Miracle-dz/UniFAR-FTUs huggingface dataset:
Minimum Cost Loop Nests for Contraction of a Sparse Tensor with a Tensor Network

Sparse tensor decomposition and completion are common in numerous applications, ranging from machine learning to computational quantum chemistry. Typically, the main bottleneck in optimization of these models are contractions of a single large sparse tensor with a network of several dense matrices or tensors (SpTTN). Prior works on high-performance tensor decomposition and completion have focused on performance and scalability optimizations for specific SpTTN kernels. We present algorithms and a runtime system for identifying and executing the most efficient loop nest for any SpTTN kernel. We consider both enumeration of such loop nests for autotuning and efficient algorithms for finding the lowest cost loop-nest for simpler metrics, such as buffer size or cache miss models. Our runtime system identifies the best choice of loop nest without user guidance, and also provides a distributed-memory parallelization of SpTTN kernels. We evaluate our framework using both real-world and synthetic tensors. Our results demonstrate that our approach outperforms available generalized state-of-the-art libraries and matches the performance of specialized codes.
Life in the Cosmos: Paradox of Silence and Self-Awareness

As humanity embarks on an age of exploration, the question of whether we are alone in the universe remains unanswered. This comprehensive review reflects on the paradoxical nature of our existence in a seemingly lifeless cosmos, delving into the silence we encounter and the depths of our self-awareness. We embark on a journey that encompasses the search for life within our solar system, the mysteries of exoplanets, and the absence of technologically detectable life. Traditional definitions of life are challenged, especially in the context of artificial intelligence, as we strive to understand the complexities of existence. Contemplating our significance and insignificance in the vast cosmos, we grapple with the profound responsibility that accompanies being the only known life forms. Through introspection and contemplation, we capture the essence of our epoch -- an era defined by cosmic loneliness yet magnificent self-awareness.
A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions

Automating the monitoring of industrial processes has the potential to enhance efficiency and optimize quality by promptly detecting abnormal events and thus facilitating timely interventions. Deep learning, with its capacity to discern non-trivial patterns within large datasets, plays a pivotal role in this process. Standard deep learning methods are suitable to solve a specific task given a specific type of data. During training, deep learning demands large volumes of labeled data. However, due to the dynamic nature of the industrial processes and environment, it is impractical to acquire large-scale labeled data for standard deep learning training for every slightly different case anew. Deep transfer learning offers a solution to this problem. By leveraging knowledge from related tasks and accounting for variations in data distributions, the transfer learning framework solves new tasks with little or even no additional labeled data. The approach bypasses the need to retrain a model from scratch for every new setup and dramatically reduces the labeled data requirement. This survey first provides an in-depth review of deep transfer learning, examining the problem settings of transfer learning and classifying the prevailing deep transfer learning methods. Moreover, we delve into applications of deep transfer learning in the context of a broad spectrum of time series anomaly detection tasks prevalent in primary industrial domains, e.g., manufacturing process monitoring, predictive maintenance, energy management, and infrastructure facility monitoring. We discuss the challenges and limitations of deep transfer learning in industrial contexts and conclude the survey with practical directions and actionable suggestions to address the need to leverage diverse time series data for anomaly detection in an increasingly dynamic production environment.
JWST Confirms the Nature of CID-42

The galaxy CID-42 (CXOC J100043.1+020637.2) at z = 0.359 has been proposed to contain a promising candidate for a gravitational-wave recoiling supermassive black hole (SMBH), a slingshot SMBH from a triple-SMBH interaction, or a kiloparsec-scale dual active galactic nucleus (AGN). These claims were primarily based on a pair of bright cores separated by ∼0. ″5 resolved in optical Hubble Space Telescope (HST) imaging. Existing HST, Chandra, and ground-based imaging and spectroscopy are unable to confirm either scenario. With improved spatial resolution, depth, and IR wavelength coverage, NIRCam multiband imaging from the COSMOS-Web JWST treasury program well resolved the two cores in CID-42, revealing a significant stellar bulge for both cores (with stellar masses of ∼1010 M ⊙ for both). JWST imaging further revealed that only the SE core contains an unobscured AGN point source, based on both image decomposition and spectral energy distribution fitting. There is no evidence for AGN activity in the NW core based on the current data. These new observations unambiguously rule out the gravitational-wave-recoiling and slingshot-SMBH scenarios and establish CID-42 as a low-redshift merging pair of galaxies, with likely only one active AGN in the system. These results demonstrate the unparalleled capabilities of JWST (even with imaging alone) in studying the galactic-scale environment of merging galaxies and SMBHs.
Local Limit Theorems for Energy Fluxes of Infinite Divisible Random Fields

