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یادگیری برنامه درسی

عنوان انگلیسی مقاله:

Curriculum Learning

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ارائه شده از دانشگاه گروه IRO، مونترال
نویسندگان Yoshua Bengio, Jérôme Louradour, Ronan Collobert, Jason Weston
رفرنس دارد 
کد محصول ۹۲۱۸
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فهرست مطالب

چکیده

۱- مقدمه

۲- نکاتی در خصوص مسئله بهینه سازی سخت یادگیری شبکه های عصبی عمیق

۳- برنامه درسی به عنوان روش مستمر

۴- آزمایشات اسباب بازی با معیار محدب

۴-۱ مثال های شفاف تر به تعمیم بهتر و سریع تر کمک می کنند

۴-۲ معرفی تدریجی مثال های سخت تر موجب تسریع آموزش انلاین می شود

۵- آزمایشاتی بر روی تشخیص شکل

۶- آزمایشات بر روی مدل سازی زبان

۶-۱ معماری

۶-۲ آزمایشات

۷- بحث و کار های آینده

 

بخشی از ترجمه

چکیده

انسان ها و حیوانات زمانی بهتر یاد می گیرند که مثال هایی به طور سازمان دهی شده و به شکلی معنی دار( نه به طور تصادفی) به آن ها ارایه شود که به تدریج این مثال ها موجب شفاف تر شدن تدریجی مفاهیم پیچیده شود. در این جا ما با این راهبرد های آموزشی در زمینه یادگیری ماشینی آشنا شده و آن ها را یادگیری برنامه آموزشی می نامیم. در مطالعات تحقیقاتی اخیر، سختی آموزش در حضور معیار های آموزشی غیر محدب ( برای شبکه های عصبی تصادفی و قطعی عمیق)، ما به بررسی آموزش برنامه درسی در شرایط مختلف می پردازیم.آزمایشات نشان می دهند که پیشرفت های قابل توجهی را می توان در زمینه تعمیم و کلیت بخشی حاصل کرد. فرضیه ما این است که آموزش برنامه درسی هم بر روی سرعت همگرایی فرایند آموزشی به میزان حداقل اثر داشته و هم در حضور معیار های غیر محدب، بر روی کیفیت کمینه محلی اثر دارد. آموزش برنامه درسی یک شکل ویژه از روش مستمر است( یک راهبرد عمومی برای بهینه سازی جهانی توابع غیر محدب است).

 

۷- بحث و کار های آینده

ما با یک سوالی شروع می کنیم که از مطالعات علوم شناختی قبلی هنوز به صورت بی پاسخ مانده است( المان ۱۹۹۳، راد و پلات ۱۹۹۹):آیا الگوریتم های یادگیری ماشینی می توانند از راهبرد برنامه درسی منتفع شوند؟ نتایج آزمایش ما در شرایط مختلف شواهدی را در خصوص پاسخ مثبت به این سوال نشان می دهد. به این ترتیب برخی از راهبرد های برنامه درسی بهتر از راهبرد های دیگر هستند و برخی نیز برای کار های دیگر بی اهمیت هستند( راد و پلات ۱۹۹۹) و این که نتایج بهتر را می توان با مجموعه داده های مربوط به راهبرد های آموزشی مناسب تر بدست اورد. با این همه، هنر آموزش سخت است و انسان ها در مورد ترتیب معرفی مطالب به دانش اموزان به توافق نرسیده اند. از دیدگاه یادگیری ماشینی، سوالات مهم به صورت چرایی و چگونگی است. این برای کمک به طراحی بهتر راهبرد های برنامه درسی در راستای خودکار سازی فرایند مهم است. در این جا فرضیاتی برای توجیه مزایای بالقوه راهبرد برنامه اموزشی ارایه می شود

 

