Miracle-dz/UniFAR-FTUs huggingface dataset:
A parsimonious regional parametric evapotranspiration 1 model based on a simplification of the Penman-Monteith 2 formula 3 4

Evapotranspiration is a key hydrometeorological process and its estimation is important in 13 many fields of hydrological and agricultural sciences. Simplified estimation proves very useful in 14 absence of a complete data set. In this respect, a parametric model based on simplification of the 15 Penman-Monteith formulation is presented. The basic idea of the parametric model is the 16 replacement of some of the variables and constants that are used in the standard Penman-Monteith 17 model by regionally varying parameters, which are estimated through calibration. The model is 18 implemented in various climates on monthly time step (USA, Germany, Spain) and compared on 19 the same basis with four radiation-based methods (Jensen-Haise, McGuiness and Bordne, 20 Hargreaves and Oudin) and two temperature-based (Thornthwaite and Blaney-Criddle). The 21 methodology yields very good results with high efficiency indexes, outperforming the other models. 22 Finally, a spatial analysis including the correlation of parameters with latitude and elevation 23 together with their regionalization through three common spatial interpolation techniques along 24 with a recent approach (Bilinear Surface Smoothing), is performed. Also, the model is validated 25 against Penman-Monteith estimates in eleven stations of the well-known CIMIS network. The total 26 framework which includes the development, the implementation, the comparison and the mapping 27
Sex Identification in the Chinese Softshell Turtle Pelodiscus sinensis ( Trionychidae ) Using the SBNO 1 Gene

The Chinese softshell turtle exhibits ZZ/ZW sex determination. To identify the sex of embryos, juvenile and adult individuals, we designed two pairs PCR primers, SB1-196 which amplify a fragment of 196 bp in the female and the other, CK1-482, amplify 482 bp fragment in both the sexes. It is validated in 24 adult turtles of known sex, sampled from three different locations. This one-step sexing technique is rapid and easy to perform, and reported for the first time.
CS 229 Final Project Re-Alignment Improvements for Deep Neural Networks on Speech Recognition Systems

The task of automatic speech recognition has traditionally been accomplished by using Hidden Markov Models (HMM), which are effective in modeling time-varying spectral vector sequences . Gaussian Mixture Models (GMM) have been used to determine how well each state of the HMM fits a frame or a short window of frames of coefficients that represents the acoustic input. However, recent research has shown that current speech recognition systems that use Deep Neural Networks (DNN) with many hidden layers can outperform GMMs on a variety of speech recognition benchmarks, sometimes by a large margin [2], . We investigate further possible improvements of the DNN-HMM hybrid, by examining the role of forced alignments in the training algorithm.
ASSETS : U : Using Telepresence Robot to Improve Self-Efficacy of People with Developmental Disabilities

People with Developmental Disabilities (DD) often rely on other people to perform basic activities such as leaving the house and accessing public spaces. This problem, exaggerated by a decrease in community engagement, has been documented to decrease their sense of self-efficacy. Telepresence robots provide a unique opportunity for people with DD to access public spaces, particularly for those who are homebound or dependent on others for using transportation or buying exhibit tickets. This research evaluates the use of telepresence robots operated by people with DD in exploring a public exhibit. This study was in partnership with Hope Services, an organization that provides skill-improving activities for people with DD. Our analysis consisted of quantitative and qualitative methods using data from semi-structured preand post-interviews focusing on participants’ sense of physical and social selfefficacy, and well-being. Our study revealed positive trends toward showing that using telepresence can contribute to wellbeing and physical and social self-efficacy. Therefore, we believe that there is some promise for using telepresence robots to tour an exploratory space for people with DD and that it can be a viable option for those who face accessibility limitations.
Learning-based Model Predictive Control for Safe Exploration and Reinforcement Learning