We study the local asymptotic behavior of divergence-like functionals of a family of $d$-dimensional Infinitely Divisible Random Fields. Specifically, we derive limit theorems of surface integrals over Lipschitz manifolds for this class of fields when the region of integration shrinks to a single point. We show that in most cases, convergence stably in distribution holds after a proper normalization. Furthermore, the limit random fields can be described in terms of stochastic integrals with respect to a L\'evy basis. We additionally discuss how our results can be used to measure the kinetic energy of a possibly turbulent flow.
Thermal phonon fluctuations and stability of the magnetic dual chiral density wave phase in dense QCD

We study the stability against thermal phonon fluctuations of the magnetic dual chiral density wave (MDCDW) phase, an inhomogeneous phase arising in cold dense QCD in a magnetic field. Following a recent study that demonstrated the absence of the Landau-Peierls (LP) instability from this phase, we calculate the (threshold) temperature at which the phonon fluctuations wash out the long-range order over a range of magnetic fields and densities relevant to astrophysical applications. Using a high-order Ginzburg-Landau expansion, we find that the threshold temperature is very near the critical temperature for fields of order $10^{18}$ G, and still a sizable fraction of the critical temperature for fields of order $10^{17}$ G. Therefore, at sufficiently large magnetic fields, the long-range order of the MDCDW phase is preserved over most of the parameter space where it is energetically favored; at smaller magnetic fields, the long-range order is still preserved over a considerable region of parameter space relevant to compact stars. We provide general symmetry arguments to explain why a magnetic field alone is not enough to eliminate the LP instability that characterizes single-modulated phases in 3+1 dimensions.
Language-Routing Mixture of Experts for Multilingual and Code-Switching Speech Recognition

Multilingual speech recognition for both monolingual and code-switching speech is a challenging task. Recently, based on the Mixture of Experts (MoE), many works have made good progress in multilingual and code-switching ASR, but present huge computational complexity with the increase of supported languages. In this work, we propose a computation-efficient network named Language-Routing Mixture of Experts (LR-MoE) for multilingual and code-switching ASR. LR-MoE extracts language-specific representations through the Mixture of Language Experts (MLE), which is guided to learn by a frame-wise language routing mechanism. The weight-shared frame-level language identification (LID) network is jointly trained as the shared pre-router of each MoE layer. Experiments show that the proposed method significantly improves multilingual and code-switching speech recognition performances over baseline with comparable computational efficiency.
RST based Dataset Reduction using Decision Tree

Rough set theory has evolved as one of the most important technique used for feature selection as a result of contemporary developments in data mining. One of the cardinal uses of Rough set theory is its application for rule generation. More often attribute reduction poses a major challenge for developing the theory and applications of rough set. This paper proposes a unique mathematical approach for determining the most important attribute and to construct the decision tree. The proposed algorithm firstly reduces the large volume of dataset which contain redundant instance. These redundant instance doesn't make any contribution to take decision hence can be deleted from the dataset. After reducing the volume of the dataset decision tree is constructed through rough set. The main concept of rough set theory is degree of dependency which is used in the proposed algorithm to select splitting attribute on the compressed data. Thus the proposed algorithm reduces the complexity of tree and in addition increases the accuracy. We have used some UCI machine learning repository (fertility dataset). Since only the subset of the features is included in the decision tree, the proposed algorithm gives better performance.
Doubly robust machine learning for an instrumental variable study of surgical care for cholecystitis

Comparative effectiveness research frequently employs the instrumental variable design since randomized trials can be infeasible for many reasons. In this study, we investigate and compare treatments for emergency cholecystitis -- inflammation of the gallbladder. A standard treatment for cholecystitis is surgical removal of the gallbladder, while alternative non-surgical treatments include managed care and pharmaceutical options. As randomized trials are judged to violate the principle of equipoise, we consider an instrument for operative care: the surgeon's tendency to operate. Standard instrumental variable estimation methods, however, often rely on parametric models that are prone to bias from model misspecification. We outline instrumental variable estimation methods based on the doubly robust machine learning framework. These methods enable us to employ various machine learning techniques for nuisance parameter estimation and deliver consistent estimates and fast rates of convergence for valid inference. We use these methods to estimate the primary target causal estimand in an IV design. Additionally, we expand these methods to develop estimators for heterogeneous causal effects, profiling principal strata, and a sensitivity analyses for a key instrumental variable assumption. We conduct a simulation study to demonstrate scenarios where more flexible estimation methods outperform standard methods. Our findings indicate that operative care is generally more effective for cholecystitis patients, although the benefits of surgery can be less pronounced for key patient subgroups.
A Survey on Figure Classification Techniques in Scientific Documents

