding these aspects of interior structure. This thesis provides the first systematic analysis of scattering in the Apollo Passive Seismic Experiment dataset, demonstrating that scattering in the Moon occurs over a wide range of frequencies, and dominantly in the near-surface megaregolith that comprises many more small scale heterogeneities than large ones. I also present a new numerical modeling technique (referred to as PHONON1D) that models seismic energy propagation and integrates high levels of scattering. Using this method, I investigate the effects of various velocity, scattering and intrinsic attenuation structures on the scattered coda. Results show that the main controls on the coda generation and decay times are the seismic velocity profile, attenuation levels, and the number density of scatterers. Thus these properties can be assessed by comparing predicted synthetic seismic coda with those observed in the Apollo Passive Seismic Experiment data. Finally, I use the PHONON1D method to show that locations within young and large impact basins, away from the edges, have the potential to minimize the scattering observed in the recorded seismic signals. These locations would be ideal for the emplacement of future seismic surveys on the lunar surface.
Massive Editing for Large Language Models via Meta Learning

While large language models (LLMs) have enabled learning knowledge from the pre-training corpora, the acquired knowledge may be fundamentally incorrect or outdated over time, which necessitates rectifying the knowledge of the language model (LM) after the training. A promising approach involves employing a hyper-network to generate parameter shift, whereas existing hyper-networks suffer from inferior scalability in synchronous editing operation amount. To mitigate the problem, we propose the MAssive Language Model Editing Network (MALMEN), which formulates the parameter shift aggregation as the least square problem, subsequently updating the LM parameters using the normal equation. To accommodate editing multiple facts simultaneously with limited memory budgets, we separate the computation on the hyper-network and LM, enabling arbitrary batch size on both neural networks. Our method is evaluated by editing up to thousands of facts on LMs with different architectures, i.e., BERT-base, GPT-2, T5-XL (2.8B), and GPT-J (6B), across various knowledge-intensive NLP tasks, i.e., closed book fact-checking and question answering. Remarkably, MALMEN is capable of editing hundreds of times more facts than strong baselines with the identical hyper-network architecture and outperforms editor specifically designed for GPT. Our code is available at https://github.com/ChenmienTan/malmen.
Rethinking Event-based Human Pose Estimation with 3D Event Representations

Human pose estimation is a fundamental and appealing task in computer vision. Traditional frame-based cameras and videos are commonly applied, yet, they become less reliable in scenarios under high dynamic range or heavy motion blur. In contrast, event cameras offer a robust solution for navigating these challenging contexts. Predominant methodologies incorporate event cameras into learning frameworks by accumulating events into event frames. However, such methods tend to marginalize the intrinsic asynchronous and high temporal resolution characteristics of events. This disregard leads to a loss in essential temporal dimension data, crucial for discerning distinct actions. To address this issue and to unlock the 3D potential of event information, we introduce two 3D event representations: the Rasterized Event Point Cloud (RasEPC) and the Decoupled Event Voxel (DEV). The RasEPC collates events within concise temporal slices at identical positions, preserving 3D attributes with statistical cues and markedly mitigating memory and computational demands. Meanwhile, the DEV representation discretizes events into voxels and projects them across three orthogonal planes, utilizing decoupled event attention to retrieve 3D cues from the 2D planes. Furthermore, we develop and release EV-3DPW, a synthetic event-based dataset crafted to facilitate training and quantitative analysis in outdoor scenes. On the public real-world DHP19 dataset, our event point cloud technique excels in real-time mobile predictions, while the decoupled event voxel method achieves the highest accuracy. Experiments on EV-3DPW demonstrate that the robustness of our proposed 3D representation methods compared to traditional RGB images and event frame techniques under the same backbones. Our code and dataset have been made publicly available at https://github.com/MasterHow/EventPointPose.
21. LATEST QUATERNARY PALEOCLIMATIC AND RADIOCARBON CHRONOLOGY, HOLE 1017E, SOUTHERN CALIFORNIA MARGIN 1