Reinforcement learning has been successfully used to solve difficult tasks in complex unknown environments. However, these methods typically do not provide any safety guarantees during the learning process. This is particularly problematic, since reinforcement learning agent actively explore their environment. This prevents their use in safety-critical, real-world applications. In this paper, we present a learning-based model predictive control scheme that provides high-probability safety guarantees throughout the learning process. Based on a reliable statistical model, we construct provably accurate confidence intervals on predicted trajectories. Unlike previous approaches, we allow for input-dependent uncertainties. Based on these reliable predictions, we guarantee that trajectories satisfy safety constraints. Moreover, we use a terminal set constraint to recursively guarantee the existence of safe control actions at every iteration. We evaluate the resulting algorithm to safely explore the dynamics of an inverted pendulum and to solve a reinforcement learning task on a cart-pole system with safety constraints.
Fatty acids analysis of Ficus religiosa stem bark by gas chromatography-mass spectrometry

1Department of Pharmacognosy and Phytochemistry, Phytochemistry Research Laboratory, Faculty of Pharmacy, Jamia Hamdard, New Delhi‐110062, INDIA. 2Department of Natural Products and Alternative Medicine, College of Clinical Pharmacy, Imam Abdulrahman Bin Faisal University, Dammam‐31441, KINGDOM OF SAUDI ARABIA. 3Regional Research Institute of Unani Medicine, Central Council of Research Unani Medicine, Aligarh‐202001, INDIA.
OCR learned by Structured Output Perceptron VF 1

We will consider a problem of recognizing a given name from images of handwritten characters. The inputs are a binary images containing a line of text. All characters have a fixed width so that we do not have to solve the segmentation problem. A sample of the input images is shown in Figure 1. A relative frequency of the names is in Figure 2. In Section 2 we will describe three linear classifiers for sequence recognition, each using a different model of the sequences. Your task will be to learn parameters of the three classifiers by the Perceptron algorithm and to evaluate their performance. Links to the data are provided in Section 3. The assignments to solve are described in Section 4.
Machine Learning for Instance Selection in SMT Solving

SMT solvers are among the most suited tools for quantifier-free first-order problems, and their support for quantified formulas has been improving in recent years. To instantiate quantifiers, they rely on heuristic techniques that generate thousands of instances, most of them useless. We propose to apply state-of-the-art machine learning techniques as classifiers for instances on top of the instantiation process. We show that such techniques can indeed decrease the number of generated useless instances. We envision that this could lead to more efficient SMT solving for quantified problems. Satisfiability-modulo-theories (SMT) solvers are among the best backends for verification tools and "hammers" in proof assistants. When proof obligations contain quantified formulas, SMT solvers rely on instantiation, replacing quantified subformulas by sets of ground instances. Three main techniques have been designed: enumerative [11], trigger-based [4], and conflict-based [12] instantiation. Among these, only conflict-based instantiation computes instances that are guaranteed to be relevant, but it is incomplete and is normally used in combination with other techniques. Enumerative and trigger-based techniques are highly heuristic and generate a large number of instances, most of them useless. As a result, the search space of the solver explodes. Reducing the number of instances could improve the solver's efficiency and success rate within a given time limit. We propose to use a state-of-the-art machine learning algorithm as a predictor over the generated set of instances to filter out irrelevant instances, and thus decrease the number of instances given to the ground solver. The predictor is invoked after each instantiation round to rate the potential usefulness of each generated instance. Several strategies are then used to build a subset of potentially relevant instances that are immediately added to the ground solver. Adding the other instances is postponed. We conducted our experiment in veriT [2], an SMT solver that implements all three in-stantiation techniques described above. We chose as predictor the XGBoost gradient boosting toolkit [3] with the binary classification objective. This configuration had already been used successfully in the context of theorem proving [6, 10]. Choosing a suitable set of features is crucial for effective machine learning. The features determine how precise the representation of the problem is. Previous works already investigate features for theorem proving [1, 5, 6, 8-10]. Our features are more specifically inspired by ENIGMA [6] and RLCoP [7]. They are basically term symbols and term walks with symbol sequences projected to features using Vowpal Wabbit hashing. Term variables and Skolem constants are translated analogously to constants. The model is further enriched with abstract features such as term size, term depth, and the number of instances. To encode our problem into sparse vectors, we use three kinds of information available to the solver: the ground part of the formula (set of literals l 1 ,. . , l m), the quantified formula
Association of Uric Acid with Antioxidant Capacity of Plasma in Patients with Type 2 Diabetic Nephropathy