Figures visually represent an essential piece of information and provide an effective means to communicate scientific facts. Recently there have been many efforts toward extracting data directly from figures, specifically from tables, diagrams, and plots, using different Artificial Intelligence and Machine Learning techniques. This is because removing information from figures could lead to deeper insights into the concepts highlighted in the scientific documents. In this survey paper, we systematically categorize figures into five classes - tables, photos, diagrams, maps, and plots, and subsequently present a critical review of the existing methodologies and data sets that address the problem of figure classification. Finally, we identify the current research gaps and provide possible directions for further research on figure classification.
Multiobjective Hydropower Reservoir Operation Optimization with Transformer-Based Deep Reinforcement Learning

Due to shortage of water resources and increasing water demands, the joint operation of multireservoir systems for balancing power generation, ecological protection, and the residential water supply has become a critical issue in hydropower management. However, the numerous constraints and nonlinearity of multiple reservoirs make solving this problem time-consuming. To address this challenge, a deep reinforcement learning approach that incorporates a transformer framework is proposed. The multihead attention mechanism of the encoder effectively extracts information from reservoirs and residential areas, and the multireservoir attention network of the decoder generates suitable operational decisions. The proposed method is applied to Lake Mead and Lake Powell in the Colorado River Basin. The experimental results demonstrate that the transformer-based deep reinforcement learning approach can produce appropriate operational outcomes. Compared to a state-of-the-art method, the operation strategies produced by the proposed approach generate 10.11% more electricity, reduce the amended annual proportional flow deviation by 39.69%, and increase water supply revenue by 4.10%. Consequently, the proposed approach offers an effective method for the multiobjective operation of multihydropower reservoir systems.
Earthquake observatory with coherent laser interferometry on the telecom fiber network

Laser interferometry enables to remotely measure microscopical length changes of deployed telecommunication cables originating from earthquakes. Long range and compatibility with data traffic make it unique to the exploration of remote regions, as well as highly-populated areas where optical networks are pervasive, and its large-scale implementation is attractive for both Earth scientists and telecom operators. However, validation and modeling of its response and sensitivity are still at an early stage and suffer from lack of statistically-significant event catalogs and limited availability of co-located seismometers. We implemented laser interferometry on a land-based telecommunication cable and analyzed 1.5 years of continuous acquisition, with successful detections of events in a broad range of magnitudes, including very weak ones. By comparing fiber and seismometer recordings we determined relations between a cable's detection probability and the magnitude and distance of events, and showed that spectral analysis of recorded data allows considerations on the earthquake dynamics. Our results reveal that quantitative analysis is possible for this sensing technique and support the interpretation of data from the growing amount of interferometric deployments. We anticipate the high integration and scalability of laser interferometry into existing telecommunication grids to be useful for the daily seismicity monitoring, in perspective exploitable for civilian protection use.
Ocular Adnexal Changes After Antiglaucoma Medication Use

Glaucoma is one of the leading causes of irreversible blindness worldwide, and the control of elevated intraocular pressure (IOP) requires medical treatment utilizing antiglaucomamedications (AGMs) that are the first line of treatment.1,2 However, there are some unwanted or adverse side effects of these AGMs on the ocular adnexa. Such effects include the development of periocular dermatitis, prostaglandin-associated orbitopathy, and eyelid malpositions.3 The etiology of eyelid changes and the inciting or underlying predisposing factors are unknown. Most AGMs that are applied to the ocular surface contain preservatives; however, preservative-free formulations are also available, and progressive ocular surface cicatricial changes have been observed after the use of preservative-free AGMs as well.4–6 In a few cases, extensive changes such as drug-induced cicatrizing conjunctivitis can simulate mucous membrane pemphigoid, which is visually debilitating and can be associated with trichiasis and cicatricial entropion.4 Clinicians need to be aware of these side effects because studies have shown that such side effects could negatively impact drug adherence and/or compliance.7 However, these side effects can be reversed by discontinuing the offending drug and changing to another class of drug, using preservative-free formulations, or switching to laser or surgical treatments, if detected early.8–10 This review summarizes the current knowledge about eyelid changes that develop secondary to chronic AGM use, excluding the effects on the ocular surface.
The State of Organization Interacting with the Surroundings. ERBV model.

The concept of the state is the base sentence in thermodynamics and mechanics. In economics and social science, and particularly in management science, it is a very difficult multidisciplinary issue. In fact, it is not even entirely clear whether such a term can be introduced at all. Management looks for good estimates of axiological evaluations, which favors a search for quantitative descriptions of instantaneous states of managed organizations. Inspiration may be drawn from statistical physics and thermodynamics. The study shows that a particularly promising approach may be a combination of the utility theory and resourcebased view (RBV). A description of the micromacro balance between an organization interacting with the surrounding, where parametric variables are inside and outside the utility, defines a resource constant characterizing the system and being useful for the manager. The state can be defined as a function of capital exchanged with the surrounding, determined value of the resources, and internal and external utility. The optimization of management can be based on determining the linear operator (transform) of the current utility to the optimum one in the same vector space.
Sheafifications of the de Rham-Witt complex

