Miracle-dz/UniFAR-FTUs huggingface dataset:
pCO2 and CO2 fluxes of the metropolitan river network in relation to the urbanization of Chongqing, China

Most rivers of the world are supersaturated with carbon dioxide (CO2), resulting in a large gas flux that has only recently been included in global carbon budgeting. However, little is known about CO2 emissions from urban river networks suffering anthropogenic disturbance in the context of global urbanization. We surveyed the partial pressure of carbon dioxide (pCO2) and CO2 flux from 84 locations in the Chongqing metropolitan river network, with an area of 5494 km2. The overall mean pCO2 and CO2 fluxes were 2152 µatm and 163.0 mol C m−2 yr−1, respectively, with 318.9 mol C m−2 yr−1 from rivers in the completely urban area and 49.6 mol C m−2 yr−1 from the least urbanized area. The riverine pCO2 level increased with the proportion of urban land, with 2–4 times higher CO2 flux in the urban areas than the remote rural ones. Sites with low flow velocity, narrow channel, and factitious bottom sediment appeared to be local hot spots of CO2 emission. The pCO2 and CO2 degassing was positively correlated with the nutrients content of surface water (i.e., nitrogen and phosphorus). Carbon was released to the atmosphere as CO2 from Chongqing metropolitan river network at a rate of 12.3 × 109 mol C yr−1. pCO2 exhibited a clear seasonality with lower values in March 2015 and June 2015 but a higher value in September 2014, which was coregulated by temperature, flood dilution, and in situ photosynthesis. The results highlight that rapid urbanization, with increasing nutrients loading and anthropogenic activities, will alter the carbon biogeochemical cycle in the terrestrial‐aqueous‐atmospheric ecosystem and then impact global CO2 budgets.
The Open Journal of Occupational Therapy The Open Journal of Occupational Therapy

Abstract Background: Executive functions are higher order cognitive processes occurring in the frontal lobe that influence cognitive, emotional, and behavioral functions. Adolescents with executive functioning deficits are at risk for difficulties in all domains of occupational functioning. Parents of these adolescents often live in a persistent state of stress that leads to highly reactive exchanges with their children. Studies have shown that a mindful approach to parenting can enhance a parent’s caregiving ability and self-awareness in the family unit. Methods: A pretest/posttest study evaluated the effectiveness of a 6-week mindful parenting program. Four parents of adolescents with executive function deficits participated in six consecutive group sessions for 1.5 hr each, one time per week, to learn mindful strategies. Results: Although most results were not found to be statistically significant, findings demonstrated promising trends for three of the parents. Statistically significant results indicated that one parent experienced improved communication with his or her adolescent, two had fewer concerns at school for their adolescent, three showed increased ability to problem-solve, one decreased his or her perfectionistic parenting skills, and one was more likely to be in the middle between the other parent and their adolescent. Conclusions: Support for parents after program conclusion may be necessary to promote lasting change. Further research is needed with larger groups and longer periods to determine the effectiveness of mindfulness programs for parents with adolescents with executive functioning deficits.
Seismic horizon detection using image processing algorithms

Seismic sections used in interpretation are actually images. We often superimpose colour-coded pictures of seismic attributes on seismic sections. Thus, it seems straightforward to use image processing algorithms to enhance the quality of the seismic images. From an image processing point of view, seismic horizons can be thought of as edges on the seismic image. We present a novel approach to detecting seismic horizons, which includes the 2D median filtering of the instantaneous phase attribute and applying an edge detection algorithm.
Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings Pre-trained Estimator

