Miracle-dz/UniFAR-FTUs huggingface dataset:
Research and Application of Cloud Master Station Architecture for The Acquisition of Electric Energy Data Based on Big Data

With the continuous deepening of the construction of new power systems and the deepening application of user side data collection, the main station of the electric energy data collection system has encountered bottlenecks in data collection, calculation, storage, etc. which cannot meet the needs of rapid business development. This paper proposes a master station system technical scheme with high-frequency real-time acquisition characteristics, which comprehensively resolves the efficiency issues of high-frequency concurrent interaction, Real-time computing, and massive data storage. It is based on Microservices architecture, distributed storage, real-time stream computing, memory batch computing, and other technologies. It has been established through experimental environment verification and on-site application in a particular Chinese province that the suggested scheme can significantly increase the collection capacity of the power consumption information collection master station and enhance the functionality of the conventional centralized collection master station.
Patient-Specific Vascularized Tumor Model: Blocking TAM Recruitment with Multispecific Antibodies Targeting CCR2 and CSF-1R

Tumor-associated inflammation drives cancer progression and therapy resistance, with the infiltration of monocyte-derived tumor-associated macrophages (TAMs) associated with poor prognosis in diverse cancers. Targeting TAMs holds potential against solid tumors, but effective immunotherapies require testing on immunocompetent human models prior to clinical trials. Here, we develop an in vitro model of microvascular networks that incorporates tumor spheroids or patient tissues. By perfusing the vasculature with human monocytes, we investigate monocyte trafficking into the tumor and evaluate immunotherapies targeting the human tumor microenvironment. Our findings demonstrate that macrophages in vascularized breast and lung tumor models can enhance monocyte recruitment via TAM-produced CCL7 and CCL2, mediated by CSF-1R. Additionally, we assess a novel multispecific antibody targeting CCR2, CSF-1R, and neutralizing TGF-β, referred to as CSF1R/CCR2/TGF-β Ab, on monocytes and macrophages using our 3D models. This antibody repolarizes TAMs towards an anti-tumoral M1-like phenotype, reduces monocyte chemoattractant protein secretion, and effectively blocks monocyte migration. Finally, we show that the CSF1R/CCR2/TGF-β Ab inhibits monocyte recruitment in patient-specific vascularized tumor models. Overall, this vascularized tumor model offers valuable insights into monocyte recruitment and enables functional testing of innovative therapeutic antibodies targeting TAMs in the tumor microenvironment (TME).
“Nitrogen demand, supply, and acquisition strategy control plant responses to elevated CO2 at different scales”

Plants respond to elevated atmospheric CO2 concentrations by reducing leaf nitrogen content and photosynthetic capacity – patterns that correspond with increased net photosynthesis rates, total leaf area, and total biomass. Nitrogen supply has been hypothesized to be the primary factor controlling these responses, as nitrogen availability limits net primary productivity globally. Recent work using evo-evolutionary optimality theory suggests that leaf photosynthetic responses to elevated CO2 are independent of nitrogen supply and are instead driven by leaf nitrogen demand to build and maintain photosynthetic enzymes, which optimizes resource allocation to photosynthetic capacity and maximizes allocation to growth. Here, Glycine max L. (Merr) seedlings were grown under two CO2 concentrations, with and without inoculation with Bradyrhizobium japonicum, and across nine soil nitrogen fertilization treatments in a full-factorial growth chamber experiment to reconcile the role of nitrogen supply and demand on leaf and whole-plant responses to elevated CO2. After seven weeks, elevated CO2 increased net photosynthesis rates despite reduced leaf nitrogen content and maximum rates of Ribulose-1,5-bisphosphate (RuBP) carboxylase/oxygenase (Rubisco) carboxylation and electron transport for RuBP regeneration. Effects of elevated CO2 on net photosynthesis and indices of photosynthetic capacity were independent of nitrogen fertilization and inoculation. However, increasing nitrogen fertilization enhanced positive effects of elevated CO2 on total leaf area and total biomass due to increased nitrogen uptake and reduced carbon costs to acquire nitrogen. Whole-plant responses to elevated CO2 were not modified by inoculation across the nitrogen fertilization gradient, as plant investment toward symbiotic nitrogen fixation was similar between CO2 treatments. These results indicate that leaf nitrogen demand to build and maintain photosynthetic enzymes drives leaf photosynthetic responses to elevated CO2, while nitrogen supply regulates whole-plant responses. Our findings build on previous work suggesting that terrestrial biosphere models may improve simulations of photosynthetic processes under future novel environments by adopting optimality principles.
Internally-consistent and fully-unbiased multimodal MRI brain template construction from UK Biobank: Oxford-MM

