Random deep neural networks are biased towards simple functions

We prove that the binary classifiers of bit strings generated by random wide deep neural networks with ReLU activation function are biased towards simple functions. The simplicity is captured by the following two properties. For any given input bit string, the average Hamming distance of the closest input bit string with a different classification is at least sqrt(n / (2{\pi} log n)), where n is the length of the string. Moreover, if the bits of the initial string are flipped randomly, the average number of flips required to change the classification grows linearly with n. These results are confirmed by numerical experiments on deep neural networks with two hidden layers, and settle the conjecture stating that random deep neural networks are biased towards simple functions. This conjecture was proposed and numerically explored in [Valle P\'erez et al., ICLR 2019] to explain the unreasonably good generalization properties of deep learning algorithms. The probability distribution of the functions generated by random deep neural networks is a good choice for the prior probability distribution in the PAC-Bayesian generalization bounds. Our results constitute a fundamental step forward in the characterization of this distribution, therefore contributing to the understanding of the generalization properties of deep learning algorithms

Effects Of Molar Ratio Of Iron Catalyst On Synthesis Of Carbon Nanotubes Via Catalytic Chemical Vapor Deposition

Research on the area of the synthesis of carbon nanotubes is fundamental and critical to the entire subject of carbon nanotubes. This dissertation describes an experiment to synthesize carbon nanotubes by the method of catalytic chemical vapor deposition (CCVD). It focuses on the relationship between the as-prepared catalyst and the synthesized carbon nanotubes. The effect of growth parameters for the synthesis of carbon nanotubes was also studied. The Fe-Mo-MgO catalysts with five different molar ratios of iron (Fe) in this composite catalyst were prepared through the impregnation method. The goal of this work was to identify the suitable molar ratio of iron (Fe) in the composite catalyst of Fe-Mo-MgO on which carbon nanotubes (CNTs) can be grown with a higher yield and quality.Scanning electron microscopy (SEM), transmission electron microscopy (TEM), xray diffraction (XRD), and thermogravimetric analysis (TGA) were used to characterize the as-prepared catalysts and as-grown carbon nanotube samples. Among these catalysts with different molar ratio of iron, the main and obvious observation in the synthesis of carbon nanotubes was the yield of synthesized carbon nanotubes. That is, increasing the molar ratio of iron, the yield of produced carbon nanotubes increases strongly, but the quality did not improve. While by decreasing the Fe concentration, both the structural defects and yield were reduced. Therefore, based on the experimental results, the best catalyst was catalyst 3 (Fe: Mo: MgO = 0.5: 0.1: 10) with a moderate molar ratio of iron. This catalyst not only had good yield but also good quality. The different parameters such as flow rate of argon (Ar) as a carrier gas, and temperature to improve the growth condition of CCVD method for the synthesis of CNTs by Fe-Mo-MgO catalyst were examined. It is found that the best flow rate for carrier gas is 100 ml/min. For the flow rate lower or higher than this, there were very few CNTs formed, since the low flow rate of Ar could not carry enough ethanol vapors through the reactor to be deposited on the catalyst. As for the high flow rate of Ar, most of the carbon source exited from the outlet of the reactor and again they could not be deposited on the catalyst, thus few carbon nanotubes were formed. In the synthesis of carbon nanotubes by CCVD method, the temperature plays a key role. The results show that when the temperature is lower than 750°C, few CNTs were formed, and when the temperature is higher than 900°C, more and more amorphous carbons were formed in the CNTs. The best temperature for the growth of carbon nanotubes by these catalysts is between 800°C and 900°C. The results showed that the growth of carbon nanotubes was significantly influenced by the reaction condition due to its sensitivity. The synthesis products were always a mixture of single-walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs)

Load-Deflection Behaviour of Frp Concrete Composite Deck

Nowadays, Fiber Reinforced Polymer (FRP) concrete composite bridge deck system hasbeen introduced because of its light-weight and durability. Strong composition is neededbetween FRP and concrete to acquire the structural composite behavior of FRP concretecomposite deck. FRP has unique properties that, if disregarded, can lead to failure duringoperation. However, when these same unique properties are taken into advantages, they canprovide the engineers with a system superior to traditional metallic materials. This studyinvestigates analytically the deflection behavior of FRP concrete composite deck using shearconnectors under flexural loading. Finite element software (LUSAS) is used to model FRPcomposite deck. For this purpose, LUSAS has introduced some elements. Volume elementsare utilized to model concrete and Glass Fiber Reinforced Polymer (GFRP) section. Meshingelements are necessary in finite element in order to act as a member in modeling. 3D solidcontinuum elements are used to mesh the sample. Five GFRP module having differentthicknesses of 8mm, 9.6mm, 11.2mm, 12.8mm and 16mm are taken to analyze. Results showthat the thicknesses of GFRP module have significant effect on the ultimate load anddeflection of the deck. Once the thickness of GFRP section increased, the deflection at midspan decreased and the ultimate load increased accordingly. Furthermore, results revealed theappropriate interface material between FRP and concrete in finite element modeling. In orderto get an effective interface element, about 40 numerical models have been analyzed. Theresults were compared with experimental study. Inserted data for verified model in LUSASwere demonstrated as an appropriate interface element between FRP and concrete

An expression signature of the angiogenic response in gastrointestinal neuroendocrine tumours: correlation with tumour phenotype and survival outcomes.

