93 research outputs found
Formal degrees of genuine Iwahori-spherical representations
For square-integrable genuine Iwahori-spherical representations of central
covers, we verify the Hiraga--Ichino--Ikeda formula for their formal degrees.
We also compute the Whittaker dimensions of these representations, when their
associated modules over the genuine Iwahori--Hecke algebra are one-dimensional
Workflow-based Fast Data-driven Predictive Control with Disturbance Observer in Cloud-edge Collaborative Architecture
Data-driven predictive control (DPC) has been studied and used in various
scenarios, since it could generate the predicted control sequence only relying
on the historical input and output data. Recently, based on cloud computing,
data-driven predictive cloud control system (DPCCS) has been proposed with the
advantage of sufficient computational resources. However, the existing
computation mode of DPCCS is centralized. This computation mode could not
utilize fully the computing power of cloud computing, of which the structure is
distributed. Thus, the computation delay could not been reduced and still
affects the control quality. In this paper, a novel cloud-edge collaborative
containerised workflow-based DPC system with disturbance observer (DOB) is
proposed, to improve the computation efficiency and guarantee the control
accuracy. First, a construction method for the DPC workflow is designed, to
match the distributed processing environment of cloud computing. But the
non-computation overheads of the workflow tasks are relatively high. Therefore,
a cloud-edge collaborative control scheme with DOB is designed. The low-weight
data could be truncated to reduce the non-computation overheads. Meanwhile, we
design an edge DOB to estimate and compensate the uncertainty in cloud workflow
processing, and obtain the composite control variable. The UUB stability of the
DOB is also proved. Third, to execute the workflow-based DPC controller and
evaluate the proposed cloud-edge collaborative control scheme with DOB in the
real cloud environment, we design and implement a practical workflow-based
cloud control experimental system based on container technology. Finally, a
series of evaluations show that, the computation times are decreased by 45.19%
and 74.35% for two real-time control examples, respectively, and by at most
85.10% for a high-dimension control example.Comment: 58 pages and 23 figure
Hardness of Graph-Structured Algebraic and Symbolic Problems
In this paper, we study the hardness of solving graph-structured linear
systems with coefficients over a finite field and over a
polynomial ring .
We reduce solving general linear systems in to solving
unit-weight low-degree graph Laplacians over with a
polylogarithmic overhead on the number of non-zeros. Given the hardness of
solving general linear systems in [Casacuberta-Kyng 2022], this
result shows that it is unlikely that we can generalize Laplacian solvers over
, or finite-element based methods over in general, to
a finite-field setting. We also reduce solving general linear systems over
to solving linear systems whose coefficient matrices are walk
matrices (matrices with all ones on the diagonal) and normalized Laplacians
(Laplacians that are also walk matrices) over .
We often need to apply linear system solvers to random linear systems, in
which case the worst case analysis above might be less relevant. For example,
we often need to substitute variables in a symbolic matrix with random values.
Here, a symbolic matrix is simply a matrix whose entries are in a polynomial
ring . We formally define the reducibility
between symbolic matrix classes, which are classified in terms of the degrees
of the entries and the number of occurrences of the variables. We show that the
determinant identity testing problem for symbolic matrices with polynomial
degree and variable multiplicity at most is at least as hard as the
same problem for general matrices over .Comment: 57 pages, submitted version to STOC2
Structural parameters optimization of internal mixing air atomizing nozzle based on orthogonal experiment
