Stochastic Control via Entropy Compression

We consider an agent trying to bring a system to an acceptable state by repeated probabilistic action. Several recent works on algorithmizations of the Lovasz Local Lemma (LLL) can be seen as establishing sufficient conditions for the agent to succeed. Here we study whether such stochastic control is also possible in a noisy environment, where both the process of state-observation and the process of state-evolution are subject to adversarial perturbation (noise). The introduction of noise causes the tools developed for LLL algorithmization to break down since the key LLL ingredient, the sparsity of the causality (dependence) relationship, no longer holds. To overcome this challenge we develop a new analysis where entropy plays a central role, both to measure the rate at which progress towards an acceptable state is made and the rate at which noise undoes this progress. The end result is a sufficient condition that allows a smooth tradeoff between the intensity of the noise and the amenability of the system, recovering an asymmetric LLL condition in the noiseless case.Comment: 18 page

ExpPoint-MAE: Better interpretability and performance for self-supervised point cloud transformers

In this paper we delve into the properties of transformers, attained through self-supervision, in the point cloud domain. Specifically, we evaluate the effectiveness of Masked Autoencoding as a pretraining scheme, and explore Momentum Contrast as an alternative. In our study we investigate the impact of data quantity on the learned features, and uncover similarities in the transformer's behavior across domains. Through comprehensive visualiations, we observe that the transformer learns to attend to semantically meaningful regions, indicating that pretraining leads to a better understanding of the underlying geometry. Moreover, we examine the finetuning process and its effect on the learned representations. Based on that, we devise an unfreezing strategy which consistently outperforms our baseline without introducing any other modifications to the model or the training pipeline, and achieve state-of-the-art results in the classification task among transformer models

SHREC’20 Track:Retrieval of digital surfaces with similar geometric reliefs

International audienceThis paper presents the methods that have participated in the SHREC'20 contest on retrieval of surface patches with similar geometric reliefs and 1 the analysis of their performance over the benchmark created for this challenge. The goal of the context is to verify the possibility of retrieving 3D models only based on the reliefs that are present on their surface and to compare methods that are suitable for this task. This problem is related to many real world applications, such as the classification of cultural heritage goods or the analysis of different materials. To address this challenge, it is necessary to characterize the local "geometric pattern" information, possibly forgetting model size and bending. Seven groups participated in this contest and twenty runs were submitted for evaluation. The performances of the methods reveal that good results are achieved with a number of techniques that use different approaches

Buck-Boost Charge Pump Based DC-DC Converter

This paper presents a novel inductorless dual-mode buck-boost charge pump (CP) based DC-DC converter. The proposed architecture allows the same circuit to accomplish two modes of operation, buck and boost, for degrading or elevating the output voltage, respectively, compared to the input. To achieve each mode, only a switching of the input–output connections is needed without any other modification in the design of the DC-DC converter. The dual-mode configuration aims to merge two different functions into one circuit, minimizing the design time and the area the DC-DC converter occupies on the die. The proposed buck-boost CP has been designed using TSMC 65 nm complementary metal–oxide–semiconductor (CMOS) technology. The functional input voltage range of the CP in boost mode is 1.2 V to 1.8 V and the typical output voltage is 1.8 V. For the buck mode, the input voltage range is 3.2 V to 3.6 V and the output is 1.5 V. For both modes, the output can be easily modified to new values by changing the comparator configuration. Efficiency results are also provided for the two modes

An Accurate Bandgap Voltage Reference Ready-Indicator

An accurate indication that the bandgap voltage reference (BGR) circuit is settled on its nominal value is essential in analog or mixed signal systems. In this paper, a generic method for accurate bandgap voltage reference (BGR) Ready-Indication (RI) is proposed. A RI signal shows that the BGR operates correctly but also is used to enable PoR circuits when those employ the bandgap voltage to generate the required thresholds. In many low power applications, several systems are periodically switched on and off to save power and to increase battery life. In such systems, most circuits must remain off, while BGR is not in ready mode, saving battery energy. In other critical applications, the Ready-Indicator can ensure that critical systems can be set on and the reference voltage is within the operating range. In this paper, the introduced methodology is applied to different reference voltage generators. Initially, a BGR with Ready-Indication is presented extensively, including post-layout simulations results in 22 nm, while thereafter, the study of this method is extended in other BGR topologies. The universality of the proposed method is proved by verifying the operation in all the BGR topologies under study covering designs in 22 nm and 65 nm and with supply voltages 1.8 V, 1 V, 0.55 V

