Miracle-dz/UniFAR-FTUs huggingface dataset:
InterControl: Generate Human Motion Interactions by Controlling Every Joint

Text-conditioned human motion generation model has achieved great progress by introducing diffusion models and corresponding control signals. However, the interaction between humans are still under explored. To model interactions of arbitrary number of humans, we define interactions as human joint pairs that are either in contact or separated, and leverage Large Language Model (LLM) Planner to translate interaction descriptions into contact plans. Based on the contact plans, interaction generation could be achieved by spatially controllable motion generation methods by taking joint contacts as spatial conditions. We present a novel approach named InterControl for flexible spatial control of every joint in every person at any time by leveraging motion diffusion model only trained on single-person data. We incorporate a motion control-net to generate coherent and realistic motions given sparse spatial control signals and a loss guidance module to precisely align any joint to the desired position in a classifier guidance manner via Inverse Kinematics (IK). Extensive experiments on HumanML3D and KIT-ML dataset demonstrate its effectiveness in versatile joint control. We also collect data of joint contact pairs by LLMs to show Inter-Control’s ability in human interaction generation. Code is available at https://github.com/zhenzhiwang/ intercontrol .
Insights into Age-Related Functional Brain Changes during Audiovisual Integration Tasks: A Comprehensive EEG Source-Based Analysis

The seamless integration of visual and auditory information is a fundamental aspect of human cognition. Although age-related functional changes in Audio-Visual Integration (AVI) have been extensively explored in the past, thorough studies across various age groups remain insufficient. Previous studies have provided valuable insights into agerelated AVI using EEG-based sensor data. However, these studies have been limited in their ability to capture spatial information related to brain source activation and their connectivity. To address these gaps, our study conducted a comprehensive audiovisual integration task with a specific focus on assessing the aging effects in various age groups, particularly middle-aged individuals. We presented visual, auditory, and audio-visual stimuli and recorded EEG data from Young (18-25 years), Transition (26- 33 years), and Middle (34-42 years) age cohort healthy participants. We aimed to understand how aging affects brain activation and functional connectivity among hubs during audio-visual tasks. Our findings revealed delayed brain activation in middleaged individuals, especially for bimodal stimuli. The superior temporal cortex and superior frontal gyrus showed significant changes in neuronal activation with aging. Lower frequency bands (theta and alpha) showed substantial changes with increasing age during AVI. Our findings also revealed that the AVI-associated brain regions can be clustered into five different brain networks using the k-means algorithm. Additionally, we observed increased functional connectivity in middle age, particularly in the frontal, temporal, and occipital regions. These results highlight the compensatory neural mechanisms involved in aging during cognitive tasks.
Support Vector Machine Implementation on MPI-CUDA and Tensorflow Framework

Support Vector Machine (SVM) algorithm requires a high computational cost (both in memory and time) to solve a complex quadratic programming (QP) optimization problem during the training process. Consequently, SVM necessitates high computing hardware capabilities. The central processing unit (CPU) clock frequency cannot be increased due to physical limitations in the miniaturization process. However, the potential of parallel multi-architecture, available in both multi-core CPUs and highly scalable GPUs, emerges as a promising solution to enhance algorithm performance. Therefore, there is an opportunity to reduce the high computational time required by SVM for solving the QP optimization problem. This paper presents a comparative study that implements the SVM algorithm on different parallel architecture frameworks. The experimental results show that SVM MPI-CUDA implementation achieves a speedup over SVM TensorFlow implementation on different datasets. Moreover, SVM TensorFlow implementation provides a cross-platform solution that can be migrated to alternative hardware components, which will reduces the development time.
Advancing Fluid-Based Thermal Management Systems Design: Leveraging Graph Neural Networks for Graph Regression and Efficient Enumeration Reduction

In this research, we developed a graph-based framework to represent various aspects of optimal thermal management system design, with the aim of rapidly and efficiently identifying optimal design candidates. Initially, the graph-based framework is utilized to generate diverse thermal management system architectures. The dynamics of these system architectures are modeled under various loading conditions, and an open-loop optimal controller is employed to determine each system's optimal performance. These modeled cases constitute the dataset, with the corresponding optimal performance values serving as the labels for the data. In the subsequent step, a Graph Neural Network (GNN) model is trained on 30% of the labeled data to predict the systems' performance, effectively addressing a regression problem. Utilizing this trained model, we estimate the performance values for the remaining 70% of the data, which serves as the test set. In the third step, the predicted performance values are employed to rank the test data, facilitating prioritized evaluation of the design scenarios. Specifically, a small subset of the test data with the highest estimated ranks undergoes evaluation via the open-loop optimal control solver. This targeted approach concentrates on evaluating higher-ranked designs identified by the GNN, replacing the exhaustive search (enumeration-based) of all design cases. The results demonstrate a significant average reduction of over 92% in the number of system dynamic modeling and optimal control analyses required to identify optimal design scenarios.
KOPPA: Improving Prompt-based Continual Learning with Key-Query Orthogonal Projection and Prototype-based One-Versus-All