Recent important and powerful frameworks for the study of differential forms by Huber-Joerder and Huber-Kebekus-Kelly based on Voevodsky's h-topology have greatly simplified and unified many approaches. This article builds towards the goal of putting Illusie's de Rham-Witt complex in the same framework by exploring the h-sheafification of the rational de Rham-Witt differentials. Assuming resolution of singularities in positive characteristic one recovers a complete cohomological h-descent for all terms of the complex. We also provide unconditional h-descent for the global sections and draw the expected conclusions. The approach is to realize that a certain right Kan extension introduced by Huber-Kebekus-Kelly takes the sheaf of rational de Rham-Witt forms to a qfh-sheaf. As such, we state and prove many results about qfh-sheaves which are of independent interest.
Managing diversity: toward a globally inclusive workplace (fifth edition)

Relations 37 (1): 1–27. doi:10.1177/001872678403700101. Osborne, D. , and T. Gaebler. 1992. Reinventing Government: How the Entrepreneurial Spirit is Transforming the Public Sector. Boston: Addison-Wesley. Pedler, M. , J. Burgoyne, and C. Brook. 2005. “What Has Action Learning Learned to Become? Action Learning. ” Research & Practice 2 (1): 49–68. Polanyi, M. 1966. The Tacit Dimension. London: Routledge and Kegan Paul. Revans, R. 2011. The ABC of Action Learning. Farnham: Gower. Shotter, J. 1993. Conversational Realities: Constructing Life Through Language. Thousand Oaks, CA: Saga. Thompson, N. and J. Pascal. 2012. “Developing Critically Reflective Practice. ” Reflective Practice 13 (2): 311–325. doi:10.1080/14623943.2012. 657795.
The OSATE Slicer: Graph-Based Reachability for Architectural Models

Model-based specification of embedded, critical systems (i.e., systems whose failure is deemed unacceptable) is increasingly becoming the standard of practice. However, analysis of these models can prove challenging when the models grow in size and complexity, which is common as more types and volume of data are loaded into them. One well-studied technique for grappling with complexity in models is slicing , where slices of models are highlighted according to some criterion. In this work, we describe a new software tool: the OSATE Slicer , which extends the concept of slicing to architectural models of embedded, critical systems. It does this by calculating of various notions of reachability which can be used to support both manual and automated analyses of system models. We then evaluate the utility of our approach based on several existing analyses and its performance based on a pre-existing corpus of architectural models and find both to be satisfactory.
Piezoelectricity in screen-printed hydroxyapatite thick films

ABSTRACT The discovery of piezoelectricity in calcined thin films and sintered ceramics of synthetic hydroxyapatite is of interest to understand the role of electricity in bone growth and to find use of piezoelectric implants in bone growth stimulation or deceleration and in energy harvesting to run spinal or neuro stimulators. Synthetic apatites including hydroxyapatite and a mixture of hydroxyapatite and tricalcium phosphates are extensively used in bone graft substitute and cement-less implant coating. We provide experimental evidence of piezoelectricity in screen-printed thick-films of hydroxyapatite, which may allow many real-time experiments to understand physiological significance of piezoelectricity and potential in vivo exploitation.
Metabolic profiling reveals key metabolites regulating adventitious root formation in ancient Platycladus orientalis cuttings

Platycladus orientalis, a common horticultural tree species, has an extremely long life span and forms a graceful canopy. Its branches, leaves, and cones have been used in traditional Chinese medicine. However, difficulty in rooting is the main limiting factor for the conservation of germplasm resources. This study shows that the rooting rates and root numbers of cuttings were significantly reduced in ancient P. orientalis donors compared to 5-year-old P. orientalis donors. The contents of differentially accumulated metabolites (DAMs) in phenylpropanoid (caffeic acid and coniferyl alcohol) and flavonoid biosynthesis (cinnamoyl-CoA and isoliquiritigenin) pathways increased significantly in cuttings propagated from ancient P. orientalis donors compared to 5-year-old P. orientalis donors during adventitious root (AR) formation. These DAMs may prevent the ancient P. orientalis cuttings from rooting, and gradual lignification of callus was one of the main reasons for the failed rooting of ancient P. orientalis cuttings. The rooting rates of ancient P. orientalis cuttings were improved by wounding the callus to identify wounding-induced rooting-promoting metabolites. After wounding, the contents of DAMs in zeatin (5′-methylthioadenosine, cis-zeatin-O-glucoside, and adenine) and aminoacyl-tRNA biosynthesis (l-glutamine, l-histidine, l-isoleucine, l-leucine, and l-arginine) pathways increased, which might promote cell division and provided energy for the rooting process. The findings of our study suggest that breaking down the lignification of callus via wounding can eventually improve the rooting rates of ancient P. orientalis cuttings, which provides a new solution for cuttings of other difficult-to-root horticultural and woody plants.