بخشی از مقاله انگلیسی

Abstract

Humans and animals learn much better when the examples are not randomly presented but organized in a meaningful order which illustrates gradually more concepts, and gradually more complex ones. Here, we formalize such training strategies in the context of machine learning, and call them “curriculum learning”. In the context of recent research studying the difficulty of training in the presence of non-convex training criteria (for deep deterministic and stochastic neural networks), we explore curriculum learning in various set-ups. The experiments show that significant improvements in generalization can be achieved. We hypothesize that curriculum learning has both an effect on the speed of convergence of the training process to a minimum and, in the case of non-convex criteria, on the quality of the local minima obtained: curriculum learning can be seen as a particular form of continuation method (a general strategy for global optimization of nonconvex functions).

 

۷ Discussion and Future Work

We started with the following question left from previous cognitive science research (Elman, 1993; Rohde & Plaut, 1999): can machine learning algorithms benefit from a curriculum strategy? Our experimental results in many different settings bring evidence towards a positive answer to that question. It is plausible that some curriculum strategies work better than others, that some are actually useless for some tasks (as in Rohde and Plaut (1999)), and that better results could be obtained on our data sets with more appropriate curriculum strategies. After all, the art of teaching is difficult and humans do not agree among themselves about the order in which concepts should be introduced to pupils. From the machine learning point of view, once the success of some curriculum strategies has been established experimentally, the important questions are: why? and how? This is important to help us devise better curriculum strategies and maybe automate that process to some extent. Here we proposed a number of hypotheses to explain the potential advantages of a curriculum strategy:

 

 

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یادگیری برنامه درسی

عنوان انگلیسی مقاله:

Curriculum Learning

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INTRODUCTION

Biological soil crusts, also called `cryptobiotic’, `microbiotic’, `microphytic’ or `cyanobacterial±lichen’ soil crusts, are a dominant feature of most semiarid and arid landscapes throughout the world. These crusts di€er in species composition and occur on a variety of soils. As a result, crustal function in di€erent geographic regions might vary in regard to ecological processes such as rainfall in®ltration and seedling establishment (Harper and Marble, 1989; Johansen, 1993; West, 1990). However, most studies agree that biological soil crusts reduce wind erodibility of soil surfaces (Leys, 1990; MacKenzie and Pearson, 1979; Williams, et al., 1995), although one study found no signi®cant di€erences (Andrew and Lange, 1986). Scanning electron microscope studies done by Belnap and Gardner (1993) show that the extracellular sheath material of cyanobacteria bind soil particles together, providing soil surface protection. Biological soil crusts are highly susceptible to disturbance, especially in soils with low aggregate stability such as sands (Belnap and Gardner, 1993; Gillette, et al., 1980; Webb and Wilshire, 1983). Cyanobacterial ®laments, lichens and mosses are brittle when dry, and crush easily when subjected to compressional or shear forces by activities such as trampling or vehicular trac. Because crustal organisms are only metabolically active when wet, re-establishment time is slow in arid systems. While cyanobacteria are mobile, and can often move up through disturbed sediments to reach light levels needed for photosynthesis, lichens and mosses are incapable of such movement and often die as a result. On newly disturbed surfaces, mosses and lichens often have extremely slow colonization and growth rates. Assuming adjoining soils are stable and rainfall is average, recovery rates for lichen cover in southern Utah has been most recently estimated at a minimum of 45 years, while recovery of moss cover was estimated at 250 years (Belnap, 1993). Due to this slow recolonization of soil surfaces by the di€erent crustal components, crusts can be found in many stages of development. Wind is a major erosive force in deserts where there is little organic matter or vegetation cover to protect soil surfaces. Soil deposition by wind often exceeds that of ¯uvial deposition in these drier regions (Goudie, 1978; Williams, et al., 1995). Sediment production from soil surfaces depends on the force of wind needed to detach particles from soil surfaces (threshold friction velocity). Since wind erosion is of major concern both in the western USA and worldwide (Dregne, 1983), it is important to understand how soil surface disturbance a€ects threshold velocities. While previous studies have addressed the role soil crusts play in stabilizing desert soil surfaces, none has examined how threshold velocities might vary between stages of crustal development or how disturbance might di€erentially in¯uence various crustal types. The purpose of this study was to determine typical threshold velocities for di€erent stages of biological soil crust development and to determine the e€ects of di€erent soil surface disturbances on various stages of crustal development.

