Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Machine Learning to Predict Chemotherapy Response in Cell Lines and Patients Based on Genetic Expression

The goal of this thesis was to examine different machine learning techniques for predicting chemotherapy response in cell lines and patients based on genetic expression. After trying regression, multi-class classification techniques and binary classification it was concluded that binary classification was the best method for training models due to the limited size of available cell line data. We found support vector machine classifiers trained on cell line data were easier to use and produced better results compared to neural networks. Sequential backward feature selection was able to select genes for the models that produced good results, however the greedy algorithm has limitations. We found that genetic algorithms and simulated annealing were able to select genes that produced better results on both cell lines and patients. We found that combining cell line data sets from different types of cancers produced models that performed well at predicting outcome in cell lines and in patients, indicating that the method of action of chemotherapy drugs is similar across different types of cancer. The use of cell line trained machine learning models to predict patient chemotherapy response shows great promise, however future studies need to acquire larger cell line data sets and find better ways of evaluating the transfer of predictive ability of cell line trained models to patients.
Towards EEG Generation Using GANs for BCI Applications

Brain-computer interface has been always facing serious data-related problems such as lack of the sufficient data and data corruption. Artificial data generation is a potential solution to address these issues. Among generative techniques, the method of generative adversarial networks (GANs) with the successful applications in image processing has gained a lot of attention. The application of GANs for time-series data generation is a recent growing topic that first of all its feasibility needs to be assessed. In the present study, we investigate the performance of GANs in generating artificial electroencephalogram (EEG) signals. The results suggest that the generated EEG signals by GANs resemble the temporal, spectral, and spatial characteristics of real EEG. It thus opens new perspectives for further research in this area.
Remote sensing applied to decipher the origin of the Marajó Island , northern Brazil

The present study provides a detailed mapping and characterization of paleochannels in the northeasten Marajó Island using remote sensing (i.e., SRTM, Landsat 5-TM and Landsat 7-ETM+). The Arari Lake area is emphasized where a funnel-shaped morphology attributed to a Late Pleistocene paleoestuarine system was recognized previously to the lake development. Remote sensing data reveal that the inner estuary was connected to three fluvial channels that ran from the east/southeast throughout an area currently occupied by the Marajó Bay, implying that this bay did not exist yet by the time the estuarine system was active. Separation of the eastern side of Marajó Island due to establishment of the Tocantins River along northeastward-orientated faults would have interrupted the fluvial inflow, ultimately causing the abandonment of the estuarine system. Key-words: Remote sensing, Landsat, SRTM, Landscape evolution, Quaternary, Marajó Island, Arari Lake
Comprehensive Analysis of Academic Performance using Data Mining Techniques-with Special Reference to UG Class of Engineering

: One of the main objectives of Indian educational system is evaluating or enhancing the educational organization. Data Mining is the process of searching the concealed information from a large quantity of data set. It analyzes the data from different source and it converts into meaningful information. There are a lot of advantages of data mining technique in education sector. It has been one of valuable resources to determine success and failure of an organization. In an educational system, there is a need to manage and analyze the data for organization's performance. In this paper, we have discussed the approach regarding analyzing student's result data and professor’s result data, in order to monitor the progression of academic performance of students and professors for the purpose of making an effective decision by the academic planners. This leads to better results thereby increasing the profitability of the educational institutions especially in the private institutions.
OPTIMIZATION OF A BIOMETRIC SYSTEM IDENTIFICATION BY HAND GEOMETRY

In this paper the optimization of a very robust and novel system has been carried out to identify biometric by the hand geometry. Thus, a study between different type of geometric parameters like the distances between elements of the hand or the hand shape has been done. These parameters are used into two different types of classifiers, Neural Networks (NN) and Support Vector Machines (SVM). The best results have been found using the measures of distances in the hands, and when SVM is used, achieving over a 99%, with at least three hands for the training in the identification system. It has been employed a supervised classification system, utilizing therefore, two phases in the process of identification, one of training and another of test. For it, a database has been created with the particularity of a low resolution and a standard format.
The Use of NCTM Articles as Reading Assignments to Motivate Prospective Elementary Teacher Engagement in Mathematics Courses

In this study, we examine the use of assigning articles published in NCTM’s practitioner journals as readings in mathematics content courses for prospective elementary teachers (PTs). In particular, we study the articles’ roles in motivating PTs to engage in their content courses. As a conceptual foundation, we characterize NCTM articles as having potential to (1) increase PTs’ “buy-in” of pedagogical approaches used in content courses, (2) challenge PTs’ unproductive beliefs about mathematics, and (3) address mathematics content via children’s thinking. We plan to analyze an existing dataset of PTs’ online typed responses to assigned NCTM articles to identify whether and how their responses reflect increased motivation to engage in their content courses. We anticipate that our results will lead to an increased understanding of PTs’ actual experiences related to the assigned article readings.
Spherical Drone Display Research Laboratories