1 Department of Biochemistry,and Genetic , Faculty of Medicine, Mazandaran,Sari, Iran. 2 Department of Biochemistry, Molecular and Cell Biology Research Center, Faculty of Medicine, Mazandaran University of Medical Sciences, Sari, Iran. 3 Department of Nephrology, Imam Khomeini Hospital, Mazandaran University of Medical Sciences, Sari, Iran. 4 Dokht Iran, high school, education office district 2, Sari, Iran.
Unanimous Jury Voting with an Ambiguous Likelihood *

We study collective decision-making in a voting game with an ambiguous likelihood and ambiguity-averse voters who are MaxMin Expected Utility maximizers. We demonstrate that there are instances when, in equilibrium, ambiguity helps support voting informatively under the unanimity rule. We further investigate related voting behaviors in a laboratory experiment. Our results guide the use of unanimity voting with the aim of minimizing type I errors as a function of the jury size, the threshold of reasonable doubt and the severity of the ambiguous likelihood.
Malware Looks Familiar : Laymen Identify Malware Run-time Similarity with Cherno faces and Stick Figures

Classifying unknown malicious binaries into malware families provides valuable information to security professionals. e task of recognizing a malicious binary (i.e., aributing it to a previously observed aack paern) is widely considered a dicult task requiring extensive domain expertise. In this work, we oer a new approach which focuses on transforming the the recognition problem domain from system traces to Cherno faces, thereby engaging the facial recognition aptitude of laymen. To do so we (i) curated a expert tagged dataset of malware variants, (ii) instrumented behavior trace monitors for each variant, (iii) constructed a simple, graph based feature set from the runtime behavior, and (iv) visualized low-dimensional representations of these system call graphs with stick gures and Cherno faces. We then selected the three families with the largest variation and asked non-experts on Amazon Mechanical Turk to classify binaries between these three families using the generated visual representations, a task that would otherwise be delegated to experts. We found that non-experts completed the task with between 63% and ∗Main contributions were while employed at CMU Soware Engineering Institute. †Main contributions were while employed at CMU Soware Engineering Institute. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for prot or commercial advantage and that copies bear this notice and the full citation on the rst page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permied. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specic permission and/or a fee. Request permissions from permissions@acm.org. BICT’17, Hoboken, New Jersey, USA © 2017 ACM. 978-x-xxxx-xxxx-x/YY/MM. $15.00 DOI: 10.1145/nnnnnnn.nnnnnnn 86% accuracy, and when aggregated, these non-expert labels successfully trained a classier to a similar level of performance as the ground truth labels. Although simple, the experiments conducted provide a novel evaluation of the inherent diculty of malware recognition tasks. Additionally new operational possibilities for eective human in the loop malware recognition are indicated and discussed as future work within the research prospectus. ACM Reference format: Nathan VanHoudnos, William Casey, David French, Brian Lindauer, Eliezer Kanal, Evan Wright, Bronwyn Woods, Seungwhan Moon, Peter Jansen, and Jaime Carbonell. 2017. is Malware Looks Familiar: Laymen Identify Malware Run-time Similarity with Cherno faces and Stick Figures. In Proceedings of EAI International Conference on Bio-inspired Information and Communications Technologies, Hoboken, New Jersey, USA, March 2017
Leveraging Existing Microarchitectural Structures to Improve First-Level Caching Efficiency