METHODS

The study site was located approximately 16 km south of Moab, Utah, USA, in Rizzo sandy loam soils. The dominant vegetation type is pinyon and juniper at an elevation of 1400 m. Annual precipitation is 250 mm, with 30 per cent of the rainfall occurring as late summer monsoons. Treatments were applied and measurements taken in July 1995 when soils were dry. All areas tested were located within a 300 m circle, with the same substrate type, soil depth and slope. Soils were collected and analyzed for sand, silt and clay content. Biological soil crust development was placed in one of four time categories, based on previous experiments regarding recovery rates after disturbance from four-wheel vehicles or foot trac (Belnap, 1993, unpublished data). These included: (a) Class 0: bare sand, with no visible biological crustal development, indicating very recent disturbance from vehicle or foot trac. (b) Class 1: ¯at crusts, with no visible frost heaving or lichen cover and low cyanobacterial biomass, indicating disturbance from vehicles or foot trac within one year of observation. (c) Class 2: moderately bumpy biological crusts with no lichen or moss development and moderate cyanobacterial biomass levels, indicating vehicular or foot trac disturbances 5±۱۰ years prior to observation. (d) Class 3: biological crusts were very bumpy, with full lichen and moss development and high cyanobacterial biomass, indicating no vehicular or foot trac disturbance for at least 20 years. Friction threshold velocities for movement of loose sand particles on the undisturbed surface (CON in Figure 2), and surface integrity of the crusts (SI in Figure 3) were determined for each crust type at two replicated sites. The FTV for particle movement was de®ned as the friction velocity at which surface particles were both detached from the soil surface and carried away by the generated wind. The FTV for surface integrity was the friction velocity at which large, intact chunks of the surface were detached and blown away. Because wind stress equals the square of friction velocity times the density of air, relative resistances of the di€erent crustal classes to wind erosion are de®ned and reported as the square of the ratio of threshold friction velocities between the classes being compared. Once FTVs were determined for the di€erent undisturbed crustal classes, disturbance treatments were applied to each crust class at each site. These treatments included: (1) Treatment F1: one pass by walking on crusts with lug-soled boots. (2) Treatment V1: one pass of a four-wheel drive vehicle with knobbed tires. (3) Treatment V2: two passes of a four-wheel drive vehicle with knobbed tires. Comparisons across the three crustal classes were done using a two-way ANOVA and multiple range test. T-tests were used to distinguish between disturbance treatments and controls.