Drawing inspiration from prompt tuning techniques applied to Large Language Models, recent methods based on pre-trained ViT networks have achieved remarkable results in the field of Continual Learning. Specifically, these approaches propose to maintain a set of prompts and allocate a subset of them to learn each task using a key-query matching strategy. However, they may encounter limitations when lacking control over the correlations between old task queries and keys of future tasks, the shift of features in the latent space, and the relative separation of latent vectors learned in independent tasks. In this work, we introduce a novel key-query learning strategy based on orthogonal projection, inspired by model-agnostic meta-learning, to enhance prompt matching efficiency and address the challenge of shifting features. Furthermore, we introduce a One-Versus-All (OVA) prototype-based component that enhances the classification head distinction. Experimental results on benchmark datasets demonstrate that our method empowers the model to achieve results surpassing those of current state-of-the-art approaches by a large margin of up to 20%.
Direct 3D imaging through spatial coherence of light

Wide-field imaging is widely adopted due to its fast acquisition, cost-effectiveness and ease of use. Its extension to direct volumetric applications, however, is burdened by the trade-off between resolution and depth of field (DOF), dictated by the numerical aperture of the system. We demonstrate that such trade-off is not intrinsic to wide-field imaging, but stems from the spatial incoherence of light: images obtained through spatially coherent illumination are shown to have resolution and DOF independent of the numerical aperture. This fundamental discovery enabled us to demonstrate an optimal combination of coherent resolution-DOF enhancement and incoherent tomographic sectioning for scanning-free, wide-field 3D microscopy on a multicolor histological section.
Neuroscience inspired scientific machine learning (Part-2): Variable spiking wavelet neural operator

We propose, in this paper, a Variable Spiking Wavelet Neural Operator (VS-WNO), which aims to bridge the gap between theoretical and practical implementation of Artificial Intelligence (AI) algorithms for mechanics applications. With recent developments like the introduction of neural operators, AI's potential for being used in mechanics applications has increased significantly. However, AI's immense energy and resource requirements are a hurdle in its practical field use case. The proposed VS-WNO is based on the principles of spiking neural networks, which have shown promise in reducing the energy requirements of the neural networks. This makes possible the use of such algorithms in edge computing. The proposed VS-WNO utilizes variable spiking neurons, which promote sparse communication, thus conserving energy, and its use is further supported by its ability to tackle regression tasks, often faced in the field of mechanics. Various examples dealing with partial differential equations, like Burger's equation, Allen Cahn's equation, and Darcy's equation, have been shown. Comparisons have been shown against wavelet neural operator utilizing leaky integrate and fire neurons (direct and encoded inputs) and vanilla wavelet neural operator utilizing artificial neurons. The results produced illustrate the ability of the proposed VS-WNO to converge to ground truth while promoting sparse communication.
Mapping a Lower Limit on the Mass Fraction of the Cold Neutral Medium Using Fourier-transformed H i 21 cm Emission Line Spectra: Application to the DRAO Deep Field from DHIGLS and the HI4PI Survey

We develop a new method for spatially mapping a lower limit on the mass fraction of the cold neutral medium by analyzing the amplitude structure of T ˆ b ( k v ) , the Fourier transform of T b (v), the spectrum of the brightness temperature of the H i 21 cm line emission with respect to the radial velocity v. This advances a broader effort exploiting 21 cm emission line data alone (without absorption line data, τ) to extract integrated properties of the multiphase structure of the H i gas and to map each phase separately. Using toy models, we illustrate the origin of interference patterns seen in T ˆ b ( k v ) . Building on this, a lower limit on the cold gas mass fraction is obtained from the amplitude of T ˆ b at high k v . Tested on a numerical simulation of thermally bi-stable turbulence, the lower limit from this method has a strong linear correlation with the “true” cold gas mass fraction from the simulation for a relatively low cold gas mass fraction. At a higher mass fraction, our lower limit is lower than the “true” value, because of a combination of interference and opacity effects. Comparison with absorption surveys shows a similar behavior, with a departure from linear correlation at N H I ≳ 3–5 × 1020 cm−2. Application to the DRAO Deep Field from DHIGLS reveals a complex network of cold filaments in the Spider, an important structural property of the thermal condensation of the H i gas. Application to the HI4PI survey in the velocity range −90 < v < 90 km s−1 produces a full sky map of a lower limit on the mass fraction of the cold neutral medium at 16. ′2 resolution. Our new method has the ability to extract a lower limit on the cold gas mass fraction for massive amounts of emission line data alone with low computing time and memory, pointing the way to new approaches suitable for the new generation of radio interferometers.
Revisiting Quantum Algorithms for Linear Regressions: Quadratic Speedups without Data-Dependent Parameters

