Miracle-dz/UniFAR-FTUs huggingface dataset:
1 CFD-DEM Investigation of Seed Clustering in an Air Seeder with the Immersed Boundary Method

Agricultural air seeders typically have uneven distribution of seeds from their outlets, leading to irregular planting patterns and inefficient farming. When larger seeds are used, a clustering effect can also occur where seeds emerge, not in steady streams, but in clustered groups. This work investigates the causes of these effects with a CFD-DEM approach to improve future air seeder design. Two modelling approaches are attempted. In the first approach, small particles based on wheat seeds are modelled with one-way coupling to validate the numerical models and spatial discretization based on experimental results for the outlet distribution. In the second approach, the Immersed Boundary (IB) method as modified by Hager (2014) is used in twoway coupling to capture the effect of larger particles (based on chickpeas) on the flow. The first method finds a coefficient of variation (CV) between the 11 different outlets of 12.5%, which is within the accepted range of typical air seeder performance, and matches the reported number by the industry. For the second approach, the concept of a “time CV” is introduced to describe the relative amount of clustering in larger seeds based. The clustering effect is found to be largely due to obstructions in the flow causing the seeds to drop into Geldart’s “spouted bed” fluidization regime (Geldart, 1973), resulting in periodic bursts of seeds after localized pressure build-up. Results of the simulations were compared to experimental data, and are in good agreement.
Droplet characteristic adjustment method based on LEM for 3 D electronic printing

The major challenge in 3D electronic printing is the print resolution and accuracy. In this paper, a typical mode lumped element modeling (LEM) method is adopted to simulate the droplet jetting characteristic. This modeling method can quickly get the droplet velocity and volume with a high accuracy. Experimental results show that LEM has a simpler structure with sufficient simulation and prediction accuracy.
FPGA implementation of a Convolutional Neural Network for "Wake up word" detection

The popularity of machine learning has increased dramatically in the last years and the possible applications varies from web search, speech recognition, object detection, etc. A big part of this development is due to the use of Convolutional Neural Networks (CNNs), where high performance Graphics Processing Units (GPUs) has been the most popular device. This thesis explores the use of a Field-Programmable Gate Array (FPGA), specifically an Arria 10 GX FPGA, to implement a "wake up word" CNN. The High-Level Synthesis (HLS) tool Intel FPGA SDK for OpenCL was used. During the project various neural networks has been implemented and tested on the FPGA with different attributes to understand their effect. An infrastructure to test various neural networks was made and used to implement the wake up word CNN. A solution to test the CNN in a setup with live recording was also made. The final implementation of the wake up word CNN achieved a classification time of 3.6 ms and 0.54 Gmac/s, where a mac is the multiply-accumulate operation. Comparing to a CNN runnning on a NVIDIA Tegra X1 GPU, the GPU was 22.2 times faster with 11.99 Gmac/s. Although the classification time of 3.6 ms is acceptable for this application, future work should attempt to keep as much of the computation and memory transfers on the FPGA chip and with minimal interaction with the host machine to improve performance.
TESTING OF THE NEW INSECTICIDES FORMULATION FOR MAIZE SEEDS TREATMENT AGAINST Tanymecus dilaticollis Gyll IN LABORATORY CONDITIONS

Maize leaf weevil (Tanymecus dilaticollis Gyll) produce high damages at maize crops, in south and south-east of the Romania. Until end of the year 2013, seed treatments were generalized. After neonicotinoid treatment restrictions, as result of EU directive 485/2013, no insecticides remain available, for maize seeds treatment, against T. dilaticollis, in Romania. As result, researches from last years were focused for finding possible alternatives to replace neonicotinoid insecticides, used for maize seed treatments, in eventuality of permanent ban of these, in 2016. In this paper, authors collective present results concerning testing of two active ingredients (cyantraniliprole and thiacloprid), for controlling of the maize leaf weevil, used high pest pressure, in laboratory conditions. The results obtained at variants treated with cyantraniliprole, thiacloprid or different combinations of these two active ingredients were compared with one standard active ingredient, clothianidin and control (untreated) variant. In this laboratory assessment, in conditions oh high pest pressure, after 8 days from pots infestation with T. dilaticollis adults, higher insect mortality was registered in case of variant treated with clothianidin active ingredients (67.50%). At the rest of the treated variants, insects mortality, after 8 days of observations, presented low values, ranged between 6.25 and 12.50%. On a scale from 1 (not attacked) to 9 (total damage), attack intensity of the T. dilaticollis at the maize untreated plants, was of 8.67. In conditions of high pest pressure, untreated plants were destroyed. The lowest attack intensity values was registered in case of variant treated with clothianidin active ingredient (I=3.92). Cyantraniliprole and thiacloprid active ingredients didn’t provide effective protections of the maize plants, in first vegetation stages (BBCH 10-14), against T. dilaticollis attack, in conditions of high pest pressure.
A Primer on Reservoir Computing