Unstructured text provides decision-makers with a rich data source in many domains, ranging from product reviews in retailing to nursing notes in healthcare. To leverage this information, words are typically translated into word embeddings —vectors that encode the semantic relationships between words—through unsupervised learning algorithms such as matrix factorization. However, learning word embeddings from new domains with limited training data can be challenging, because the meaning/usage may be diﬀerent in the new domain, e.g., the word “positive” typically has positive sentiment, but often has negative sentiment in medical notes since it may imply that a patient is tested positive for a disease. Intuitively, we expect that only a small number of domain-speciﬁc words may have new meanings/usages. We propose an intuitive two-stage estimator that exploits this structure via a group-sparse penalty to eﬃciently transfer learn domain-speciﬁc word embeddings by combining large-scale text corpora (such as Wikipedia) with limited domain-speciﬁc text data. We bound the generalization error of our estimator, proving that it can achieve the same accuracy (compared to not transfer learning) with substantially less domain-speciﬁc data when only a small number of embeddings are altered between domains. Our results provide the ﬁrst bounds on group-sparse matrix factorization, which may be of independent interest. We empirically evaluate the eﬀectiveness of our approach compared to state-of-the-art ﬁne-tuning heuristics from natural language processing.
A 95 GeV light Higgs in the top-pair-associated diphoton channel at the LHC in the Minial Dilaton Model

Motivated by experimental hints and theoretical frameworks indicating the existence of an extended Higgs sector, we undertake a study to explore the feasibility of detecting a 95 GeV light Higgs boson decaying into diphoton within the Minimal Dilaton Model (MDM) at the 14 TeV LHC. Initially, we identify the correlations between the production cross section, decay branching ratios, and model parameters, e.g., the scalar-mixing angle $\sin\theta_S$. Subsequently, we utilize Monte Carlo simulations to generate the signal of the light Higgs boson via the process $pp \to t\bar{t}(s\to \gamma\gamma)$, along with the corresponding backgrounds. To effectively separate the signal from the dominant $tt\gamma\gamma$ backgrounds, we employ a meticulous cut-based selection process. Ultimately, we find that with an integrated luminosity of $L = 3000 {~\rm fb}^{-1}$, the regions of $|\sin\theta_S|>0.2$ can be covered at over a $3\sigma$ level.
Absence of Bound States for Quantum Walks and CMV Matrices via Reflections

We give a criterion based on reflection symmetries in the spirit of Jitomirskaya--Simon to show absence of point spectrum for (split-step) quantum walks and Cantero--Moral--Vel\'azquez (CMV) matrices. To accomplish this, we use some ideas from a recent paper by the authors and their collaborators to implement suitable reflection symmetries for such operators. We give several applications. For instance, we deduce arithmetic delocalization in the phase for the unitary almost-Mathieu operator and singular continuous spectrum for generic CMV matrices generated by the Thue--Morse subshift.
Disorder-induced acoustic non-Hermitian skin effect

The interplay between disorder and topology leads to rich phenomena such as topological Anderson insulator phases, where disorder opens a topological bandgap and thereby enhances transport. With the recent advances in non-Hermitian topological physics, topological Anderson insulators have been generalized to systems with non-Hermitian disorder. However, achieving disorder-induced nontrivial point-gap topology, which is unique to non-Hermitian systems and is responsible for non-Hermitian skin effect, remains elusive. In this work, we experimentally realized non-Hermitian disorder-induced point-gap topology in a one-dimensional acoustic crystal. By controlling the gain factor of the amplifier in each basic unit, we obtained a series of disordered unidirectional hoppings with tunable strength. Using disordered acoustic crystals under open boundary conditions, we observed the emergence of non-Hermitian skin effect and the direction reversal of skin mode localization when the disorder strength is tuned. These results serve as hallmarks of point-gap topological phase transition induced by non-Hermitian disorder. Our work paves the way for exploring various disorder-induced phenomena in non-Hermitian acoustic systems.
Advancing Translation Preference Modeling with RLHF: A Step Towards Cost-Effective Solution