Linear regression is one of the most fundamental linear algebra problems. Given a dense matrix $A \in \mathbb{R}^{n \times d}$ and a vector $b$, the goal is to find $x'$ such that $ \| Ax' - b \|_2^2 \leq (1+\epsilon) \min_{x} \| A x - b \|_2^2 $. The best classical algorithm takes $O(nd) + \mathrm{poly}(d/\epsilon)$ time [Clarkson and Woodruff STOC 2013, Nelson and Nguyen FOCS 2013]. On the other hand, quantum linear regression algorithms can achieve exponential quantum speedups, as shown in [Wang Phys. Rev. A 96, 012335, Kerenidis and Prakash ITCS 2017, Chakraborty, Gily{\'e}n and Jeffery ICALP 2019]. However, the running times of these algorithms depend on some quantum linear algebra-related parameters, such as $\kappa(A)$, the condition number of $A$. In this work, we develop a quantum algorithm that runs in $\widetilde{O}(\epsilon^{-1}\sqrt{n}d^{1.5}) + \mathrm{poly}(d/\epsilon)$ time. It provides a quadratic quantum speedup in $n$ over the classical lower bound without any dependence on data-dependent parameters. In addition, we also show our result can be generalized to multiple regression and ridge linear regression.
An HCAI Methodological Framework: Putting It Into Action to Enable Human-Centered AI

Human-centered AI (HCAI), as a design philosophy, advocates prioritizing humans in designing, developing, and deploying intelligent systems, aiming to maximize the benefits of AI technology to humans and avoid its potential adverse effects. While HCAI has gained momentum, the lack of guidance on methodology in its implementation makes its adoption challenging. After assessing the needs for a methodological framework for HCAI, this paper first proposes a comprehensive and interdisciplinary HCAI methodological framework integrated with seven components, including design goals, design principles, implementation approaches, design paradigms, interdisciplinary teams, methods, and processes. THe implications of the framework are also discussed. This paper also presents a"three-layer"approach to facilitate the implementation of the framework. We believe the proposed framework is systematic and executable, which can overcome the weaknesses in current frameworks and the challenges currently faced in implementing HCAI. Thus, the framework can help put it into action to develop, transfer, and implement HCAI in practice, eventually enabling the design, development, and deployment of HCAI-based intelligent systems.
The impact of elliptical deformations for optimizing the performance of dual-core fluorine-doped photonic crystal fiber

: In this paper we study the impact of elliptically-deformed features such as cladding air-holes and elliptically-modulated cores, as ingredients for optimizing the coupling characteristics of dual-core fluorine-doped photonic crystal fiber (PCF) couplers. We provide a detailed numerical investigation by using a trial and error approach for optimizing the propagation characteristics of fluorine-doped PCF couplers. Typical characteristics of the newly proposed PCF coupler structure are: wavelength-flattened coupling characteristics between 0.7 μ m and 1.6 μ m wavelength range, coupling efficiency of 50 ± 1 % from 0.9 μ m to 1.6 μ m, and a reasonably small coupling length of 1.3 cm. In addition we have elaborately derived the design parameters so that our proposed dual-core PCF coupler exhibits polarization-insensitive characteristics verified by using a full-vectorial beam propagation method. The proposed dual-core PCF can be effectively used as a 3-dB coupler, over a wide wavelength range.
Mechanism and inotropic actions of the water extracts of the leaves and seeds of Datura metel (Solanaceae) on isolated frog’s heart

Datura metel leaves are used by athletes to enhance their performances. The water extract of the leaves and seeds was tested for their cardiotonic effect. Langherdoff’s model for the measurement of the mechanical activity of the isolated amphibian heart in ventricular perfusion was used to evaluate some parameters of cardiac contractility. The effect of the extracts was also compared to sympathetic agonist and antagonist. The leaf and seed extracts induced a significant ( p < 0.05) positive inotropic effect (68.65% and 70.42%) when used at 1.5 and 2.5 mg/mL, respectively. No significant change in the heart rate was observed . In addition, reversible heart arrest for the leaf and seed extracts was noticed at the dose of 6 and 5 mg/ml, respectively. The stimulatory activity of the tested extracts was inhibited by acetylcholine and amlodipine, suggesting that the extracts have cardiotonic properties, likely through muscarinic receptors and calcium channels.
Gene expression in the primate orbitofrontal cortex related to anxious temperament

Significance Anxiety disorders, as well as other comorbid psychiatric disorders, pose a staggering burden worldwide. Currently available treatments, while effective, fail to achieve remission in a majority of patients. Novel treatments, guided by an understanding of the neurobiological substrates of anxiety disorders, would pave the way for increased efficacy in treatment and therefore improved outcomes for individuals suffering from these disorders. The studies within this paper explore the molecules associated with a highly translationally relevant nonhuman primate model for the risk for anxiety disorders, highlighting pathways that could be targeted in the treatment of anxiety.
Evolvability A Unifying Concept in Evolutionary Biology?