Anatomical MRI templates of the brain are essential to group-level analyses and image processing pipelines, as they provide a reference space for spatial normalisation. While it has become common for studies to acquire multimodal MRI data, many templates are still limited to one type of modality, usually either scalar or tensor-based. Aligning each modality in isolation does not take full advantage of the available complementary information, such as strong contrast between tissue types in structural images, or axonal organisation in the white matter in diffusion tensor images. Most existing strategies for multimodal template construction either do not use all modalities of interest to inform the template construction process, or do not use them in a unified framework. Here, we present multimodal, cross-sectional templates constructed from UK Biobank data: the OMM-1 template, and age-dependent templates for each year of life between 45 to 81. All templates are fully unbiased to represent the average shape of the populations they were constructed from, and internally consistent through jointly informing the template construction process with T1, T2-FLAIR and DTI data. The OMM-1 template was constructed with a multi-resolution, iterative approach using 240 individuals in the 50-55 year age range. The age-dependent templates were estimated using a Gaussian Process, which describes the change in average brain shape with age in 37,330 individuals. All templates show excellent contrast and alignment within and between modalities. The global brain shape and size is not preconditioned on existing templates, although maximal possible compatibility with MNI-152 space was maintained through rigid alignment. We showed benefits in registration accuracy across two datasets (UK Biobank and HCP), when using the OMM-1 as the template compared with FSL’s MNI-152 template, and found that the use of age-dependent templates further improved accuracy to a small but detectable extent. All templates are publicly available and can be used as a new reference space for uni- or multimodal spatial alignment.
Multi-task machine learning reveals the neuroanatomy fingerprint of mental processing

Mental processing delineates the functions of the human mind encompassing a wide range of motor, sensory, emotional, and cognitive processes, each of which is underlain by the neuroanatomical substrates. Identifying accurate representation of neuroanatomical substrates of mental processing could inform understanding of its neural mechanism. The challenge is that it is unclear whether a specific mental process possesses a ’neuroanatomy fingerprint’, i.e., a unique pattern of neuroanatomy that determines the mental process. We used multi-task deep learning to disentangle the neuroanatomy fingerprint of a mental process from other multiple mental processes. The neuroanatomy fingerprint is a unique set of functional activity strength of whole-brain regions with high discrimination ability (AUC = 0.99) among different mental processes. The functional activity strength distribution of neuroanatomy fingerprint unveils both common and distinct brain regions involved in different mental processes. The neuroanatomy fingerprint also addresses a long-standing hypothesis: At least in Chinese and US populations, there consistently exists a neuroanatomy fingerprint to underlie the associated mental process. One-Sentence Summary Each mental process has a neuroanatomy fingerprint, which is represented as a unique pattern of functional activity strength of whole-brain regions, to differentiate from other different mental processes.
Analysis of the Cross-Study Replicability of Tuberculosis Gene Signatures Using 49 Curated Transcriptomic Datasets

Background Tuberculosis (TB) is the leading cause of infectious disease mortality worldwide. Numerous blood-based gene expression signatures have been proposed in the literature as alternative tools for diagnosing TB infection. Ongoing efforts are actively focused on developing additional signatures in other TB-related contexts. However, the generalizability of these signatures to different patient contexts is not well-characterized. There is a pressing need for a well-curated database of TB gene expression studies for the systematic assessment of existing and newly developed TB gene signatures. Results We built the curatedTBData, a manually-curated database of 49 TB transcriptomic studies. This data resource is freely available through GitHub and as an R Bioconductor package that allows users to validate new and existing biomarkers without the challenges of harmonizing heterogeneous studies. We also demonstrate the use of this data resource with cross-study comparisons for 72 TB gene signatures. For the comparison of subjects with active TB from healthy controls, 19 gene signatures had weighted mean AUC of 0.90 or greater, with the highest result of 0.94. In active TB disease versus latent TB infection, 7 gene signatures had weighted mean AUC of 0.90 or greater, with a maximum of 0.93. We also explore ensembling methods for averaging predictions from multiple gene signatures to significantly improve diagnostic ability beyond any single signature. Conclusions The curatedTBData data package offers a comprehensive resource of curated gene expression and clinically annotated data. It could be used to identify robust new TB gene signatures, to perform comparative analysis of existing TB gene signatures, and to develop alternative gene set scoring or ensembling methods, among other things. This resource will also facilitate the development of new signatures that are generalizable across cohorts or more applicable to specific subsets of patients (e.g. with rare comorbid conditions, etc.). We demonstrated that these blood-based gene signatures could distinguish patients with distinct TB outcomes; moreover, the combination of multiple gene signatures could improve the overall predictive accuracy in differentiating these subtypes, which point out an important aspect for the translation of genomics to clinical implementation.
Deep Networks as Paths on the Manifold of Neural Representations