This paper is a brief primer on concepts in reservoir computing (RC). Reservoir computing defines a class of artificial neural network (ANN) models that mimic neural microcircuits in the biological brain using an un-trained reservoir of neurons and a trained readout function. This structure distinguishes RC models from traditional ANNs which train neurons at all layers in the network. The remainder of this paper is organized as follows. Section 2 introduces fundamental concepts in ANNs necessary for understanding RC models. Section 3 introduces high-level concepts, motivations, and the historic development of RC. Section 4 covers the Liquid State Machine (LSM) model in a more formal capacity. Section 5 details several practical aspects of RC. Section 6 highlights recent work and future trends in RC research.
Optimizing the reference population in a genomic selection design

In genomic selection, when candidate animals for reproduction are selected on an estimate of their breeding value from genomic information (using Single Nucleotide Polymorphims (SNP) chips), it is needed to build a reference population whose members are both genotyped on the SNPs and phenotyped for the economical trait(s) to be improved. We studied, with numerical simulations of such genomic selection plan, how to optimize the design of this reference population. The problem is summarized as minimizing a quadratic function on Boolean variables with a cardinality constraint. Integer linear/quadratic/constraint programming and weighted Max-SAT and CSP solvers are compared on a few examples.
A CONTROL THEORY FRAMEWORK FOR HIERARCHICAL REINFORCEMENT LEARNING

Hierarchical reinforcement learning (HRL) algorithms are gaining attention as a method to enhance learning performance in terms of speed and scalability. A key idea to introduce hierarchy in reinforcement learning is to use temporal abstractions. The use of hierarchical schemes in reinforcement learning simplifies the task of designing a policy and adding prior knowledge. In addition, well-designed temporally extended actions can speed up the learning process by constraining the exploration. Furthermore, policies learned are often simpler to interpret and with fewer parameters, without losing representation power. These advantages make HRL suitable for complex robotics tasks with continuous action-spaces and high state-action dimensionality. The aim of this thesis is to adapt the approach of control theory to hierarchical schemes in the field of hierarchical reinforcement learning. We designed a novel framework to fill the gap between control theory and machine learning.
A New Way to Measure Cirrus Ice Water Content by Using Ice Raman Scatter with Raman Lidar

To improve our understanding of cirrus cloud radiative impact on the current and future climate, we need improved knowledge of cirrus cloud microphysical properties. However, long-term studies of cirrus clouds indicated that it is a challenging task to get them accurately by both remote sensing and in situ sampling. This study presents a new method to remotely sense cirrus microphysical properties by using ice Raman scatter with a Raman lidar. The intensity of Raman scattering is fundamentally proportional to the number of molecules involved. Therefore, ice Raman scattering signal provides a more direct way to measure ice water content. Case studies show that this method has the potential to provide essential information of cirrus microphysical properties to study cloud physical processes in cirrus clouds.
Demystifying the Role of the Steward in Joined-Up Government Services Delivery

This study seeks to enhance understanding regarding the public sector stewardship of integrated services delivery initiatives in joined-up digital government, with a particular focus on understanding the nature of the steward’s role in practice. This specifically includes the skills required of those performing stewardship, and the key challenges stewards faced. Eight interviews supplemented by the use of reflective journals were completed with stewards of the New Zealand public sector to understand their practice and perception of the steward concept. Three tiers of stewardship practice were identified: (1) Internal Stewardship, (2) Inter-Agency Stewardship, and (3) Stewardship as a Governance Model. Each tier exhibited unique challenges. Participants overcame these challenges through performing two complementary sub-roles: Navigator and Storyteller. Understanding the operational challenges of the stewardship tiers and how the stewardship roles were enacted has implications for both practitioners and researchers.
Commitment in the Entrepreneurial Process : A reconceptualization