The relevance of ge ne tic constraints for evolutionary change beyond microevolutionary timescales is debated. The high evolvability of natu ral populations predicts rapid adaptation, but evolvability is often found to correlate with phenotypic divergence on longer timescales, which makes sense if evolvability constrains divergence. This chapter attempts to reconcile the observation of high evolv-ability of populations with the idea that ge ne tic constraints may still be relevant on long timescales. We first establish that a relationship between evolvability and divergence is a common empirical phenomenon both among populations within species (microevolution) and among species (macro-evolution). We then argue that a satisfactory model for the prevalence of this empirical relationship is lacking. Linking microevolutionary theory with the dynamics of the adaptive landscape across time— moving toward a proper quantitative theory of phenotypic change on macroevolution times-cales—is key to better understanding the relative importance of ge ne tic constraints on phenotypic evolution beyond a handful of generations.
Prehistory of Nevada's northern tier : archaeological investigations along the Ruby Pipeline. (Anthropological papers of the American Museum of Natural History, no. 101)

9 CHAPTER
Delta Coverage: The Analytics Journey to Implement a Novel Nurse Deployment Program

Amidst critical levels of nurse shortages, we partnered with Indiana University Health (IUH) System to pioneer a novel suite of advanced data and decision analytics for a groundbreaking internal travel nursing program. This state-wide program leverages a flexible pool of resource nurses who can move between the 16 IUH hospitals located in five diverse regions and serving more than 1.4 million residents. This program breaks the mold of traditional resource nurse by moving nurses between hospitals to dynamically respond to short-term patient census fluctuations in days rather than weeks. This paradigm shift necessitated the development of new operational protocols and analytics to execute them, including a creating two-week advance on-call list for travel and a 24-48 hour call-in decision. Our co-developed Delta Coverage Analytics Suite, launched in Oct 2021 as a Microsoft Power BI application, provides an integrated solution to support this groundbreaking initiative at an unprecedented state-wide scale, in contrast to existing nurse scheduling tools that primarily cater to single hospitals or units. The suite incorporates (i) a patient census forecast based on a deep generative model capturing complex spatial-temporal correlations and avoiding error accumulation common in traditional time-series models, which seamlessly integrates with (ii) a stochastic optimization that prescribes optimal on-call and call-in decisions. The pilot, conducted from May to June 2023, produced a remarkable 13% reduction in understaffing, with estimated annual savings of $ 400K and over 250 fewer understaffed shifts, all by efficiently managing the movement of only 10 nurses. As the first known program of its kind, our efforts establish new benchmarks for evidence-based and data-driven nurse workforce management, potentially transforming how healthcare institutions approach staffing challenges nationwide.
C³: Code Clone-Based Identification of Duplicated Components

Reinventing the wheel is a detrimental programming practice in software development that frequently results in the introduction of duplicated components. This practice not only leads to increased maintenance and labor costs but also poses a higher risk of propagating bugs throughout the system. Despite numerous issues introduced by duplicated components in software, the identification of component-level clones remains a significant challenge that existing studies struggle to effectively tackle. Specifically, existing methods face two primary limitations that are challenging to overcome: 1) Measuring the similarity between different components presents a challenge due to the significant size differences among them; 2) Identifying functional clones is a complex task as determining the primary functionality of components proves to be difficult. To overcome the aforementioned challenges, we present a novel approach named C3 (Component-level Code Clone detector) to effectively identify both textual and functional cloned components. In addition, to enhance the efficiency of eliminating cloned components, we develop an assessment method based on six component-level clone features, which assists developers in prioritizing the cloned components based on the refactoring necessity. To validate the effectiveness of C3, we employ a large-scale industrial product developed by Huawei, a prominent global ICT company, as our dataset and apply C3 to this dataset to identify the cloned components. Our experimental results demonstrate that C3 is capable of accurately detecting cloned components, achieving impressive performance in terms of precision (0.93), recall (0.91), and F1-score (0.9). Besides, we conduct a comprehensive user study to further validate the effectiveness and practicality of our assessment method and the proposed clone features in assessing the refactoring necessity of different cloned components. Our study establishes solid alignment between assessment outcomes and participant responses, indicating the accurate prioritization of clone components with a high refactoring necessity through our method. This finding further confirms the usefulness of the six “golden features” in our assessment.