Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Regularization Weight for CRF Optimization

CNNs have made a tremendous impact on the field of computer vision in the last several years. For dense image classification tasks, CNNs usually assign a label to each pixel individually. To enforce spatial coherence, CNNs are often combined with CRFs. CRF optimization includes a regularization term that helps to obtain a spatially consistent labeling. The weight of the regularization term, usually learned from the training data, is fixed to the same value for all images. However, for optimal results, it is best to use a different amount of regularization for each image depending on the image content and the reliability of the label probabilities learned by CNN. We propose to learn the regularization weight from training data for each individual image. To this end, we construct a dataset where the optimal regularization weight for CRF optimization has been precomputed for each image. We use a CNN with a standard architecture, but the input is tailored to our problem. The input is the image itself and the label probabilities produced by CNN, since the regularization weight depends on both. The output of our CNN is the regularization weight, which is then used with a CRF optimizer in a post processing step. We test the effectiveness of our approach on the task of salient object segmentation. We show that accuracy improvement can be achieved when using regularization weight learned on per-image basis as opposed to using a single regularization weight learned for all images in the dataset.
The value of ascospore septation in separating Mycosphaerella from Sphaerulina in the Dothideales: a Saccardoan myth?

P. W. Crous, J. Z. Groenewald, M. J. Wingfield & A. Aptroot (2003). The value of ascospore septation in separating Mycosphaerella from Sphaerulina in the Dothideales: a Saccardoan myth? Sydowia 55 (2): 136-152. Ascospore septation was used by Saccardo as a primary character to separate genera in the Dothideales (Ascomycetes). The genus Sphaerulina (3to multi-septate ascospores) was thus distinguished from Mycosphaerella (l-septate ascospores). Several species in Sphaerulina were found to have similar anamorphs as Mycosphaerella. Sequence data derived from the rDNA (ITS 1, 5.8S and ITS 2) gene suggest that Sphaerulina is heterogeneous, and that species with Mycosphaerella-like anamorphs belong in Mycosphaerella, while those with yeast-like anamorphs belong to the Dothioraceae. Sphaerulina eucalypti, which occurs on Eucalyptus leaves in South Africa, is transferred to Sydowia. This species is also newly linked to its anamorph, Selenophoma eucalypti. Mycosphaerella sphaerulinae and its Pseudocercospora sphaerulinae anamorph are newly described from leaf spots of Eucalyptus globulus and E. nitens in Chile. This study contributes to growing phylogenetic evidence that ascospore septation is a poor indicator of generic status in the Ascomycetes. Ke:ywords: Eucalyptus, leaf spot, Mycosphaerella, Pseudocercospora, Sphaerulina, Sydowia, systematics.
Microbial Community Function in Electroactive Biofilm-based Constructed Wetlands

Abstract. The performance enhancement of constructed wetlands can be achieved through the coupling with microbial electrochemical technologies (MET). MET is a setup designed to mimic metabolic electrons exchange with insoluble donors and acceptors with the aid of electroactive bacteria and external electrical circuits. An alternative MET that dispenses of electrodes and circuits but uses an electro-conductive biofilter is called Microbial Electrochemical-based Constructed Wetland (METland). Previously it has been demonstrated that a METland has higher biodegradation rates than horizontal flow constructed wetlands, however given its novelty there are still uncertainties related to the removal of pollutants, including their microbial activity. The genetic characterization of microbial communities of a METland is desirable, but is time and resource consuming, then a characterization alternative could be based on functional analysis of the microbial communities. Community-level physiological profile (CLPP) is a useful method to evaluate the functional diversity of microbial communities based on the carbon source utilization pattern (CSUP). Therefore, this study was focused on the microbial characterization of laboratory scale METland based on CLPP analysis. The study included the characterization of microbial communities attached to two carbon-based electro-conductive materials (calcined petroleum coke from crushed electrodes – PK-A; calcined petroleum coke with low sulphur and nitrogen content – PK-LSN), in planted and non-planted set-ups. Variations on the metabolic activity of tested systems were identified and it seems to be related to the characteristics of the material, rather than the presence/absence of plants. In general, CSUP show differences along flow pathway, as well as among the tested systems, being carbohydrates and carboxylic/acetic acids the most consumed carbon sources, followed by polymers, amides/amines and amino acids. Also, were established some correlations between the utilization of carbon sources and the removal of pollutants. The obtained results provide useful insight into the spatial dynamics of METland systems.
A Neural Network-based Hybrid Method to Generate Feasible Neighbors for Flexible Job Shop Scheduling Problem