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۱٫ Introduction

Environmental degradation is a major contemporary globally issue that is bedeviling most parts of the arid environment. This is due to the several centuries of poor management of the earth’s natural resources, and the increasing pressure associated with rapid population growth. Degradation of natural resources especially vegetation is most felt in the fragile environment of arid regions of the world which is home to several millions of people (United Nations Environment Programm (UNEP), 2008; Federal Ministry of Environment (FME), 2008). While drought and desertification are largely natural occurrence (Oladipo, 1993) these phenomena are significantly exacerbated by anthropogenic factors particularly farming, grazing and tree felling. Desertification, as defined in Chapter 12 of “Agenda 21”, Rio declaration on environment and development “is the degradation of the land in arid, semi-arid and sub-humid dry areas resulting from various factors, including climatic variations and human activities” (UNICED, 1992). It reduces the ability of land to support many life forms, limiting biodiversity and constraining socio-economic activities and development. It also stimulates accelerated soil erosion and deposition particularly by wind. Open land with little or no vegetal cover is highly vulnerable to both wind and water erosion (Cao, et al. 2008). In the absence of vegetation, rain water is mainly disposed as runoff due to minimal infiltration rate. This further constrains the growth and productivity of plants. Even long-lived plants: trees, shrubs and other perennials that would normally survive droughts eventually find survival an insurmountable challenge. A reduction in plant cover also results in lowering the quantity of humus in the soil, and plant productivity drops further (Adesina, 2008). As protective plant cover disappears, the probability of flash floods becomes higher and further degradation occurs. Thus, in general terms, desertification is self-reinforcing, that is, once the process starts it creates conditions for self sustenance. Desertification and drought have severe impacts on food security, livelihood, socio-economic and cultural activities of the region where they occur. It is accompanied by a reduction in the natural potential of the land and depletion in surface and ground-water resources. Desertification is not only a feature of natural deserts, but also takes place on land which is exposed to persistent drought and human pressure. In West Africa and in Nigeria in particular, food insecurity associated with desertification is mounting (UNEP, 2008). This is causing significant socio-economic challenges including mass movement of people from arid environment and reduced supply of food as well as disruption in the economic and social activities of people living in semi-arid environment (National Action Plan (NAP), 2007). Shelterbelt has been adjudged as one of the effective ecological means of contending the effects of desertification in an arid environment. Shelterbelts are rows of trees planted perpendicular to the direction of the prevailing winds to reduce their velocities. Igugu and Osemeobo (1991) reported that between 1963 and 1989 over 236,500 hectares of shelterbelts were established in the States threatened by desertification in Nigeria. This coverage of shelterbelts apart from being inadequate has sadly been allowed to deteriorate by the activities of farmers, loggers and fuelwood extractors. The Nigerian Forest Policy specifies that at least 20% of the country’s total land area should be reserved as forest estates to effectively fight environmental degradation (Odigie and Obiaga, 1991; FAO, 2005). Considering the magnitude of shelterbelt development needed to fight the menace of desertification in Nigeria, both government and people living in the arid and semi-arid region need to actively rise up to the challenges. In semi-arid regions, the short rainy season provides limited opportunities for plants to grow. This can however be exploited to grow and nurture adaptable varieties of plants in afforestation projects. Afforestation is the deliberate planting of trees where it has not existed before or where original tree cover had been removed. Without afforestation and support for the trees, it is almost impracticable to have re-establishment of tree cover in most tropical arid lands. Growing seasons are short and wild grazing is uncontrolled. In the event that natural regrowths happen, removal by grazing animals makes the plants short-lived. Afforestation can be achieved by establishing shelterbelts, increasing densities of farm-trees, planting live fences and by establishing woodlot (Igboanugo, 1991). Among these afforestation strategies, shelterbelts are the most effective and environmentally friendly approach to achieving success in combating desertification in the arid and semi-arid environments. Shelterbelts are established both to ensure their survival and enhance their capacity to yield optimum environmental benefits. Apart from the ecological and socio-economic benefits that they provide, shelterbelts reduce wind velocities as well as modify micro-climates (Ojo, et al., 1987; Igugu and Osemeobo, 1991; Wang and Takle, 1996; Mohammed, et al., 1996; Cornelis and Gabriels, 2005; Torita and Satou, 2007). In particular, the planting of shelterbelts in rows perpendicular to the direction of the prevailing winds, makes it possible for the trees to act as windbreak and so protect vegetation, and soils on the lee side against wind damages. Shelterbelt also improves the microclimatic conditions by the cooling effect of transpiration of the trees and conservation of available water resources. In this way, shelterbelts provide ‘safe sites’ for other plants to thrive through the process of establishment and succession (Pascal, 2003). They can thus become “growth” areas for the expansion of vegetation cover in the area. Among the specific objectives of establishing shelterbelts in Nigeria as highlighted by Igugu and Osemeobo, (1991) are to: i. Create windbreaks against high winds, check moving sand dunes and create conducive microclimate for sustainable agricultural production and recreation. ii. Provide habitat for small animals including migratory birds; iii. Improve soil productivity and carrying capacities of biotic and abiotic resources. Shelterbelts are established on communal lands and in forest reserves. The traditional shelterbelts in Nigeria are composed of pure stands of Azadirachta indica (neem tree) or Eucalyptus camaldulensis with ten rows of trees in an escapement of 2.5 m x 2.5 m to give a size of 200 m x 30 m per belt (Okefiena, 1988). Shelterbelts are being used to curb the expansion of desert condition and reduce the subsisting impacts of aridity in northern region. It is crucial that these shelterbelts are successfully established otherwise the objectives behind them may never be realized. This study assesses the influence of some selected shelterbelts in arid environment of Yobe State, Nigeria on vegetation characteristics. This is with intention of identifying its significance in fighting desert encroachment.