Researchers disagree on the role that entrepreneurial commitment plays in nascent markets. One view is that uncertainty requires a flexible approach, which precludes commitments. Another view is that flexibility and commitment can be simultaneous and that successful entrepreneurs balance the two. This article reconciles the two views by differentiating the forms of flexibility on the basis of commitment. It proposes a framework that formulates entrepreneurial action in terms of each form’s context of relevance, and discusses the implications for the entrepreneurial process.
MASS CUSTOMIZATION : REUSE OF TOPOLOGY INFORMATION TO ACCELERATE SLICING PROCESS FOR ADDITIVE MANUFACTURING

Additive manufacturing (AM) can build objects with complex features with little extra effort, opening up potentials to realize mass customization. Continuous Liquid Interface Production (CLIP) prints object in a continuous fashion, leading to extremely high productivity and consequently enabling mass customization. CLIP adopts a large number of images as input, which poses a fundamental challenge in layer generation. The slicing procedure for a single customized model can take tens of minutes or even hours to complete, and the time consumption becomes more prominent in mass customization context. Motivated by the similarities among the customized products, we proposed a new slicing paradigm. It reuses topology information obtained from the template model for other customized products from the same category. The idea of topology information reuse is implemented at three levels, including self reuse, intramodel reuse, and inter-model reuse. Experimental results show that the proposed slicing paradigm can significantly reduce the time consumption on pre-fabrication computation, and ultimately fulfill mass customization enabled by AM.
Region Duplication Detection Based On Statistical Features of Image

Abrupt boom in digital world has led to an instant increase in the popularity of digital images. Easy availability of image tampering tools like Picasa, Adobe Photoshop and Gimp etc. have made image tampering widespread. As such detecting tampering in images has become an active area of research. Region duplication is most common image tampering technique because of the ease with which it can be carried out. Available techniques for region duplication detection fail to accurately locate the tampered region and lack robustness. This paper proposes duplicate region detection method based on statistical texture features using gray level co-occurrence matrix (GLCM) and gray level run length matrix (GLRLM) features. The method divides the forged image into overlapping blocks, calculate texture features based on GLCM and GLRLM of each block. Feature vectors thus obtained for each block are lexicographically sorted. Blocks with similar features are identified using feature distances. Post processing isolates the duplicate regions. Experimental results establish that the proposed method using GLRLM features can precisely locate duplicate regions in image and can effectively withstand the common post processing operation like jpeg compression, blurring, brightness and contrast change with reduced computation complexity.
The Use of Land Surface Temperature Observations for the Assessment of Land Surface Parameterizations

Land Surface Temperature estimated from Meteosat Second Generation (MSG) is used to assess ECMWF skin temperature, a variable which can be interpreted as a radiative temperature of the model surface. It is shown that the ECMWF model tends to slightly overestimate skin temperature during night-time and underestimate daytime values. Such underestimation of daily amplitudes is particularly pronounced in arid and semi-arid regions. The introduction of monthly fields of the leaf area index leads to a consistent positive impact on the model skin temperature, mostly associated to an overall reduction in the evaporative fraction. Such changes are more evident for forecasts experiments without assimilation. On the other hand, the revision of model roughness lengths for momentum and heat is shown to have a pronounced positive impact on the bias of daytime skin temperatures, particularly in areas which are not affected by changes in the model vegetation.
New FastPFOR for Inverted File Compression

Inverted index is used in most Information Retrieval Systems (IRS) to achieve the fast query response time. In inverted index, compression schemes are used to improve the efficiency of IRS. In this chapter, the authors study and analyze various compression techniques that are used for indexing. They also present a new compression technique that is based on FastPFOR called New FastPFOR. The storage structure and the integers’ representation of the proposed method can improve its performances both in compression and decompression. The study on existing works shows that the recent research works provide good results either in compression or in decoding, but not in both. Hence, their decompression performance is not fair. To achieve better performance in decompression, the authors propose New FastPFOR in this chapter. To evaluate the performance of the proposed method, they experiment with TREC collections. The results show that the proposed method could achieve better decompression performance than the existing techniques.
Integration of Oil Palm and Cattle for Post Conflict Sustainable Development