Alves, R. 2019. Leveraging Existing Microarchitectural Structures to Improve First-Level Caching Efficiency. Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 1821. 42 pp. Uppsala: Acta Universitatis Upsaliensis. ISBN 978-91-513-0681-0. Low-latency data access is essential for performance. To achieve this, processors use fast firstlevel caches combined with out-of-order execution, to decrease and hide memory access latency respectively. While these approaches are effective for performance, they cost significant energy, leading to the development of many techniques that require designers to trade-off performance and efficiency. Way-prediction and filter caches are two of the most common strategies for improving firstlevel cache energy efficiency while still minimizing latency. They both have compromises as way-prediction trades off some latency for better energy efficiency, while filter caches trade off some energy efficiency for lower latency. However, these strategies are not mutually exclusive. By borrowing elements from both, and taking into account SRAM memory layout limitations, we proposed a novel MRU-L0 cache that mitigates many of their shortcomings while preserving their benefits. Moreover, while first-level caches are tightly integrated into the cpu pipeline, existing work on these techniques largely ignores the impact they have on instruction scheduling. We show that the variable hit latency introduced by way-misspredictions causes instruction replays of load dependent instruction chains, which hurts performance and efficiency. We study this effect and propose a variable latency cache-hit instruction scheduler, that identifies potential misschedulings, reduces instruction replays, reduces negative performance impact, and further improves cache energy efficiency. Modern pipelines also employ sophisticated execution strategies to hide memory latency and improve performance. While their primary use is for performance and correctness, they require intermediate storage that can be used as a cache as well. In this work we demonstrate how the store-buffer, paired with the memory dependency predictor, can be used to efficiently cache dirty data; and how the physical register file, paired with a value predictor, can be used to efficiently cache clean data. These strategies not only improve both performance and energy, but do so with no additional storage and minimal additional complexity, since they recycle existing cpu structures to detect reuse, memory ordering violations, and misspeculations.
RF Analog Impairments Modeling for Communication Systems Simulation: Application to OFDM-based Transceivers

With the growing complexity of personal mobile communication systems demanding higher data-rates and high levels of integration using low-cost CMOS technology, overall system performance has become more sensitive to RF analog front-end impairments. Designing integrated transceivers requires a thorough understanding of the whole transceiver chain including RF analog front-end and digital baseband. Communication system engineers have to include RF analog imperfections in their simulation benches in order to study and quantify their impact on the system performance.
Appendices for Amortized Monte Carlo Integration

Figure 4: Additional results for one-dimensional tail integral example as per Figure 1a. [left] Relative mean squared errors (as per (25)). [right] Mean squared error E[(μ(y, θ) − μ̂(y, θ)]. Conventions as per Figure 1. The results for SNIS q1 indicate that it severely underestimates E2 leading to very large errors, especially when the mismatch between p(x|y) and f(x; θ) is as significant as in the tail integral case.
SIGNAL AND DETECTOR RANDOMIZATION FOR MULTIUSER AND MULTICHANNEL COMMUNICATION SYSTEMS

SIGNAL AND DETECTOR RANDOMIZATION FOR MULTIUSER AND MULTICHANNEL COMMUNICATION SYSTEMS Mehmet Emin Tutay Ph.D. in Electrical and Electronics Engineering Supervisor: Assoc. Prof. Dr. Sinan Gezici November 2013 Randomization can be considered as a possible approach to enhance error performance of communication systems subject to average power constraints. In the first part of this dissertation, we consider downlink of a multiuser communications system subject to an average power constraint, where randomization is employed at the transmitter and receiver sides by modeling signal levels as random variables (stochastic signals) and employing different sets of detectors via time-sharing (detector randomization), respectively. In the second part, we consider single-user systems, where we assume that there exist multiple channels between the transmitter and receiver with arbitrary noise distributions over each of them and only one of the channels can be employed for transmission at any given time. In this case, randomization is performed by choosing the channel in use according to some probability mass function and employing stochastic signaling at the transmitter. First, the jointly optimal power control with signal constellation randomization is proposed for the downlink of a multiuser communications system. Unlike a conventional system in which a fixed signal constellation is employed for all the
FACTORS AFFECTING MANURE QUANTITY, QUALITY, AND USE