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دانلود رایگان ترجمه مقاله ردیابی فلزات در خاک و گیاهان اطراف کارخانه سیمان در پرتوریا – Pjoes 2015

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Introduction

Metal pollution of soil dust and agricultural soils arising from industrial activities, vehicular emissions, and waste disposal sites are well documented [1-3]. The cement industry forms part of the industries that are well known to be problematic as regards the introduction of heavy metals from the dust emanating from their operations [1-3]. The deposition of these trace metals occurred at various distances around the cement factories and are influenced by wind velocity, particle size, and stack fumes [4]. Typical raw cement is made up of 25 mg/kg Cr, 21 mg/kg Cu, 20 mg/kg Pb, and 53 mg/kg Zn [5]. Further to this elemental composition, it was also reported that about 0.07 kg of dust is generated into the atmosphere when 1 kg of cement is manufactured [2]. Soil contamination by heavy metals can cause longterm problems on the biogeochemical cycle, which may affect soil functioning systems, leading to changes in soil fauna [6]. From previous studies in other countries, it has been established that dust containing elevated amounts of trace metals emanating from the vicinity of cement factories may adversely affect humans, plants, and soil composition within the vicinity [7]. Most cement factories have been noted as potential sources of metals such as Hg, Zn, Pb, Cr, and Cd [8-11]. The effects and concentrations of the dust containing trace metals as pollutants vary and depend largely on technology employed from the cement industries to ameliorate environmental degradation. In humans, trace metals such as Pb may affect the brain and cause retarded growth, especially in children [12]. In plants, excessive [Pb] alters normal metabolic pathways by disrupting specific cellular enzymes and may also inhibit the photosynthetic ability of plants [13]. On a general note, excessive levels of heavy metals may result in the induction of oxidation stress, damage to DNA, and disturbances in the biosynthetic pathways [14]. Quality of the environment is vital for sustainable development, especially in the face of rapid developmental programs from developing countries. The rapid economic developments in South Africa over the past few years have resulted in an increased demand for cement production [15], which stood at 14.9 million tons in 2012 and is expected to reach 18.1 million tons in 2018 owing to the emergence of new cement manufacturing plants in South Africa and neighbouring countries such as Lesotho, Botswana, and Swaziland [15]. Although several studies have noted the impact of the cement industry on the environment from developed countries, few studies have been conducted in South Africa [1, 5, 6, 10]. The present study was carried out to investigate the concentrations of heavy metals from soils and plants collected around the Hercules cement factory in Pretoria. The study also assessed the level of heavy metal contamination in the topsoil based on pollution index (PI).