Faithfulness, expressiveness, and elegance is the constant pursuit in machine translation. However, traditional metrics like \textit{BLEU} do not strictly align with human preference of translation quality. In this paper, we explore leveraging reinforcement learning with human feedback (\textit{RLHF}) to improve translation quality. It is non-trivial to collect a large high-quality dataset of human comparisons between translations, especially for low-resource languages. To address this issue, we propose a cost-effective preference learning strategy, optimizing reward models by distinguishing between human and machine translations. In this manner, the reward model learns the deficiencies of machine translation compared to human and guides subsequent improvements in machine translation. Experimental results demonstrate that \textit{RLHF} can effectively enhance translation quality and this improvement benefits other translation directions not trained with \textit{RLHF}. Further analysis indicates that the model's language capabilities play a crucial role in preference learning. A reward model with strong language capabilities can more sensitively learn the subtle differences in translation quality and align better with real human translation preferences.
Discrete Neural Algorithmic Reasoning

Neural algorithmic reasoning aims to capture computations with neural networks via learning the models to imitate the execution of classical algorithms. While common architectures are expressive enough to contain the correct model in the weights space, current neural reasoners are struggling to generalize well on out-of-distribution data. On the other hand, classical computations are not affected by distribution shifts as they can be described as transitions between discrete computational states. In this work, we propose to force neural reasoners to maintain the execution trajectory as a combination of finite predefined states. Trained with supervision on the algorithm's state transitions, such models are able to perfectly align with the original algorithm. To show this, we evaluate our approach on the SALSA-CLRS benchmark, where we get perfect test scores for all tasks. Moreover, the proposed architectural choice allows us to prove the correctness of the learned algorithms for any test data.
Probabilistic Routing for Graph-Based Approximate Nearest Neighbor Search

Approximate nearest neighbor search (ANNS) in high-dimensional spaces is a pivotal challenge in the field of machine learning. In recent years, graph-based methods have emerged as the superior approach to ANNS, establishing a new state of the art. Although various optimizations for graph-based ANNS have been introduced, they predominantly rely on heuristic methods that lack formal theoretical backing. This paper aims to enhance routing within graph-based ANNS by introducing a method that offers a probabilistic guarantee when exploring a node's neighbors in the graph. We formulate the problem as probabilistic routing and develop two baseline strategies by incorporating locality-sensitive techniques. Subsequently, we introduce PEOs, a novel approach that efficiently identifies which neighbors in the graph should be considered for exact distance computation, thus significantly improving efficiency in practice. Our experiments demonstrate that equipping PEOs can increase throughput on a commonly utilized graph index (HNSW) by a factor of 1.6 to 2.5, and its efficiency consistently outperforms the leading-edge routing technique by 1.1 to 1.4 times.
The AI Security Pyramid of Pain

We introduce the AI Security Pyramid of Pain, a framework that adapts the cybersecurity Pyramid of Pain to categorize and prioritize AI-specific threats. This framework provides a structured approach to understanding and addressing various levels of AI threats. Starting at the base, the pyramid emphasizes Data Integrity, which is essential for the accuracy and reliability of datasets and AI models, including their weights and parameters. Ensuring data integrity is crucial, as it underpins the effectiveness of all AI-driven decisions and operations. The next level, AI System Performance, focuses on MLOps-driven metrics such as model drift, accuracy, and false positive rates. These metrics are crucial for detecting potential security breaches, allowing for early intervention and maintenance of AI system integrity. Advancing further, the pyramid addresses the threat posed by Adversarial Tools, identifying and neutralizing tools used by adversaries to target AI systems. This layer is key to staying ahead of evolving attack methodologies. At the Adversarial Input layer, the framework addresses the detection and mitigation of inputs designed to deceive or exploit AI models. This includes techniques like adversarial patterns and prompt injection attacks, which are increasingly used in sophisticated attacks on AI systems. Data Provenance is the next critical layer, ensuring the authenticity and lineage of data and models. This layer is pivotal in preventing the use of compromised or biased data in AI systems. At the apex is the tactics, techniques, and procedures (TTPs) layer, dealing with the most complex and challenging aspects of AI security. This involves a deep understanding and strategic approach to counter advanced AI-targeted attacks, requiring comprehensive knowledge and planning.
DIO: Dataset of 3D Mesh Models of Indoor Objects for Robotics and Computer Vision Applications