Magical technologies that create airborne images have long been featured in the world of science fiction, and there have been many studies on how to create these technologies in the real world. These include technologies known as Augmented Reality (AR)*1 to display images created by combining real scenes and computer graphics [1], image projection technologies using floating magnetic spheres [2], and laser technologies that generate plasma in the air [3]. Another of these technologies gaining a lot of attention in recent years involves drones fitted with light sources such as the Light Emitting Diodes (LEDs) to display the airborne images. For example, Intel’s “Shooting Star” system uses and simultaneously controls several hundred microdrones fitted with a high luminosity LEDs to produce a giant image floating in the air [4]. Another similar system is the Japanese company MicroAd’s SKY MAGIC image display system using drone groups [5]. These technologies are pioneering a new area of image display by establishing new methods to float images in the air. However, these current systems can only have one or several LED lights fitted to each drone, and when flying in groups, the number that can be simultaneously controlled is limited PoV Display Spherical Display Drone
Qualitative Analysis for Multiple Adaptation Loops

Using multiple adaptation loops enlarges the adaptation space of self-adaptive systems, but poses an adaptation chain problem. Adaptation loops are triggered not only by changes in the environment but also by changes caused by other adaptation loops. The chain of adaptations should be analyzed to validate specifications of these loops early in the development cycle of self-adaptive systems. In this paper, we focus on qualitative analysis of multiple adaptation loops to validate their specifications. Our analysis explores all possible adaptation chains and checks whether their loops are possible to lead the qualities of the managed system to the desired levels. To this end, we devise a process for constructing behavior models of adaptation loops and the managed system, and a tool for automatically transforming the behavior models into a formal model used for model checking with a reachability property. We present case studies based on extended Znn.com to validate the results of our qualitative analysis. Furthermore, we also evaluate the scalability of the analysis and show that it can be completed within a reasonable amount of time.
The Little Ice Age in Iberian mountains

The Little Ice Age (LIA) is known as one of the coldest stages of the Holocene. Most records from the Northern Hemisphere show evidence of significantly colder conditions during the LIA, which in some cases had substantial socio-economic consequences. In this study we investigated the magnitude and timing of climate variability during the LIA in the mountains of the Iberian Peninsula, based on a wide range of natural records (including from glacial, periglacial, and lacustrine/peatland areas; fluvial/alluvial deposits; speleothems; and tree rings), historical documents, and early instrument data. The onset of the LIA commenced in approximately CE 1300, and cold conditions with alternating moisture regimes persisted until approximately CE 1850; the environmental responses ranged from rapid (e.g. tree rings) to delayed (e.g. glaciers). The colder climate of the LIA was accompanied by severe droughts, floods, and cold/heat waves that showed significant spatio-temporal variation across the Iberian mountains. Several phases within the LIA have been detected, including (a) 1300–1480: increasing cooling with moderate climate oscillations; (b) 1480–1570: relatively warmer conditions; (c) 1570–1620: gradual cooling; (d) 1620–1715: coldest climate period of the LIA, particularly during the Maunder Minimum, with temperatures approximately 2 °C below those at present; (e) 1715–1760: warmer temperatures and a low frequency of extreme events; (f) 1760–1800: climate deterioration and more climate extremes (i.e. cold and heat waves, floods and droughts); (g) 1800–1850: highly variable climate conditions alternating with stability (1800–1815), extreme events (1815–1835), and a slight trend of warming associated with intense hydrometeorological events (1835–1850); (h) since 1850: a gradual staggered increase in temperature of approximately 1 °C. Post-LIA warming has led to substantial changes in geo-ecological dynamics, mainly through shrinking of the spatial domain affected by cold climate processes.
03 10 06 2 v 2 2 J an 2 00 4 Giant Gravitons in type IIA PP-wave Background

We examine giant gravitons with a worldvolume magnetic flux q in type IIA pp-wave background and find that they can move away from the origin along x direction in target space satisfying Rx = −q. This nontrivial relation can be regarded as a complementary relation of the giant graviton on IIA pp-wave and is shown to be connected to the spacetime uncertainty principle. The giant graviton is also investigated in a system of N D0-branes as a fuzzy sphere solution. It is observed that x enters into the fuzzy algebra as a deformation parameter. Such a background dependent Myers effect guarantees that we again get the crucial relation of our giant graviton. In the paper, we also find a BIon configuration on the giant graviton in this background.
Compression of Point Cloud Geometry with Random Access