In this paper, a hybrid method is proposed to generate feasible neighbors for the flexible job shop scheduling problem. Many of the optimization and artificial intelligence methods have been used to solve this important and NP-hard combinatorial problem which provides the basis for solving real-life problems. It is well-known that for such problems the hybrid methods obtain better results than the other approaches. For instance, the applied non-hybrid neural networks for the combinatorial problems, as the Hopfield neural network, usually converge early. Also, their results almost always contain large gaps. These shortcomings prevent them to find good results. Another necessity for a quality search is to find suitable neighbors of the obtained solutions; however, it is possible to create infeasible neighbors during the optimization process. The aim of this study is to overcome these deficiencies. In the suggested approach, at first, an initial solution is generated and then using the left shift heuristics, its gaps are removed. Based on the critical path and critical block concepts, 6 neighbors are constructed for the obtained solution. After the generation of each neighbor, a neural network runs and controls the constraints of the problem. If the achieved neighbor is feasible it is saved. Else if it is infeasible, the neural network tries to transform it into a feasible solution. This is done by applying penalties to the start time of the operations on the violated constraints, which shifts them to the right or the left. During this process, if there are not any violated constraints, the neural network reaches the stable condition so it stops and the obtained solution is saved as a feasible neighbor. Otherwise, after a certain number of the iterations, it stops without any feasible neighbors. Then these steps are repeated for the other created neighbors. This constraint-based process provides an effective and diverse search. Finally, the obtained neighbors, are improved using the left shift heuristics. Also to demonstrate the importance of the initial solutions, they are generated randomly and also using the Giffler and Thompson's heuristic. The comparison between the proposed approach and the methods from the literature shows that it constructs better neighbors. However, using the Giffler and Thompson heuristic to create the initial solution improves the results significantly.
Non-Binary N-MSK Non-orthogonal Modulation for Wireless Communication

This paper presents a novel multi-level convolutional encoder(MCE) modulation method, which is non-binary TCM based on Rings of Integers. We also give how to searching such good codes, which can be decoded by genetic algorithm. Through simulation different environments, the paper shows such codes have advantage over traditional TCM. Introduction Convolutional codes based on rings of integers modulo-M were first presented by Massey and Mittelholzer [1, 2]. They were followed by Baldini and Farrell [3, 4], who presented TCM codes based on rings of integers. Baldini and Farrell have developed a number of modulo-M ring-TCM codes for N-PSK constellations and have concluded that, due to the similarities between N-MSK signal sets and the algebraic structure of rings of integers modulo-M, modulo-M ring-TCM codes are the natural linear codes for N-MSK modulation. In this paper, we propose a new overlapped non-orthogonal modulation theory, based on the theory apply the N-MSK for wireless communication system. We also present the NMSK coding based on Rings of Integers, and how to search such code. Then a novel genetic decoding algorithm are discussed in detail, its performance are evaluated through simulation. Through studying its performance under different channel circumstance, we learned the N-MSK has better SIR characters, and higher spectrum efficiency than traditional TCM modulation. At last, we also extend the application to non-orthogonal situation, which can be used for improving the performance of wireless system. N-MSK modulation model NMSK signal are consisting of N MSK signals with different amplitudes, which can be expressed as[12]: 1 2 1 ( , , ,., ) ( , )    N NMSK N i i S t A S t     (1a) 2( 1) 1 2 / / 2     N i i A E T (1b) Where E presents average engorge; and T presents code interval; S(t, αi) presents the i th componentsof NMSK,which can be expressed as , 1 , ( ( 1) ) ( , ) 2 cos 2 2 ( 1)                  i n i i c i n t n T S t f t T n T t nT     (2a) 1
Specification and Partial Implementation of a Test and Control Framework for the OPS-SAT Project Bachelorthesis by Moritz Woitelle

When there is an innovation in satellite operations, it is very hard to test it. This is due to the risk associated with changing operational methods or software on an ongoing satellite mission. If there would be an error uploaded with the software, the operators would lose an otherwise healthy satellite. Also, space missions are handicapped by the fact that the on-board computer (OBC) has to withstand the radiation environment of outer space. The terrestrial chip industry doubles the computing power of computer chips every year. To make computer chips radiation hardened takes time and is cost intensive, so the radiation hardened hardware cannot keep up with this doubling. This leads to more and more commercially available off the shelf (COTS) hardware being used to build satellites. But using COTS hardware has its downsides. Only by applying special techniques, such as redundancy, the COTS hardware can survive as long as radiation hardened hardware in space radiation environment. To further research the reliability of COTS components is a challenge. The European Space Agency (ESA) tackles those two known challenges in spaceflight with its new satellite called Operations-Satellite (OPS-SAT). Its main objective is to be an orbital laboratory for experiments in the satellite operations sector. Any European institution or company can enlist for testing their experiments. The experiments can control the satellite, even if their creators do not have any experience in satellite software or operations. To handle this, OPS-SAT has to be robust and safe. This thesis describes the aspects that make OPS-SAT robust hardware wise and specifies a flexible framework, to test experiments on ground, based on GitLab. The framework is able to semi automatically handle all 115 proposed experiments so far and is able to grow with more registrations. It can create logs and regression views for the operators at ESA to see the development of the experiments over time. Initial tests for file size which create reports for the experimenters are possible. The tests are automatically scheduled. Also, the experimenters can run their own software on duplicates of the experiment processing platform at European Space Operations Center (ESOC) in a future iteration of the project.
Honeybee declines in a changing landscape: interactive effects of honeybee declines and land-use intensification on pollinator communities