Deep neural networks implement a sequence of layer-by-layer operations that are each relatively easy to understand, but the resulting overall computation is generally difficult to understand. An intuitive hypothesis is that the role of each layer is to reformat information to reduce the “distance” to the desired outputs. With this spatial analogy, the layer-wise computation implemented by a deep neural network can be viewed as a path along a high-dimensional manifold of neural representations. With this framework, each hidden layer transforms its inputs by taking a step of a particular size and direction along the manifold, ideally moving towards the desired network outputs. We formalize this intuitive idea by leveraging recent advances in metric representational similarity. We extend existing representational distance methods by defining and characterizing the manifold that neural representations live on, allowing us to calculate quantities like the shortest path or tangent direction separating representations between hidden layers of a network or across different networks. We then demonstrate these tools by visualizing and comparing the paths taken by a collection of trained neural networks with a variety of architectures, finding systematic relationships between model depth and width, and properties of their paths.
The Weight-Estimate Method at Work in Southeastern Oregon.

The objectives of the weightestimate method as used in conjunction with range surveys might be summarized as: to check the condition and production of the range and to obtain information to aid in its improvement and restoration, if necessary. To accomplish these purposes, the weight-estimate method requires that clipping of usable range forage be done periodically to enable the observer to check original estimates of plant weights.
Equivariant plug-and-play image reconstruction

Plug-and-play algorithms constitute a popular framework for solving inverse imaging problems that rely on the implicit definition of an image prior via a denoiser. These algorithms can leverage powerful pre-trained denoisers to solve a wide range of imaging tasks, circumventing the necessity to train models on a per-task basis. Unfortunately, plug-and-play methods often show unstable behaviors, hampering their promise of versatility and leading to suboptimal quality of reconstructed images. In this work, we show that enforcing equivariance to certain groups of transformations (rotations, reflections, and/or translations) on the denoiser strongly improves the stability of the algorithm as well as its reconstruction quality. We provide a theoretical analysis that illustrates the role of equivariance on better performance and stability. We present a simple algorithm that enforces equivariance on any existing denoiser by simply applying a random transformation to the input of the denoiser and the inverse transformation to the output at each iteration of the algorithm. Experiments on multiple imaging modalities and denoising networks show that the equivariant plug-and-play algorithm improves both the reconstruction performance and the stability compared to their non-equivariant counterparts.
Whence Nonlocality? Removing Spooky Action-at-a-Distance from the de Broglie Bohm Pilot-Wave Theory Using a Time-Symmetric Version of the de Broglie Double Solution

In this work, we review and extend a version of the old attempt made by Louis de Broglie for interpreting quantum mechanics in realistic terms, namely, the double solution. In this theory, quantum particles are localized waves, i.e., solitons, that are solutions of relativistic nonlinear field equations. The theory that we present here is the natural extension of this old work and relies on a strong time-symmetry requiring the presence of advanced and retarded waves converging on particles. Using this method, we are able to justify wave–particle duality and to explain the violations of Bell’s inequalities. Moreover, the theory recovers the predictions of the pilot-wave theory of de Broglie and Bohm, often known as Bohmian mechanics. As a direct consequence, we reinterpret the nonlocal action-at-a-distance in the pilot-wave theory. In the double solution developed here, there is fundamentally no action-at-a-distance but the theory requires a form of superdeterminism driven by time-symmetry.
Faster Sublinear-Time Edit Distance

We study the fundamental problem of approximating the edit distance of two strings. After an extensive line of research led to the development of a constant-factor approximation algorithm in almost-linear time, recent years have witnessed a notable shift in focus towards sublinear-time algorithms. Here, the task is typically formalized as the $(k, K)$-gap edit distance problem: Distinguish whether the edit distance of two strings is at most $k$ or more than $K$. Surprisingly, it is still possible to compute meaningful approximations in this challenging regime. Nevertheless, in almost all previous work, truly sublinear running time of $O(n^{1-\varepsilon})$ (for a constant $\varepsilon>0$) comes at the price of at least polynomial gap $K \ge k \cdot n^{\Omega(\varepsilon)}$. Only recently, [Bringmann, Cassis, Fischer, and Nakos; STOC'22] broke through this barrier and solved the sub-polynomial $(k, k^{1+o(1)})$-gap edit distance problem in time $O(n/k + k^{4+o(1)})$, which is truly sublinear if $n^{\Omega(1)} \le k \le n^{\frac14-\Omega(1)}$. The $n/k$ term is inevitable (already for Hamming distance), but it remains an important task to optimize the $\mathrm{poly}(k)$ term and, in general, solve the $(k, k^{1+o(1)})$-gap edit distance problem in sublinear-time for larger values of $k$. In this work, we design an improved algorithm for the $(k, k^{1+o(1)})$-gap edit distance problem in sublinear time $O(n/k + k^{2+o(1)})$, yielding a significant quadratic speed-up over the previous $O(n/k + k^{4+o(1)})$-time algorithm. Notably, our algorithm is unconditionally almost-optimal (up to subpolynomial factors) in the regime where $k \leq n^{\frac13}$ and improves upon the state of the art for $k \leq n^{\frac12-o(1)}$.
Has Anything Changed? 3D Change Detection by 2D Segmentation Masks