The compression of point cloud geometry is a primary task in most immersive image compression technologies. The current standardization projects for compression of immersive image and video modalities show the importance foreseen for point cloud compression applications. In this poster we show results for the compression of point cloud geometry, when one imposes additional functionality requirements in the form of random access to various parts of the scene or object inside one point cloud. We first propose a method for segmenting a point cloud image based on the color attributes at each node, using the mean shift algorithm on several 2D projections, first independently, and then using a fusion approach, so that in the end we obtain a relatively small number of regions, i.e., clusters of 3D points. The next task is to compress the point cloud, with random access to each segment, by encoding independently in a lossless mode, using one octree for each segment. Finally we present results of compressing separately the disjoint point clouds as compared to the performance of compressing the initial point cloud in its entirety at once by a single octree.
Sangam : A Multi-component Core Cache Prefetcher

Data prefetching is an important technique used in all commercial processors. Data prefetchers aim at hiding the long data access latency. In this paper, we present the design of an L1 cache prefetcher that employs three different components organized in a hierarchical manner to address the diversity of access patterns. Each level of the prefetcher uses different amounts of control-flow and data-flow information. The first level of the prefetcher exploits the joint feature of instruction pointer (IP) and the current delta (i.e., control-flow as well as data-flow) in the access stream sourced by the current IP to predict a sequence of deltas with high confidence. To maintain high coverage, when the IP-delta-based prefetcher is unable to offer a high-confidence prediction, we invoke the second level of the prefetcher which employs an IP-based (i.e., only control-flow-based) stride predictor. If the IP-based stride predictor is also unable to discover any stride pattern with confidence, a next-line prefetcher with adaptive degree (only data-flow-based) is triggered at the third level of the prefetcher. The same three-level prefetcher, incorporated at the L2 cache, however, offers marginal benefits when working together with the L1 cache prefetcher. We also augment our threelevel L1 cache prefetcher with a technique to optimize L1 cache lookup bandwidth consumed by the prefetches. Additionally, our proposal incorporates a novel technique to maintain the aggressiveness of the L1 cache prefetcher even at the time of resource shortage in the L1 cache controller. Averaged over 46 single-thread SPEC CPU 2017 traces, our proposal achieves a 40.3% speedup over no prefetching. On a four-core configuration, averaged over 100 fourway multi-programmed workloads, our proposal achieves a 19.5% speedup over no prefetching.
Cache-Related Preemption Delays and Real-Time Scheduling : A Survey for Uniprocessor Systems

The trend in nowadays real-time embedded systems is to use commercial off-the-shelf components, and in particular CPUs with cache memories. But because of the way the cache is working, additional delays known as Cache-Related Preemption Delays (CRPDs) might occur as soon as preemptive scheduling is considered. These CRPDs make the predictability problem more complex and may even threaten the system schedulability. This article presents different existing strategies to deal with CRPD issues. These strategies can focus on reducing the CRPDs at the cache level or can work at the scheduling level to control the number of preemptions. Combinations between those different methods are also presented.
The 'golden view': data-driven governance in the scoring society

Drawing on the first comprehensive investigation into the uses of data analytics in UK public services, this article outlines developments and practices surrounding the upsurge in data-driven forms of what we term ‘citizen scoring’. This refers to the use of data analytics in government for the purposes of categorisation, assessment and prediction at both individual and population level. Combining Freedom of Information requests and semi-structured interviews with public sector workers and civil society organisations, we detail the practices surrounding these developments and the nature of concerns expressed by different stakeholder groups as a way to elicit the heterogeneity, tensions and negotiations that shape the contemporary landscape of data-driven governance. Described by practitioners as a way to achieve a ‘golden view’ of populations, we argue that data systems need to be situated in this context in order to understand the wider politics of such a ‘view’ and the implications this has for state-citizen relations in the scoring society.
Performance comparison of parallel turbulent noise evaluation with different gradient selection methods

Noise is of vital interest in many parts of computer sciene, especially in the computer graphics field where noise is used to create nature-like effects. Perlin’s 1985 algorithm to generate noise remains the most popular in spite of many alternatives having been presented over the years. In this report we have examined the execution time impact of two new gradient table data structures and a new hash method for this algorithm, suggested by Perlin in 2002 and Olano in 2005 respectively. Our implementation simulated turbulence and ran in parallel on a modern GPU using the OpenCL framework. We also examined if the turbulence method’s octave summation could benefit from parallelization. Results suggest that Olano’s hash method performs significantly faster, while Perlin’s original gradient table data structure performs slightly faster than the suggested improvements. We also found that a parallelization of the octave summation in the turbulence method performs significantly faster.
REGIONAL ECONOMIC INTEGRATION ? CHALLENGES CONFRONTING THE OIC COUNTRIES