Over-application of nutrients to land leads to losses of fertilizer nutrients and is a threat to the environmental standards we want, especially with respect to water quality. Manure management and application has been specifically targeted by regulatory agencies in recent years to try to assure that losses are low and to avoid environmental consequences off-site. Previous papers, including one given at this conference in 1995, described principles of nutrient budgeting that are important to fully utilize fertilizer value of manure and avoid overapplication and resulting pollution of ground and surface waters (Van Horn et al. 1995, 1996, 1997, 1998). A nutrient management plan, or budget includes:
Chapter 7 MULTISENSORY INTEGRATION

A fast, reliable and unambiguous perception of the environment requires the integration of all the information simultaneously acquired and processed by different perceptual systems. However, research has traditionally focused mostly on the processing of unimodal stimuli, which has effectively left unanswered the questions of how exactly information processed by different perceptual systems is integrated to give unified and coherent perceptual experiences. Only in recent years has the interest on multimodal perception increased and, with it, the number of experimental studies that investigate this issue. We now know that the integration of multimodal information is something more than the simple adding together of information from the different senses. It has been shown, for example, that the perceived position from which a sound originates can be affected by the visual perception of an object, simultaneous presented (the ventriloquist effect). We also know that the perception of facial emotions can be affected by the hearing of vocal expressions of emotions, or that people can report felt textures as being seen or visual textures as being touched (when cross-modal illusory conjunctions are perceived). In this chapter we will review evidence on cross-modal sensory integration, focusing in particular on spatial integration, spatial attention, object identification and feature binding. Caterina Cinel and Glyn Humphreys 4
THE EFFECTS OF DIFFERENT NUTRIENT COMPOSITION IN THE FEED ON THE MUSCLE FIBERS TYPOLOGY IN PIGS

The study deals with the effects of different levels of crude protein in the feed on the muscle fibers typology in pigs. The test included 72 hybrid animals (barrows and gilts) of the Dx(LWDxL) genotype. The pigs were fattened ad-libitum in three feeding phases. All animals were divided into 3 groups according to the crude protein (CP) level intake. In order to monitor the quantitative (number) and qualitative (area, diameter) parameters of the muscle fibers, there were muscle samples taken from the musculus longissimus lumborum et thoracis (MLLT) muscle following the animals‘ slaughter. The study showed that pigs with the highest amount of rapeseed meal level in their diet showed the smallest (P<0.01) slow oxidative muscle I type fibers area (2 292 μm). Furthermore, the highest (P<0.001) percentage of the type I muscle fibers (16 %) and their number/1 mm (31.0) were observed in the same group. The results show that different crude protein levels in the pig diet can affect their muscle fibers typology.
Visualizing Intrinsic Space for Spatial Data via Input Regularized Gaussian Process Latent Variable Models

We propose the input-regularized Gaussian process latent variable model for visualizing a latent intrinsic input space that improves interpolation performance in regression tasks. The proposed model assumes that a latent location is associated with each observed input location, and the covariance function is determined by distance between the latent locations. The latent locations are estimated so that the output covariance of the given data is appropriately captured by the latent locations while preserving the neighbor relationships between the observed input space and the latent space by input regularization. When the input regularization is omitted, the proposed model reduces to the Gaussian process latent variable model. When the input regularization is strong enough to perfectly preserve the neighbor relationships, the proposed model becomes Gaussian process regression. The degree of the regularization is controlled by a hyperparameter, which can be automatically selected by cross-validation using the given data. We demonstrate the effectiveness of the proposed model with real-world spatial data sets in terms of interpolation performance of multiple output values.