As capturing devices become common, 3D scans of interior spaces are acquired on a daily basis. Through scene comparison over time, information about objects in the scene and their changes is inferred. This information is important for robots and AR and VR devices, in order to operate in an immersive virtual experience. We thus propose an unsupervised object discovery method that identifies added, moved, or removed objects without any prior knowledge of what objects exist in the scene. We model this problem as a combination of a 3D change detection and a 2D segmentation task. Our algorithm leverages generic 2D segmentation masks to refine an initial but incomplete set of 3D change detections. The initial changes, acquired through render-and-compare likely correspond to movable objects. The incomplete detections are refined through graph optimization, distilling the information of the 2D segmentation masks in the 3D space. Experiments on the 3Rscan dataset prove that our method outperforms competitive baselines, with SoTA results.
A New Challenging Curve Fitting Benchmark Test Set for Global Optimization Solvers

Benchmark sets are extremely important for evaluating and developing global optimization algorithms and related solvers. A new test set named PCC benchmark is proposed especially for optimization problems of nonlinear curve fitting for the first time, with the aspiration of helping developers to investigate and compare the performance of different global optimization solvers, as well as more effective optimization algorithms could be developed. Compared with the well-known classical nonlinear curve fitting benchmark set given by the National Institute of Standards and Technology (NIST) of USA, the most distinguishable features of the PCC benchmark are small problem dimensions, unconstrained with free search domain and high level of difficulty for obtaining global optimization solutions, which make the PCC benchmark be not only suitable for validating the effectiveness of different global optimization algorithms, but also more ideal for verifying and comparing various related solvers. Seven of the world's leading global optimization solvers, including Baron, Antigone, Couenne, Lingo, Scip, Matlab-GA and 1stOpt, are employed to test NIST and PCC benchmark thoroughly in terms of both effectiveness and efficiency. The results showed that the NIST benchmark is relatively simple and not suitable for global optimization testing, meanwhile the PCC benchmark is a unique, challenging and effective test dataset for global optimization.
FlatProxy: A DPU-centric Service Mesh Architecture for Hyperscale Cloud-native Application

Service mesh is a fundamental technology for building cloud-native applications, which ensures the stable running of a large number of services by an intermediate layer that governs communication between services. However, service mesh is not well suited for high-performance scenarios. The root cause is that the current service mesh is not suitable for the evolution of cloud-native applications. On the one hand, the service mesh built on CPU cannot listen to communication bypassing the CPU. On the other hand, service mesh includes many I/O-intensive and computationally-intensive tasks that can overload CPU cores as traffic grows beyond CPU performance. Therefore, we propose a data-centric service mesh that migrates the proxy of the service mesh to the entrance of the network. Moreover, we also design the DPU-centric FlatProxy, a data-centric service mesh based on DPU. There are three advantages to the DPU-centric service mesh. Firstly, it takes over all traffic flow in and out of the node, which expands the sense scale of the service mesh from container to node. Secondly, it improves communication performance and reduces host resource usage by offloading some functions and optimizing communication. Thirdly, it minimizes performance and security issues through the physical isolation of business services and cloud infrastructure. Compared with Envoy, the current mainstream service mesh implementation, FlatProxy reduces latency by 90\% and improves throughput by 4x in Gbps and 8x in qps, and it only occupies a small amount of CPU resources.
JUNO: Optimizing High-Dimensional Approximate Nearest Neighbour Search with Sparsity-Aware Algorithm and Ray-Tracing Core Mapping

Approximate nearest neighbor (ANN) search is a widely applied technique in modern intelligent applications, such as recommendation systems and vector databases. Therefore, efficient and high-throughput execution of ANN search has become increasingly important. In this paper, we first characterize the state-of-the-art product quantization-based method of ANN search and identify a significant source of inefficiency in the form of unnecessary pairwise distance calculations and accumulations. To improve efficiency, we propose JUNO, an end-to-end ANN search system that adopts a carefully designed sparsity- and locality-aware search algorithm. We also present an efficient hardware mapping that utilizes ray tracing cores in modern GPUs with pipelined execution on tensor cores to execute our sparsity-aware ANN search algorithm. Our evaluations on four datasets ranging in size from 1 to 100 million search points demonstrate 2.2x-8.5x improvements in search throughput. Moreover, our algorithmic enhancements alone achieve a maximal 2.6x improvement on the hardware without the acceleration of the RT core.