The creation of accurate virtual models of real-world objects is imperative to robotic simulations and applications such as computer vision, artificial intelligence, and machine learning. This paper documents the different methods employed for generating a database of mesh models of real-world objects. These methods address the tedious and time-intensive process of manually generating the models using CAD software. Essentially, DSLR/phone cameras were employed to acquire images of target objects. These images were processed using a photogrammetry software known as Meshroom to generate a dense surface reconstruction of the scene. The result produced by Meshroom was edited and simplified using MeshLab, a mesh-editing software to produce the final model. Based on the obtained models, this process was effective in modelling the geometry and texture of real-world objects with high fidelity. An active 3D scanner was also utilized to accelerate the process for large objects. All generated models and captured images are made available on the website of the project.
A Systematic Comparison of Contextualized Word Embeddings for Lexical Semantic Change

Contextualized embeddings are the preferred tool for modeling Lexical Semantic Change (LSC). Current evaluations typically focus on a specific task known as Graded Change Detection (GCD). However, performance comparison across work are often misleading due to their reliance on diverse settings. In this paper, we evaluate state-of-the-art models and approaches for GCD under equal conditions. We further break the LSC problem into Word-in-Context (WiC) and Word Sense Induction (WSI) tasks, and compare models across these different levels. Our evaluation is performed across different languages on eight available benchmarks for LSC, and shows that (i) APD outperforms other approaches for GCD; (ii) XL-LEXEME outperforms other contextualized models for WiC, WSI, and GCD, while being comparable to GPT-4; (iii) there is a clear need for improving the modeling of word meanings, as well as focus on how, when, and why these meanings change, rather than solely focusing on the extent of semantic change.
PDM relativistic quantum oscillator in Einstein-Maxwell-Lambda space-time

In this analysis, we study the dynamics of quantum oscillator fields within the context of a position-dependent mass (PDM) system situated in an Einstein-Maxwell space-time, incorporating a non-zero cosmological constant. The magnetic field is aligned along the symmetry axis direction. To analyze PDM quantum oscillator fields, we introduce a modification to the Klein-Gordon equation by substituting the four-momentum vector $p_{\mu} \to \Big(p_{\mu}+i\,\eta\,X_{\mu}+i\,\mathcal{F}_{\mu}\Big)$ into the Klein-Gordon equation, where the four-vector is defibed by $X_{\mu}=(0, r, 0, 0)$, $\mathcal{F}_{\mu}=(0, \mathcal{F}_r, 0, 0)$ with $\mathcal{F}_r=\frac{f'(r)}{4\,f(r)}$, and $\eta$ is the mass oscillator frequency. The radial wave equation for the relativistic modified Klein-Gordon equation is derived and subsequently solved for two distinct cases: (i) $f(r)=e^{\frac{1}{2}\,\alpha\,r^2}$, and (ii) $f(r)=r^{\beta}$, where $\alpha \geq 0, \beta \geq 0$. The resultant energy levels and wave functions for quantum oscillator fields are demonstrated to be influenced by both the cosmological constant and the geometrical topology parameter which breaks the degeneracy of the energy spectrum. Furthermore, we observed noteworthy modifications in the energy levels and wave functions when compared to the results derived in the flat space background.
Design and evaluation of a multi-finger skin-stretch tactile interface for hand rehabilitation robots

Object properties perceived through the tactile sense, such as weight, friction, and slip, greatly influence motor control during manipulation tasks. However, the provision of tactile information during robotic training in neurorehabilitation has not been well explored. Therefore, we designed and evaluated a tactile interface based on a two-degrees-of-freedom moving platform mounted on a hand rehabilitation robot that provides skin stretch at four fingertips, from the index through the little finger. To accurately control the rendered forces, we included a custom magnetic-based force sensor to control the tactile interface in a closed loop. The technical evaluation showed that our custom force sensor achieved measurable shear forces of +-8N with accuracies of 95.2-98.4% influenced by hysteresis, viscoelastic creep, and torsional deformation. The tactile interface accurately rendered forces with a step response steady-state accuracy of 97.5-99.4% and a frequency response in the range of most activities of daily living. Our sensor showed the highest measurement-range-to-size ratio and comparable accuracy to sensors of its kind. These characteristics enabled the closed-loop force control of the tactile interface for precise rendering of multi-finger two-dimensional skin stretch. The proposed system is a first step towards more realistic and rich haptic feedback during robotic sensorimotor rehabilitation, potentially improving therapy outcomes.
Second Order Meanfield Approximation for calculating Dynamics in Au-Nanoparticle Networks