BACKGROUND: Gastroenteropancreatic neuroendocrine tumours (GEP-NETs) are heterogeneous with respect to biological behaviour and prognosis. As angiogenesis is a renowned pathogenic hallmark as well as a therapeutic target, we aimed to investigate the prognostic and clinico-pathological role of tissue markers of hypoxia and angiogenesis in GEP-NETs. METHODS: Tissue microarray (TMA) blocks were constructed with 86 tumours diagnosed from 1988 to 2010. Tissue microarray sections were immunostained for hypoxia inducible factor 1α (Hif-1α), vascular endothelial growth factor-A (VEGF-A), carbonic anhydrase IX (Ca-IX) and somatostatin receptors (SSTR) 1–5, Ki-67 and CD31. Biomarker expression was correlated with clinico-pathological variables and tested for survival prediction using Kaplan–Meier and Cox regression methods. RESULTS: Eighty-six consecutive cases were included: 51% male, median age 51 (range 16–82), 68% presenting with a pancreatic primary, 95% well differentiated, 51% metastatic. Higher grading (P=0.03), advanced stage (P<0.001), high Hif-1α and low SSTR-2 expression (P=0.03) predicted for shorter overall survival (OS) on univariate analyses. Stage, SSTR-2 and Hif-1α expression were confirmed as multivariate predictors of OS. Median OS for patients with SSTR-2+/Hif-1α-tumours was not reached after median follow up of 8.8 years, whereas SSTR-2-/Hif-1α+ GEP-NETs had a median survival of only 4.2 years (P=0.006). CONCLUSION: We have identified a coherent expression signature by immunohistochemistry that can be used for patient stratification and to optimise treatment decisions in GEP-NETs independently from stage and grading. Tumours with preserved SSTR-2 and low Hif-1α expression have an indolent phenotype and may be offered less aggressive management and less stringent follow up

Microclimate Monitoring Using Multivariate Analysis to Identify Surface Moisture in Historic Masonry in Northern Italy

Preserving historical porous materials requires careful monitoring of surface humidity to mitigate deterioration processes like salt crystallization, mold growth, and material decay. While microclimate monitoring is a recognized preventive conservation tool, its role in detecting surface-specific moisture risks remains underexplored. This study evaluates the relationship between indoor microclimate fluctuations and surface moisture dynamics across 13 historical sites in Northern Italy (Lake Como, Valtellina, Valposchiavo), encompassing diverse masonry typologies and environmental conditions. High-resolution sensors recorded temperature and relative humidity for a minimum of 13 months, and eight indicators&mdash;including dew point depression, critical temperature&ndash;humidity zones, and damp effect indices&mdash;were analyzed to assess the moisture risks. The results demonstrate that multivariate microclimate data could effectively predict humidity accumulation. The key findings reveal the impact of seasonal ventilation, thermal inertia, and localized air stagnation on moisture distribution, with unheated alpine sites showing the highest condensation risk. The study highlights the need for integrated monitoring approaches, combining dew point analysis, mixing ratio stability, and buffering performance, to enable early risk detection and targeted conservation strategies. These insights bridge the gap between environmental monitoring and surface moisture diagnostics in porous heritage materials

Police department scheduling system (PDSS)

The Police Department Scheduling System (PDSS) was designed to assist the Simi Valley Police Department (SVPD) in the scheduling of their personnel. The system simplifies the task of producing and maintaining personnel lists and quarterly schedules. When provided with minimal personnel data and quarterly schedules, the time consuming task of producing and maintaining daily watch reports is greatly simplified. Data validation and table look-ups are extensively employed to certify the integrity of the data and facilitate the burden of data entry. The system uses the latest tools in data base technology and provides a modern user interface. It is window based and allows the use of a mouse. The interface is also user-configured to a degree, and provides a non-threatening and visually appealing environment for the untrained computer user. It operates on the industry standard PC environment running the DOS operating system. Effort has been made to insure proper system operation with both color and monochrome monitors. The system uses printer effects to enhance the aesthetic appeal of the output and a variety of printers (dot-matrix, or lasers) are supported for output generation. Although PDSS is specifically developed to serve the needs of the Simi Valley Police Department, it may be modified with very little effort and used in other Police Departments.California State University, Northridge. Department of Computer Science.Includes bibliographical references (page 30