In order to grasp the influence of structural parameters of internal mixing air atomizing nozzles on the atomization characteristics and dust reduction efficiency, so as to obtain economical and reasonable nozzle structure parameters, the self-designed and developed spray dust reduction experimental platform and the orthogonal design method was used to carry out experiment on nozzle atomization characteristics and dust reduction efficiency under the combination of structural parameters. The experimental results shown that, with the diameter of the liquid cap injection hole increased, the nozzle water flow rate increased, while the air flow rate decreased continuously. Nozzle air flow increased with the number of liquid cap injection hole, whereas nozzle water flow was less affected by the number of liquid cap injection hole. When the diameter of the water injection hole gradually increased, the Sauter Mean Diameter (SMD) increased continuously. SMD with the increase of the number of air injection holes shown a change law of first decrease and then increase, and the minimum value was reached when the number of air injection holes was 4, where the atomization effect was the best. When the air cap outlet diameter was 2.0 mm and 2.5 mm, the nozzle droplet size was smaller. With the increase of the diameter of the water injection holes and the number of air injection holes of the liquid cap, the dust reduction efficiency of total dust and respirable dust both first increased and then decreased, and the best effect of the dust reduction was obtained in the diameter of water injection holes of 1.5 mm and the number of air injection holes of 4, respectively. With the diameter of the air cap outlet increased, the dust reduction efficiency of both total dust and respirable dust increased, but the increase of the dust reduction efficiency was smaller when the diameter of the air cap outlet was greater than 2.0 mm. Comprehensively considering the nozzle atomization characteristics and dust reduction efficiency, for the nozzle air cap, the outlet diameter should be 2.0 mm, for the nozzle liquid cap, it was reasonable to use a water injection hole diameter of 1.5 mm and the number of air injection holes to be 4, which can obtain the highest dust reduction efficiency. It is more reasonable to use the nozzles with the combination of above structure parameters for industrial applications, which can obtain smaller droplet size and higher dust reduction efficiency with lower air and water consumption
Rethink Baseline of Integrated Gradients from the Perspective of Shapley Value
Numerous approaches have attempted to interpret deep neural networks (DNNs)
by attributing the prediction of DNN to its input features. One of the
well-studied attribution methods is Integrated Gradients (IG). Specifically,
the choice of baselines for IG is a critical consideration for generating
meaningful and unbiased explanations for model predictions in different
scenarios. However, current practice of exploiting a single baseline fails to
fulfill this ambition, thus demanding multiple baselines. Fortunately, the
inherent connection between IG and Aumann-Shapley Value forms a unique
perspective to rethink the design of baselines. Under certain hypothesis, we
theoretically analyse that a set of baseline aligns with the coalitions in
Shapley Value. Thus, we propose a novel baseline construction method called
Shapley Integrated Gradients (SIG) that searches for a set of baselines by
proportional sampling to partly simulate the computation path of Shapley Value.
Simulations on GridWorld show that SIG approximates the proportion of Shapley
Values. Furthermore, experiments conducted on various image tasks demonstrate
that compared to IG using other baseline methods, SIG exhibits an improved
estimation of feature's contribution, offers more consistent explanations
across diverse applications, and is generic to distinct data types or instances
with insignificant computational overhead.Comment: 12 page
Less is More: Learning Reference Knowledge Using No-Reference Image Quality Assessment
Image Quality Assessment (IQA) with reference images have achieved great
success by imitating the human vision system, in which the image quality is
effectively assessed by comparing the query image with its pristine reference
image. However, for the images in the wild, it is quite difficult to access
accurate reference images. We argue that it is possible to learn reference
knowledge under the No-Reference Image Quality Assessment (NR-IQA) setting,
which is effective and efficient empirically. Concretely, by innovatively
introducing a novel feature distillation method in IQA, we propose a new
framework to learn comparative knowledge from non-aligned reference images. And
then, to achieve fast convergence and avoid overfitting, we further propose an
inductive bias regularization. Such a framework not only solves the congenital
defects of NR-IQA but also improves the feature extraction framework, enabling
it to express more abundant quality information. Surprisingly, our method
utilizes less input while obtaining a more significant improvement compared to
the teacher models. Extensive experiments on eight standard NR-IQA datasets
demonstrate the superior performance to the state-of-the-art NR-IQA methods,
i.e., achieving the PLCC values of 0.917 (vs. 0.884 in LIVEC) and 0.686 (vs.