Oxygen isotopic (δ 18 O) climatic stratigraphy and radiocarbon chronology, at high resolution, have been used to establish an age model for Ocean Drilling Program Hole 1017E, a continuous 25-m sequence of hemipelagic sediments from the continental slope (956 m water depth), east of Point Arguella, Southern California. The upper part of Hole 1017E from ~33 ka (7.445 mbsf) was dated using 13 calendar-corrected radiocarbon ages of mixed planktonic foraminiferal assemblages. Benthic oxygen isotopic stratigraphy records a continuous 130-k.y. sequence ranging from marine isotope Stage 6 to the present day. The benthic δ 18 O curve, representing the last two interglacial and glacial cycles, closely resembles the well-dated, deep-sea reference sequence, providing a detailed chronologic framework. Sedimentation rates remained relatively constant throughout the sequence at ~18 cm/k.y. and were sufficiently rapid to provide considerable potential for high-resolution paleoceanographic/ paleoclimatic investigations. Planktonic foraminiferal oxygen isotopic stratigraphy based on the surface-dwelling form Globigerina bulloides defines an almost complete sequence of interstadial/stadial oscillations (Dansgaard/Oeschger cycles [D/O]). Combined use of radiocarbon chronology, deep-sea oxygen isotopic datums, and visual pattern matching has enabled us to identify the sequence of D/O cycles as described for the Greenland (GRIP2) ice core. This has strengthened the stratigraphic framework for the last 60 k.y. in the sequence as a basis for further paleoenvironmental investigations.
InstrumentGen: Generating Sample-Based Musical Instruments From Text

We introduce the text-to-instrument task, which aims at generating sample-based musical instruments based on textual prompts. Accordingly, we propose InstrumentGen, a model that extends a text-prompted generative audio framework to condition on instrument family, source type, pitch (across an 88-key spectrum), velocity, and a joint text/audio embedding. Furthermore, we present a differentiable loss function to evaluate the intra-instrument timbral consistency of sample-based instruments. Our results establish a foundational text-to-instrument baseline, extending research in the domain of automatic sample-based instrument generation.
The high energy X-ray probe: resolved X-ray populations in extragalactic environments

We construct simulated galaxy data sets based on the High Energy X-ray Probe (HEX-P) mission concept to demonstrate the significant advances in galaxy science that will be yielded by the HEX-P observatory. The combination of high spatial resolution imaging (<20 arcsec FWHM), broad spectral coverage (0.2–80 keV), and sensitivity superior to current facilities (e.g., XMM-Newton and NuSTAR) will enable HEX-P to detect hard (4–25 keV) X-ray emission from resolved point-source populations within ∼800 galaxies and integrated emission from ∼6,000 galaxies out to 100 Mpc. These galaxies cover wide ranges of galaxy types (e.g., normal, starburst, and passive galaxies) and properties (e.g., metallicities and star-formation histories). In such galaxies, HEX-P will: 1) provide unique information about X-ray binary populations, including accretor demographics (black hole and neutron stars), distributions of accretion states and state transition cadences; 2) place order-of-magnitude more stringent constraints on inverse Compton emission associated with particle acceleration in starburst environments; and 3) put into clear context the contributions from X-ray emitting populations to both ionizing the surrounding interstellar medium in low-metallicity galaxies and heating the intergalactic medium in the z > 8 Universe.
Challenging Common Paradigms in Multi-Task Learning

While multi-task learning (MTL) has gained significant attention in recent years, its underlying mechanisms remain poorly understood. Recent methods did not yield consistent performance improvements over single task learning (STL) baselines, underscoring the importance of gaining more profound insights about challenges specific to MTL. In our study, we challenge paradigms in MTL in the context of STL: First, the impact of the choice of optimizer has only been mildly investigated in MTL. We show the pivotal role of common STL tools such as the Adam optimizer in MTL empirically in various experiments. To further investigate Adam's effectiveness, we theoretical derive a partial loss-scale invariance under mild assumptions. Second, the notion of gradient conflicts has often been phrased as a specific problem in MTL. We delve into the role of gradient conflicts in MTL and compare it to STL. For angular gradient alignment we find no evidence that this is a unique problem in MTL. We emphasize differences in gradient magnitude as the main distinguishing factor. Lastly, we compare the transferability of features learned through MTL and STL on common image corruptions, and find light evidence that MTL can lead to superior transferability. Overall, we find surprising similarities between STL and MTL suggesting to consider methods from both fields in a broader context.
Determination of toxic comments and unintended model bias minimization using Deep learning approach

Online conversations can be toxic and subjected to threats, abuse, or harassment. To identify toxic text comments, several deep learning and machine learning models have been proposed throughout the years. However, recent studies demonstrate that because of the imbalances in the training data, some models are more likely to show unintended biases including gender bias and identity bias. In this research, our aim is to detect toxic comment and reduce the unintended bias concerning identity features such as race, gender, sex, religion by fine-tuning an attention based model called BERT(Bidirectional Encoder Representation from Transformers). We apply weighted loss to address the issue of unbalanced data and compare the performance of a fine-tuned BERT model with a traditional Logistic Regression model in terms of classification and bias minimization. The Logistic Regression model with the TFIDF vectorizer achieve 57.1% accuracy, and fine-tuned BERT model's accuracy is 89%. Code is available at https://github.com/zim10/Determine_Toxic_comment_and_identity_bias. git
Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation

Recent advancements in Large Language Models (LLMs) have revolutionized decision-making by breaking down complex problems into more manageable language sequences referred to as"thoughts". An effective thought design should consider three key perspectives: performance, efficiency, and flexibility. However, existing thought can at most exhibit two of these attributes. To address these limitations, we introduce a novel thought prompting approach called"Everything of Thoughts"(XoT) to defy the law of"Penrose triangle of existing thought paradigms. XoT leverages pretrained reinforcement learning and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge into thoughts, thereby enhancing LLMs' capabilities and enabling them to generalize to unseen problems efficiently. Through the utilization of the MCTS-LLM collaborative thought revision framework, this approach autonomously produces high-quality comprehensive cognitive mappings with minimal LLM interactions. Additionally, XoT empowers LLMs to engage in unconstrained thinking, allowing for flexible cognitive mappings for problems with multiple solutions. We evaluate XoT on several challenging multi-solution problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our results demonstrate that XoT significantly outperforms existing approaches. Notably, XoT can yield multiple solutions with just one LLM call, showcasing its remarkable proficiency in addressing complex problems across diverse domains.
Local Differential Privacy for Smart Meter Data Sharing

Energy disaggregation techniques, which use smart meter data to infer appliance energy usage, can provide consumers and energy companies valuable insights into energy management. However, these techniques also present privacy risks, such as the potential for behavioral profiling. Local differential privacy (LDP) methods provide strong privacy guarantees with high efficiency in addressing privacy concerns. However, existing LDP methods focus on protecting aggregated energy consumption data rather than individual appliances. Furthermore, these methods do not consider the fact that smart meter data are a form of streaming data, and its processing methods should account for time windows. In this paper, we propose a novel LDP approach (named LDP-SmartEnergy) that utilizes randomized response techniques with sliding windows to facilitate the sharing of appliance-level energy consumption data over time while not revealing individual users' appliance usage patterns. Our evaluations show that LDP-SmartEnergy runs efficiently compared to baseline methods. The results also demonstrate that our solution strikes a balance between protecting privacy and maintaining the utility of data for effective analysis.
Analytical investigation of time-dependent two-dimensional non-Newtonian boundary layer equations

In this study, five different time-dependent incompressible non-Newtonian boundary layer models in two dimensions are investigated, including external magnetic field effects. The power law, the Casson fluid, the Oldroyd-B model, the Walter fluid B model and the Williamson fluid are analyzed. For the first two models, analytical results are given for the velocity and pressure distributions, which can be expressed by different types of hypergeometric functions. Depending on the parameters involved in the analytical solutions of the nonlinear ordinary differential equation obtained by the similarity transformation, a very wide range of solution types is presented.
TADA: Making Node-link Diagrams Accessible to Blind and Low-Vision People

Diagrams often appear as node-link representations in many contexts, such as taxonomies, mind maps and networks in textbooks. Despite their pervasiveness, they present significant accessibility challenges for blind and low-vision people. To address this challenge, we introduce Touch-and-Audio-based Diagram Access (TADA), a tablet-based interactive system that makes diagram exploration accessible through musical tones and speech. We designed and developed TADA informed by insights gained from an interview study with 15 participants who shared their challenges and strategies for accessing diagrams. TADA enables people to access a diagram by: i) engaging in open-ended touch-based explorations, ii) allowing searching of specific nodes, iii) navigating from one node to another and iv) filtering information. We evaluated TADA with 25 participants and found that it can be a useful tool for gaining different perspectives about the diagram and participants could complete several diagram-related tasks.
A pseudofermion functional renormalization group study of dipolar-octupolar pyrochlore magnets

Motivated by recent experiments on Ce$_2$Zr$_2$O$_7$ that reveal a dynamic, liquid-like ground state, we study the nearest neighbor XYZ Hamiltonian of dipolar-octupolar pyrochlore magnets with the pseudofermion functional renormalization group (PFFRG), which is numerically implemented by the SpinParser software. Taking the interaction between the octupolar components to be dominant and antiferromagnetic, we map out the phase diagram demarcating the quantum disordered and magnetically ordered states. We identify four distinct phases, namely the $0$-flux and $\pi$-flux quantum spin ices, and the all-in-all-out magnetic orders along the local $z$ and $x$ axes. We further use the static two-spin correlations output by the PFFRG algorithm to compute the polarized neutron scattering cross-sections, which are able to capture several qualitative features observed experimentally, in the materially relevant parameter regime that stabilizes the $\pi$-flux quantum spin ice. Our results provide support for a quantum spin liquid ground state in Ce$_2$Zr$_2$O$_7$.
Restoring symmetries in quantum computing using Classical Shadows