Expression of CTGF and TNFa in alveolar macrophages of patients with idiopathic pulmonary fibrosis before and after treatment with azathioprine or interferon-γ-1b

SUMMARY.Backgrou nd: Idiopathic pulmonary fibrosis (IPF) is a fatal lungdisorder the aetiology of which is unknown and for which there isno effective therapy. Connective tissue growth factor (CTGF) andtumour necrosis factor alpha (TNFα) have been reported to participatesignificantly in the pathogenesis of the disease. The role of alveolarmacrophages in the expression of these cytokines remains unclear.Materials and Methods: Samples of bronchoalveolar lavagefluid (BALF) derived from 20 newly diagnosed patients with IPF beforeand after 6 months of treatment with either interferon (IFN-γ-1b) andprednisolone (10 patients) or azathioprine (AZA) and prednisolone(10 patients) and from 10 normal subjects (control group) were usedfor the analysis of CTGF and TNFα protein expression in the alveolarmacrophages. The effectiveness of the two drug regimes on thepulmonary function tests (FEV1, FVC, DLCO) and PaO2 and PaCO2 ofthe patients with IPF was investigated. Results: Decreased CTGFprotein expression was detected in the patients with IPF comparedwith the control group (p=0.001). TNFα expression in IPF patientsdid not differ from that of the normal control subjects. Neither ofthe drug regimes affected the protein expression of these factorsorthe pulmonary function parameters. Co nclusion: These findingssuggest that the alveolar macrophages are not the main source ofCTGF and TNFα in IPF. Treatment with either AZA or IFN-γ-1b did notresult in any significant change in the protein expression of thesefactors. Pneumon 2011, 24(2):149-156

Expression of Hypoxia-Inducible Factor (HIF)-1a-Vascular Endothelial Growth Factor (VEGF)-Inhibitory Growth Factor (ING)-4- axis in sarcoidosis patients

Abstract Background Sarcoidosis is a granulomatous disorder of unknown etiology. The term of immunoangiostasis has been addressed by various studies as potentially involved in the disease pathogenesis. The aim of the study was to investigate the expression of the master regulator of angiogenesis hypoxia inducible factor (HIF)-1a – vascular endothelial growth factor (VEGF)- inhibitor of growth factor 4-(ING4) - axis within sarcoid granuloma. Methods A total of 37 patients with sarcoidosis stages II-III were recruited in our study. Tissue microarray technology coupled with immunohistochemistry analysis were applied to video-assisted thoracoscopic surgery (VATS) lung biopsy samples collected from 37 sarcoidosis patients and 24 controls underwent surgery for benign lesions of the lung. Computerized image analysis was used to quantify immunohistochemistry results. qRT-PCR was used to assess HIF-1a and ING4 expression in 10 sarcoidosis mediastinal lymph node and 10 control lung samples. Results HIF-1a and VEGF-ING4 expression, both in protein and mRNA level, was found to be downregulated and upregulated, respectively, in sarcoidosis samples compared to controls. Immunohistochemistry coupled with computerized image analysis revealed minimal expression of HIF-1a within sarcoid granulomas whereas an abundant staining of ING4 and VEGF in epithelioid cells was also visualized. Conclusions Our data suggest an impairment of the HIF-1a – VEGF axis, potentialy arising by ING4 overexpression and ultimately resulting in angiostasis and monocyte recruitment within granulomas. The concept of immunoangiostasis as a possible protection mechanism against antigens of infectious origin needs further research to be verified.</p

SHREC 2022: Fitting and recognition of simple geometric primitives on point clouds

This paper presents the methods that have participated in the SHREC 2022 track on the fitting and recognition of simple geometric primitives on point clouds. As simple primitives we mean the classical surface primitives derived from constructive solid geometry, i.e., planes, spheres, cylinders, cones and tori. The aim of the track is to evaluate the quality of automatic algorithms for fitting and recognising geometric primitives on point clouds. Specifically, the goal is to identify, for each point cloud, its primitive type and some geometric descriptors. For this purpose, we created a synthetic dataset, divided into a training set and a test set, containing segments perturbed with different kinds of point cloud artifacts. Among the six participants to this track, two are based on direct methods, while four are either fully based on deep learning or combine direct and neural approaches. The performance of the methods is evaluated using various classification and approximation measures