Exploiting physical processes for fast and energy-efficient computation bears great potential in the advancement of modern hardware components. This paper explores non-linear charge tunneling in nanoparticle networks, controlled by external voltages. The dynamics are described by a master equation, which describes the development of a distribution function over the set of charge occupation numbers. The driving force behind this evolution are charge tunneling events among nanoparticles and their associated rates. In this paper, we introduce two meanfield approximations to this master equation. By parametrization of the distribution function using its first- and second-order statistical moments, and a subsequent projection of the dynamics onto the resulting moment manifold, one can deterministically calculate expected charges and currents. Unlike a kinetic Monte Carlo approach, which extracts samples from the distribution function, this meanfield approach avoids any random elements. A comparison of results between the meanfield approximation and an already available kinetic Monte Carlo simulation demonstrates great accuracy. Our analysis also reveals that transitioning from a first-order to a second-order approximation significantly enhances the accuracy. Furthermore, we demonstrate the applicability of our approach to time-dependent simulations, using eulerian time-integration schemes.
Training Bayesian Neural Networks with Sparse Subspace Variational Inference

Bayesian neural networks (BNNs) offer uncertainty quantification but come with the downside of substantially increased training and inference costs. Sparse BNNs have been investigated for efficient inference, typically by either slowly introducing sparsity throughout the training or by post-training compression of dense BNNs. The dilemma of how to cut down massive training costs remains, particularly given the requirement to learn about the uncertainty. To solve this challenge, we introduce Sparse Subspace Variational Inference (SSVI), the first fully sparse BNN framework that maintains a consistently highly sparse Bayesian model throughout the training and inference phases. Starting from a randomly initialized low-dimensional sparse subspace, our approach alternately optimizes the sparse subspace basis selection and its associated parameters. While basis selection is characterized as a non-differentiable problem, we approximate the optimal solution with a removal-and-addition strategy, guided by novel criteria based on weight distribution statistics. Our extensive experiments show that SSVI sets new benchmarks in crafting sparse BNNs, achieving, for instance, a 10-20x compression in model size with under 3\% performance drop, and up to 20x FLOPs reduction during training compared with dense VI training. Remarkably, SSVI also demonstrates enhanced robustness to hyperparameters, reducing the need for intricate tuning in VI and occasionally even surpassing VI-trained dense BNNs on both accuracy and uncertainty metrics.
All Language Models Large and Small

Many leading language models (LMs) use high-intensity computational resources both during training and execution. This poses the challenge of lowering resource costs for deployment and faster execution of decision-making tasks among others. We introduce a novel plug-and-play LM framework named Language Optimising Network Distribution (LONDI) framework. LONDI learns to selectively employ large LMs only where complex decision-making and reasoning are required while using low-resource LMs everywhere else. LONDI consists of a system of two (off-)policy networks, an LM, a large LM (LLM), and a reinforcement learning module that uses switching controls to quickly learn which system states to call the LLM. We then introduce a variant of LONDI that maintains budget constraints on LLM calls and hence its resource usage. Theoretically, we prove LONDI learns the subset of system states to activate the LLM required to solve the task. We then prove that LONDI converges to optimal solutions while also preserving budgetary constraints on LLM calls almost surely enabling it to solve various tasks while significantly lowering computational costs. We test LONDI's performance in a range of tasks in ScienceWorld and BabyAI-Text and demonstrate that LONDI can solve tasks only solvable by resource-intensive LLMs while reducing GPU usage by up to 30%.