We introduce a method to enforce some symmetries starting from a trial wave-function prepared on quantum computers that might not respect these symmetries. The technique eliminates the necessity for performing the projection on the quantum computer itself. Instead, this task is conducted as a post-processing step on the system's"Classical Shadow". Illustrations of the approach are given for the parity, particle number, and spin projectors that are of particular interest in interacting many-body systems. We compare the method with another classical post-processing technique based on direct measurements of the quantum register. We show that the present scheme can be competitive to predict observables on symmetry-restored states once optimization through derandomization is employed. The technique is illustrated through its application to compute the projected energy for the pairing model Hamiltonian.
ToP-ToM: Trust-aware Robot Policy with Theory of Mind

Theory of Mind (ToM) is a fundamental cognitive architecture that endows humans with the ability to attribute mental states to others. Humans infer the desires, beliefs, and intentions of others by observing their behavior and, in turn, adjust their actions to facilitate better interpersonal communication and team collaboration. In this paper, we investigated trust-aware robot policy with the theory of mind in a multiagent setting where a human collaborates with a robot against another human opponent. We show that by only focusing on team performance, the robot may resort to the reverse psychology trick, which poses a significant threat to trust maintenance. The human's trust in the robot will collapse when they discover deceptive behavior by the robot. To mitigate this problem, we adopt the robot theory of mind model to infer the human's trust beliefs, including true belief and false belief (an essential element of ToM). We designed a dynamic trust-aware reward function based on different trust beliefs to guide the robot policy learning, which aims to balance between avoiding human trust collapse due to robot reverse psychology. The experimental results demonstrate the importance of the ToM-based robot policy for human-robot trust and the effectiveness of our robot ToM-based robot policy in multiagent interaction settings.
Characteristics of geographic distribution of four critically endangered species of Abies in subtropical China and its relationship with climate: Characteristics of geographic distribution of four critically endangered species of Abies in subtropical China and its relationship with climate

Aims Our objectives were to analyze the geographic distribution characteristics of four critically endangered species of Abies in China's subtropical regions, clarify their relationship with climatic conditions and determine the climatic factors that restrict the geographic distributions. Methods Geographic distribution information was collected from field survey and literature materials. Climate information was collected from China's meteorological stations and used to build a climate database of the distri- bution area. Commonly used climatic indexes and research methods were applied to analyze the relationship be- tween geographic distribution and climate. Restrictive climatic factors were determined based on factor analysis. Important findings Abies beshanzuensis, A. fanjingshanensis, A. yuanbaoshanensis and A. beshanzuensis var. ziyuanensis are scattered in the subtropical region of China. The horizontal range of distribution is narrow: 25°25'-27°55' N, 108°42'-119°12' E. The populations are distributed vertically in the upper part of mountains, with altitude ranging from 1 363 to 2 390 m. The uppermost altitude rises with decreasing longitude. Temperature of the entire distribution area is low, with mean annual temperature 7.4-11.2 °C. Precipitation is abundant, with a mean annual precipitation of 1 265-1 946 mm, of which 75% falls in the growing season. Humidity is high, and annual relative humidity reaches 77%-86%. The ranges of climatic indexes are narrow, indicating specific re- quirements for water and heat. The coldness index of the distribution area is low, limiting vertical diffusion to- wards low altitude. Therefore, the populations are confined in the upper part of mountains. The distribution areas of different species share similar climatic conditions. The temperature is relatively lower in the distribution area of A. fanjingshanensis. The humidity is slightly higher in the distribution area of A. yuanbaoshanensis. Three
MARIANOITE, A NEW MEMBER OF THE CUSPIDINE GROUP FROM THE PRAIRIE LAKE SILICOCARBONATITE, ONTARIO: DISCUSSION

In their paper entitled “Marianoite, a new member of the cuspidine group from the Prairie Lake silicocarbonatite, Ontario”, [Chakhmouradian et al. (2008)][1] presented chemical, crystallographic and structural data on what they claimed is a new mineral species in the cuspidine group. All the