5 Acknowledgements . 6 List of Tables . 7 List of Figures . 8 List of Appendices . 9 Chapter 1: Introduction . 10 1.1 The importance of pollination . 10 1.2 Honeybees as pollinators . 10 1.3 Honeybee declines . 11 1.4 Wild pollinators . 12 1.5 Impact of honeybees on wild pollinators . 13 1.6 Land-use intensification and its effect on pollinators . 13 1.7 Pollination in NZ . 14 1.8 Study Location . 14 1.9 Objectives and study Outline . 16 Chapter 2: Changes in pollinator community composition and resulting pollination rates . 18 2.
Multi-tasking scheduling for heterogeneous systems

Heterogeneous platforms play an increasingly important role in modern computer systems. They combine high performance with low power consumption. From mobiles to supercomputers, we see an increasing number of computer systems that are heterogeneous. The most well-known heterogeneous system, CPU+GPU platforms have been widely used in recent years. As they become more mainstream, serving multiple tasks from multiple users is an emerging challenge. A good scheduler can greatly improve performance. However, indiscriminately allocating tasks based on availability leads to poor performance. As modern GPUs have a large number of hardware resources, most tasks cannot efficiently utilize all of them. Concurrent task execution on GPU is a promising solution, however, indiscriminately running tasks in parallel causes a slowdown. This thesis focuses on scheduling OpenCL kernels. A runtime framework is developed to determine where to schedule OpenCL kernels. It predicts the best-fit device by using a machine learning-based classifier, then schedules the kernels accordingly to either CPU or GPU. To improve GPU utilization, a kernel merging approach is proposed. Kernels are merged if their predicted co-execution can provide better performance than sequential execution. A machine learning based classifier is developed to find the best kernel pairs for co-execution on GPU. Finally, a runtime framework is developed to schedule kernels separately on either CPU or GPU, and run kernels in pairs if their co-execution can improve performance. The approaches developed in this thesis significantly improve system performance and outperform all existing techniques.
Non-uniform replication for replicated objects

A large number of web applications/services are supported by applications running in cloud computing infrastructures. Many of these application store their data in georeplicated key-value stores, that maintain replicas of the data in several data centers located across the globe. Data management in these settings is challenging, with solutions needing to balance availability and consistency. Solutions that provide high-availability, by allowing operations to execute locally in a single data center, have to cope with a weaker consistency model. In such cases, replicas may be updated concurrently and a mechanism to reconcile divergent replicas is needed. Using the semantics of data types (and operations) helps in providing a solution that meets the requirements of applications, as shown by conflict-free replicated data types. As information grows it becomes difficult or even impossible to store all information at every replica. A common approach to deal with this problem is to rely on partial replication, where each replica maintains only part of the total system information. As a consequence, each partial replica can only reply to a subset of the possible queries. In this thesis, we introduce the concept of non-uniform replication where each replica stores only part of the information, but where all replicas store enough information to answer every query. We apply this concept to eventual consistency and conflict-free replicated data types and propose a set of useful data type designs where replicas synchronize by exchanging operations. Furthermore, we implement support for non-uniform replication in AntidoteDB, a geo-distributed key-value store, and evaluate the space efficiency, bandwidth overhead, and scalability of the solution.
Controlled spatiotemporal excitation of metal nanoparticles with picosecond optical pulses

We discuss realistic finite-difference time-domain simulations of chirped optical pulses interacting with silver nanowires and cone-shaped nanoparticles. These systems are differently shaped and larger than those previously studied fM. I. Stockman, S. V. Faleev, and D. J. Bergman, Phys. Rev. Lett. 88, 067402 s2002dg, but still allow for localized hot spots near the metal surfaces to be generated and controlled in a spatiotemporal manner. The control is made possible by chirping the pulses such that the effective frequency passes through surface plasmon resonances associated with different spatial regions of the nanostructure over the course of time. We demonstrate how this leads to counterintuitive, negative group velocities in some situations. The control is shown to scale with pulse duration from <150 fs to at least <2200 fs= 2.2 ps, and we anticipate it to scale to longer durations. We further show that the response of such nanostructures to chirped pulses can provide a means of encoding or decoding optical signals, which could be verified experimentally.
THE USE OF BEHAVIOURAL ADVICE IN THE MANAGEMENT OF SEPARATION ANXIETY IN CANINES: A LITERATURE REVIEW.