Methodology

The study was carried out at about 50 m from a cement factory in Pretoria. The cement factory is situated just next to a very busy road (GPS: 25º۴۳’۲۱ S, 28º۱۰’۱۵ E). The area falls on the western part of Pretoria. There are two major seasons in the area (winter and summer), although the city usually witnesses a short period of spring and autumn. Sampling was done during the two major seasons. Sampling was carried out in the northeastern (NE), northwestern (NW), and southwestern (SW) areas of the cement company. Soil and plant samples were collected from these directions around the area: 30 soil samples from the topsoil (0-15 cm) and 30 soil samples from the sub soil (15-30 cm). Plants samples were collected from each of the directions where soil samples were collected and were identified up to the species. The soil samples were ground in the laboratory and airdried. From the ground soil samples, 0.5 g of the soil were added with 2.0 ml of HCl, 2.0 ml of HClO4, 2.0 ml of HF, and 8 ml of HNO3. The resulting solutions were then analyzed for trace metals contents using ICP-MS in order to determine the concentrations of trace metals from the soil samples. The plant samples were partitioned into three parts, namely for analyses: root, stem, and leaves. From these parts, 0.2 g of each of the different parts were acid-digested using 2 ml HCl, 1 ml HClO4, 2 ml of HF, and 5 ml of HNO3, and the resulting solutions were then analysed for trace metal contents using ICP-MS. Quality assurance was done using Certified Reference Materials for both soil and plant samples and the analysis was also carried out in triplicate. The ability of plants to uptake trace metals from the soil was determined using the transfer factor model [16]. The transfer factor is calculated as the concentration of heavy metals in plant parts to the concentration present in the soil. This is an index of soil-plant transfer. Values >1 indicate that plants are enriched in elements from soil (accumulator), ratios around 1 indicate that plants are not influenced by elements (indicator), and values <1 show that plants exclude the element from soil (excluder). Pollution Assessment Pollution assessment of the soil was calculated using the pollution index (Pi) method and the geo-accumulation index (Igeo). The pollution index was calculated using the formula:

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دانلود آهنگ جدید شهاب مظفری و علی یاسینی وای وای

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شعر و ملودی : علی یاسینی | تنظیم قطعه : سعید شمس | میکس و مسترینگ : محمد فلاحی

Download New Music BY : Shahab Mozaffari Ft. Ali Yasini | Vay Vay With Text And 2 Quality 320 And 128 On Music-fa

متن آهنگ وای وای شهاب مظفری و علی یاسینی

───┤ ♩♬♫♪♭ ├───

تو می دونی دلم گیره●♪♫

منو عطرِ تو می گیره●♪♫

بهم نگو که بیخیال شو●♪♫

فکر کنم یه کم دیره●♪♫

شعر و ملودی : علی یاسینی
بگو چته نمی دونم●♪♫

منم مثلِ تو دیوونم●♪♫

بس کن این اَدا اَطوارو●♪♫

چی می خوای از جونم؟●♪♫
وای! وای! دلمون رفت!●♪♫

دلو بُرد و ولمون کرد●♪♫

وای! وای! دلمون رفت!●♪♫

کاش راضی بشه زودتر!●♪♫
وای! وای! دلمون رفت!●♪♫

تنظیم قطعه : سعید شمس

دلو برد و ولمون کرد●♪♫

وای! وای! دلمون رفت!●♪♫

کاش راضی بشه زودتر!●♪♫

با ما چی کار کردی تو؟●♪♫

چرا انقد سردی خب؟●♪♫

توو چشام نگاه کن●♪♫

دیوونم کردی تو!●♪♫
هر جا بری هستم پات●♪♫

از این کارات دست بردار●♪♫

اگه تو قبول کنی که●♪♫

شروع کنیم از فردا●♪♫
وای! وای! دلمون رفت!●♪♫

دلو برد و ولمون کرد●♪♫

میکس و مسترینگ : محمد فلاحی

وای! وای! دلمون رفت!●♪♫

کاش راضی بشه زودتر●♪♫
وای! وای! دلمون رفت!●♪♫

دلو برد و ولمون کرد●♪♫

وای! وای! دلمون رفت!●♪♫

کاش راضی بشه زودتر!●♪♫

───┤ ♩♬♫♪♭ ├───

دانلود آهنگ شهاب مظفری و علی یاسینی وای وای

دانلود آهنگ شهاب مظفری و علی یاسینی وای وای

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