As a significant subset of the developing world, the member countries of the Organisation of the Islamic Conference (OICMCs) will ine vitably be faced with the serious impact of globalisation on the one hand, and with the negative as much as the positive implications of regionalisation on the other. Their own efforts over a period of more than two decades to enhance regional co-operation and i ntegration at the OIC level seem to have made little impact. In view of the c onfusing or unpredictable trends witnessed in the world economy at the threshold of the 21s t century, the question “where to go from here” is pertinent to raise. Internal dynamics and exogenous change s at the global level will certainly force the OIC countri es to adjust and reform at the national level, while configuring, if possible, viable course s of action to enhance intraregional co-operation/integration. This paper attempts to review the emerging trends in global and regional integration and to consider their implications for the OIC countries in the light of the latter’s efforts over the last two de cades.
Chapter 8 Relations Old and New

We note that (binary) relations on a set form a (partially) ordered monoid with involution, which is also residuated and complete, hence a quantale. Relations between sets form an ordered category with involution. If p : A f+ Band a : B f+ C, one defines a p : A f+ C by c(ap)a ¢:} 3bEB(cab V bpa), for all a E A and c E C, and p v : B f+ A by apvb ~ bpa. The partial order between relations A f+ B is defined elementwise. We shall discuss some appearances of relations in anthropology, linguistics, computer science, algebra and category theory.
Discrete-multitone modulation for short distance 100 Gbit / s optical links

The commercially available 100 Gbit/s optical channel employs coherent modulation and demodulation allowing transmission over long distances, up to thousands of kilometers, but for short distances the cost of a coherent receiver can be overkill. In literature there are several techniques, based on advanced different digital modulation schemes, that reduce the complexity of the transmitter or the receiver, allowing a more costeffective solution. The contribution of this work is the design and implementation of a communication system employing Discrete Multitone (DMT) modulation with bit and power loading, transmitted using an externally modulated laser in C-band, optically amplified, and received with a single photodiode (direct detection). After the design of the system followed by numerical simulations, it has been experimentally tested both in back-to-back and over several spans of Standard Single-Mode optical Fibers (SSMFs), up to 80 km. We studied and adopted Single Side-Band (SSB) at the transmitter to increase spectral efficiency and reduce the effects of Chromatic Dispersion (CD). This work is organized as follows. After the introduction, in chapter 2 we present the main concepts of optical communications. Chapter 3 presents the theory of multi-channel modulation (such as DMT), comparing it with single-channel techniques. Chapter 4 shows channel capacity for multi-channel modulation scheme and the Levin-Campello bit and power loading algorithm. After reviewing the state-of-the-art, in chapter 5 is presented the system design, along with the algorithms we developed, and the experimental setup; the results are showed in chapter 6. The results we obtained are very promising since we measured a pre-Forward Error Correction (FEC) Bit Error Rate (BER) below the standard hard-decision FEC threshold. Chapter 7 presents the conclusions of this work.
Appropriate Measures for Retention Forestry

In this review I attempt to reveal the significance of life history and fractal organisation theory for retention forestry. The retention approach has emerged from the recognition that even intense natural disturbances leave biological legacies and spatial heterogeneity in the new forest which contrasts with the simple and homogeneous environment that is often the outcome of traditional harvesting practices. The review presents some insight into the understanding of a complex, self-organising dynamical system that supports organismal units being its branches and leaves in a tree, or a wide variety of flora and fauna in a forest. It seems that the formation of a scale-invariant structure of life cycle events may fit in the general terms of homeostasis, lifespan and fitness, so it could be argued that habitat quality and integrity of forest communities should be evaluated based on the assessment of organism-specific effects and responses in ecosystem functioning. Moreover, biological legacies defined as biologically created patterns that persist from the pre-disturbance ecosystem and influence recovery processes in the post-disturbance ecosystem should include organically derived measures of ecosystem integrity, such as habitat quality.
Notes on Latent Structure Models and SPIGOT

These notes aim to shed light on the recently proposed structured projected intermediate gradient optimization technique (SPIGOT, Peng et al., 2018). SPIGOT is a variant of the straight-through estimator (Bengio et al., 2013) which bypasses gradients of the argmax function by back-propagating a surrogate "gradient. " We provide a new interpretation to the proposed gradient and put this technique into perspective, linking it to other methods for training neural networks with discrete latent variables. As a by-product, we suggest alternate variants of SPIGOT which will be further explored in future work.