0.661 in LIVEFB)
GNG12 as A Novel Molecular Marker for the Diagnosis and Treatment of Glioma
PurposeGNG12 influences a variety of tumors; however, its relationship with glioma remains unclear. The aim of this study was to comprehensively investigate the relationship between GNG12 and the clinical characteristics and prognosis of glioma patients and reveal the mechanisms causing the malignant process of GNG12.Materials and MethodsWe obtained information on clinical samples from multiple databases. The expression level of GNG12 was validated using a RT-qPCR and IHC. KM curves were used to assess the correlation between the GNG12 expression and OS of glioma patients. An ROC curve was drawn to assess the predictive performance of GNG12. Univariate and multivariate Cox analyses were performed to analyze the factors affecting the prognosis of patients with glioma. GSEA and TIMER databases were used to estimate the relationship between GNG12 expression, possible molecular mechanisms, and immune cell infiltration. CMap analysis was used to screen candidate drugs for glioma. Subsequent in vitro experiments were used to validate the proliferation and migration of glioma cells and to explore the potential mechanisms by which GNG12 causes poor prognosis in gliomas.ResultsGNG12 was overexpressed in glioma patients and GNG12 expression level correlated closely with clinical features, including age and histological type, etc. Subsequently, the K-M survival analysis indicated that the expression level of GNG12 was relevant to the prognosis of glioma, and the ROC curve implied that GNG12 can predict glioma stability. Univariate and multivariate analyses showed that GNG12 represents a risk factor for glioma occurrence. GNG12 expression is closely associated with some immune cells. Additionally, several in vitro experiments demonstrated that down-regulation of GNG12 expression can inhibits the proliferation and migration capacity of glioma cells. Ultimately, the results for the GSEA and WB experiments revealed that GNG12 may promote the malignant progression of gliomas by regulating the cell adhesion molecule cell signaling pathway.ConclusionIn this study, we identified GNG12 as a novel oncogene elevated in gliomas. Reducing GNG12 expression inhibits the proliferation and migration of glioma cells. In summary, GNG12 can be used as a novel biomarker for the early diagnosis of human gliomas and as a potential therapeutic target
An Experimental Study on the Establishment of Pulmonary Hypertension Model in Rats induced by Monocrotaline
Pulmonary hypertension is called PH for short. It is caused by the pulmonary artery vascular disease leading to pulmonary vascular resistance, and the increase right lung compartment load, which resulting in weakening or even collapse of the right ventricular function. The establishment of rat PH model under the action of monocrotaline is a repeatable, simple and accessible operation technique, which has been widely used in the treatment of pulmonary hypertension. This paper discusses the principle and properties of the PH model on rats under the monocrotaline action
Biological Bone Micro Grinding Temperature Field under Nanoparticle Jet Mist Cooling
Clinical neurosurgeons used micro grinding to remove bone tissues, and drip irrigation-type normal saline (NS) is used with low cooling efficiency. Osteonecrosis and irreversible thermal neural injury caused by excessively high grinding temperature are bottleneck problems in neurosurgery and have severely restricted the application of micro grinding in surgical procedures. Therefore, a nanoparticle jet mist cooling (NJMC) bio-bone micro grinding process is put forward in this chapter. The nanofluid convective heat transfer mechanism in the micro grinding zone is investigated, and heat transfer enhancement mechanism of solid nanoparticles and heat distribution mechanism in the micro grinding zone are revealed. On this basis, a temperature field model of NJMC bio-bone micro grinding is established. An experimental platform of NJMC bio-bone micro grinding is constructed, and bone micro grinding force and temperatures at different measuring points on the bone surface are measured. The results indicated that the model error of temperature field is 6.7%, theoretical analysis basically accorded with experimental results, thus certifying the correctness of the dynamic temperature field in NJMC bio-bone micro grinding
Local Composite Quantile Regression Smoothing for Harris Recurrent Markov Processes
In this paper, we study the local polynomial composite quantile regression (CQR) smoothing method for the nonlinear and nonparametric models under the Harris recurrent Markov chain framework. The local polynomial CQR regression method is a robust alternative to the widely-used local polynomial method, and has been well studied in stationary time series. In this paper, we relax the stationarity restriction on the model, and allow that the regressors are generated by a general Harris recurrent Markov process which includes both the stationary (positive recurrent) and nonstationary (null recurrent) cases. Under some mild conditions, we establish the asymptotic theory for the proposed local polynomial CQR estimator of the mean regression function, and show that the convergence rate for the estimator in nonstationary case is slower than that in stationary case. Furthermore, a weighted type local polynomial CQR estimator is provided to improve the estimation efficiency, and a data-driven bandwidth selection is introduced to choose the optimal bandwidth involved in the nonparametric estimators. Finally, we give some numerical studies to examine the finite sample performance of the developed methodology and theory
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