his paper aimed to explain the integration of oil palm and cattle in the concept of sustainable development in post-conflict Aceh Timur, Indonesia. This paper looked deeper on community point of view toward the integration of palm and cattle farmers, which includes the process and benefits of such integration. It is then analyzed with sustainable development in post-conflict Aceh. Taking qualitative approach design, this research was conducted in three districts in East Aceh, they are sub-district of Idi Tunong, sub-district of Nurussalam and sub-district of Rantou Panyang. Research data comprised of non-participant observation results, in-depth interviews, and documentation. Data analysis was done in three stages; data reduction, data presentation and conclusion. Results of study showed that the process of integration of palm oil and cattle carried out by the farmers community were divided to 3 types of models; naturally integrated system; cooperative integrated system; and intensive integration system. The programs yield many positive impacts towards society. Impacts were obvious both on economic sector in an increased of income and fulfilment of social and spiritual activities (worship). In addition, the use of pesticides for weeds eradication and fertilizers are significantly reduced. This fact described that types of models are aligned with on the framework of sustainable development concept, as well as to a positive peace building in Aceh
Complexity bounds for approximations with deep ReLU neural networks in Sobolev norms

We analyze to what extent deep ReLU neural networks can efficiently approximate Sobolev regular functions if the approximation error is measured with respect to weaker Sobolev norms. In this context, we first establish upper approximation bounds by ReLU neural networks for Sobolev regular functions by explicitly constructing the approximating ReLU neural networks. Then, we establish lower approximation bounds for the same type of function classes. A trade-off between the regularity used in the approximation norm and the complexity of the neural network can be observed in upper and lower bounds. Our results extend recent advances in the approximation theory of ReLU networks to the regime that is most relevant for applications in the numerical analysis of partial differential equations.
Glycemic index of Coleus tuberosus crackers rich in resistant starch type III

This study aimed to analyze the glycemic index value of processed products of Coleus tuberosus. The study included ten healthy subjects with seven males, three females (30 ± 3.80 years on average age and 22.49 ± 1.82 on average BMI). Participants tested three different meals (D-glucose anhydrous, wheat crackers, and Coleus tuberosus crackers) with equal carbohydrate load (50 g). Blood glucose concentrations were measured at 0 min as well as at 30, 60, 90 and 120 min after the start of the meal for glucose levels determination. The result showed that the glycemic index of Coleus tuberosus crackers rich in resistant starch tipe III categorized in the low category. The glycemic index value of Coleus tuberosus crackers at 40.88 ± 6.42, whereas wheat crackers has high glycemic index (76.08 ± 5.36). Based on this results, Coleus tuberosus crackers is good for control of blood glucose profile.
Scalable Static Analysis Using Facebook Infer

Static analysis has nowadays become one of the most popular ways of catching bugs early in the modern software. However, reasonably precise static analyses do still often have problems with scaling to larger codebases. And efficient static analysers, such as Coverity or Code Sonar, are often proprietary and difficult to openly evaluate or extend. Facebook Infer offers a static analysis framework that is open source, extendable, and promoting efficient modular and incremental analysis. In this work, we propose three inter-procedural analysers extending the capabilities of Facebook Infer: Looper (a resource bounds analyser), L2D2 (a low-level deadlock detector), and Atomer (an atomicity violation analyser). We evaluated our analysers on both smaller hand-crafted examples as well as publicly available benchmarks derived from real-life low-level programs and obtained encouraging results. In particular, L2D2 attained 100 % detection rate and 11 % false positive rate on an extensive benchmark of hundreds of functions and millions of lines of code.
A novel algorithm based on wavelet transform for ship target detection in optical remote sensing images

The rapid development of the satellite observation technology provides a very rich source of data for sea reconnaissance and ships surveillance. In the face of such a vast sea remote sensing data, it is urgent need to realize the automatic ship detection in optical remote sensing images, but the optical remote sensing images are easily affected by meteorological conditions, such as clouds, waves, which results in larger false alarm; and the weak contrast between optical remote sensing image target and background is easy to cause missing alarm. In this paper, a novel algorithm based on wavelet transform for ship target detection in optical remote sensing images is proposed, which can effectively remove these noise and interference. The segmentation of sea and land background is first applied to the image preprocessing to achieve more accurate detection results, and then discrete wavelet transform is used to deal with the part of sea background. The results show that almost all of the offshore ships can be detected, and through the comparison of the results of four different wavelet basis functions, the accuracy of ship detection is further improved.