Shen Jiaying 1 , Seah Jing Yan 1 and Yap Qi Rou 2 . 1. Department of Research, Oei Family Clinic. 2. Yong Loo Lin School of Medicine, National University of Singapore. Manuscript Info Abstract . Manuscript History Received: 06 June 2019 Final Accepted: 08 July 2019 Published: August 2019
Energy-Efficient eDRAM-Based On-Chip Storage Architecture for GPGPUs

In a typical GPGPU, the on-chip storage is critical to the massive parallelism and is desired to be large. However, the fast increasing size of the on-chip storage based on traditional SRAM cells, such as register file (RF), shared memory and first level data (L1D) cache, makes the area cost and energy consumption unsustainable for future GPGPUs. In this paper, we first propose to use the embedded-DRAM (eDRAM) as an alternative for the on-chip storage. Compared to the conventional SRAM, eDRAM enables higher density and lower leakage power, but suffers from limited data retention time. Periodic refresh operation is a viable approach to maintain data integrity but aggravates the performance and energy consumption with the scaling of eDRAM cells into deep sub-micron technology nodes. To recover the performance loss, we exploit the features in the GPGPU architecture and propose various novel refresh schemes to mitigate the refresh penalty. To improve the energy efficiency, we apply lightweight compiler techniques and runtime monitoring for selective refreshing that intelligently eliminate the unnecessary refreshes. The evaluation on our proposed refresh schemes demonstrates that, comparing to the conventional SRAM-based designs, our eDRAM-based on-chip storage exhibits comparable performance but less energy consumption and smaller silicon area, enabling the sustainable on-chip storage scaling for even higher parallelism in future GPGPUs.
Join on Samples: A Theoretical Guide for Practitioners

Despite decades of research on AQP (approximate query processing), our understanding of sample-based joins has remained limited and, to some extent, even superficial. The common belief in the community is that joining random samples is futile. This belief is largely based on an early result showing that the join of two uniform samples is not an independent sample of the original join, and that it leads to quadratically fewer output tuples. Unfortunately, this early result has little applicability to the key questions practitioners face. For example, the success metric is often the final approximation's accuracy, rather than output cardinality. Moreover, there are many non-uniform sampling strategies that one can employ. Is sampling for joins still futile in all of these settings? If not, what is the best sampling strategy in each case? To the best of our knowledge, there is no formal study answering these questions. This paper aims to improve our understanding of sample-based joins and offer a guideline for practitioners building and using real-world AQP systems. We study limitations of offline samples in approximating join queries: given an offline sampling budget, how well can one approximate the join of two tables? We answer this question for two success metrics: output size and estimator variance. We show that maximizing output size is easy, while there is an information-theoretical lower bound on the lowest variance achievable by any sampling strategy. We then define a hybrid sampling scheme that captures all combinations of stratified, universe, and Bernoulli sampling, and show that this scheme with our optimal parameters achieves the theoretical lower bound within a constant factor. Since computing these optimal parameters requires shuffling statistics across the network, we also propose a decentralized variant in which each node acts autonomously using minimal statistics. We also empirically validate our findings on popular SQL and AQP engines.
Low-Carbon Binder for Cemented Paste Backfill: Flowability, Strength and Leaching Characteristics

Blast furnace slag was used as the main raw material to prepare the alkali activated slag (AAS), a low-carbon binder, for cemented paste backfill (CPB). The optimum parameters for preparing the AAS binders using an orthogonal experiment were obtained. Under the optimum conditions (NaOH content was 3 wt. %, Ordinary Portland cement (OPC) content was 7 wt. %, and gypsum dosage was 4 wt. %), the 28 days compressive strength of the binder was 29.55 MPa. The flow ability of the fresh CPB slurry decreased with solid content due to the increased yield stress, while the flow ability increased when rising the binder dosage. A predictive model for the compressive strength of CPB samples was reached through multivariate analysis and the R2 values were higher than 0.9. Sensitivity analysis showed that the solid content is the most important parameter which influences on the development of the CPB strength with a correlation coefficient of 0.826. From the Toxicity Characteristic Leaching Procedure (TCLP) tests, the leaching concentrations of Pb and Cd were below the threshold. As a result, the AAS has potential application as an alternative binder and cemented paste backfill.