Miracle-dz/UniFAR-FTUs huggingface dataset:
Costs and Tradeoffs of Resistance and Tolerance to Belowground Herbivory in Potato

The success of sustainable crop production depends on our ability to select or create varieties that can allocate resources to both growth and defence. However, breeding efforts have emphasized increases in yields but have partially neglected defence traits against pests. Estimating the costs of multiple defences against tuber herbivores and the tradeoffs among them, as well as understanding the relationship between yield and multiple defences is still unknown but relevant to both basic and applied ecology. Using twenty commercial potato varieties available in Colombia and the tuber herbivore Tecia solanivora, we tested whether high yielding varieties show a reduction in three types of defence: constitutive and induced resistance, as well as tolerance. Specifically, we determined (1) the costs in terms of yield of all three defences, (2) the possible tradeoffs among them, and (3) if oviposition preference was related to the expression of these defences. We detected no costs in terms of yield of constitutive and induced resistance to tuber damage. We did, however, find evidence of costs of being able to tolerate tuber herbivory. While we found no tradeoffs among any of the estimated defences, there was a positive correlation between aboveground compensatory growth and tolerance in terms of tuber production, suggesting that after damage there are no shifts in the allocation of resources from aboveground to belowground biomass. Finally, we found that females laid more eggs on those varieties with the lowest level of constitutive resistance. In conclusion our findings suggest that in potatoes, breeding for higher yields has not caused any reduction in constitutive or induced resistance to tuber damage. This is not the case for tolerance where those varieties with higher yields are also less likely to tolerate tuber damage. Given the high incidence of tuber pests in Colombia, selecting for higher tolerance could allow for high productivity in the presence of herbivores. Finding mechanisms to decouple the tolerance response from yield should be a new priority in potato breeding in Colombia to guarantee a higher yield in both the presence and absence of herbivores.
Making strategy at different stages of the industry life-cycle - relationships with small firm performance

This article proposes that individual small firms, just like large firms, use different approaches to make strategy. Three processes relevant to small firms are identified: simplistic, participative and adaptive. The article examines how these processes are related to performance, depending on industry life cycle stage. Empirical analysis indicates that all three approaches are related to small firm performance, but that the importance and impact of these relationships will change according to the industry life cycle stage. Markedly, and contrary to evidence from studies in large firms, small firms in mature industries are most likely to benefit from using adaptive strategy-making processes.
Motion Balance Filtering

This paper presents a new technique called motion balance filtering, which corrects an unbalanced motion to a balanced one while preserving the original motion characteristics as much as possible. Differently from previous approaches that deal only with the balance of static posture, we solve the problem of balancing a dynamic motion. We achieve dynamic balance by analyzing and controlling the trajectory of the zero moment point (ZMP). Our algorithm consists of three steps. First, it analyzes the ZMP trajectory to find out the duration in which dynamic balance is violated. Dynamic imbalance is identified by the ZMP trajectory segments lying out of the supporting area. Next, the algorithm modifies the ZMP trajectory by projecting it into the supporting area. Finally, it generates the balanced motion that satisfies the new ZMP constraint. This process is formulated as a constrained optimization problem so that the new motion resembles the original motion as much as possible. Experiments prove that our motion balance filtering algorithm is a useful method to add physical realism to a kinematically edited motion.
Adaptive Budget for Online Learning

Although the perceptron algorithm has been considered a simple supervised learning algorithm, it has the advantage of learning from the training data set one at a time. This makes it more suitable for online learning tasks and new families of kernelized perceptrons have been shown to be effective in handling streaming data. However, the amount of memory required for storing the online model which grows without any limits and the consequent excessive computation and time complexity makes this framework infeasible in real problems. A common solution to this restriction is to limit the allowed budget size and discard some of the examples in the memory when the budget size is exceeded. In this paper we present a framework for choosing a proper adaptive budget size based on underlying properties of data streams. The experimental results on several synthetic and real data sets show the efficiency of our proposed system compared to other algorithms.
Performance Measurement Via Random Portfolios

Problems with performance measurement using information ratios relative to a benchmark are exposed. Random portfolios (that obey constraints but disregard utility) are shown to measure investment skill effectively. Investment mandates can also be based on random portfolios - this allows active fund managers more freedom to implement their ideas, and provides the investor more flexibility to gain utility. The issue of the proper attitude towards tracking error is broached, but left largely undecided. There is also a critique of Fisher's method of combining p-values that shows Stouffer's method to be preferable.
Chromatin Organization by Repetitive Elements (CORE): A Genomic Principle for the Higher-Order Structure of Chromosomes

Eukaryotic genomes contain a large amount of DNA repeats (also known as repetitive DNA, repetitive elements, and repetitive sequences). Here, I propose a role of repetitive DNA in the formation of higher-order structures of chromosomes. The central idea of this theory is that chromatin regions with repetitive sequences pair with regions harboring homologous repeats and that such somatic repeat pairing (RP) assembles repetitive DNA chromatin into compact chromosomal domains that specify chromatin folding in a site-directed manner. According to this theory, DNA repeats are not randomly distributed in the genome. Instead, they form a core framework that coordinates the architecture of chromosomes. In contrast to the viewpoint that DNA repeats are genomic ‘junk’, this theory advocates that repetitive sequences are chromatin organizer modules that determine chromatin-chromatin contact points within chromosomes. This novel concept, if correct, would suggest that DNA repeats in the linear genome encode a blueprint for higher-order chromosomal organization.
Exposing Digital Image Forgeries from Near Duplicate Images

Nowadays image modification is easy due to availability of powerful digital image editing software. Images are powerful tool for communication, after images are posted on internet, other users can copy resize and re-encode them and then repost their version by generating similar but not identical copies. The main task is to find out original image between near duplicate image is difficult. It is difficult to adjust the illumination condition when Image composition or splicing operation performed on image to create composite image. Due to these illumination inconsistencies gives clue to detect forgery. But method that operate on illuminant color prone to estimation error, further improvement can be achieved by advanced illuminant estimator as skin color of the faces automatically detected in the image. After finding out the original image relationship within set of near duplicate images is shown by Image Phylogeny Tree (IPT) to finding the structure of transformation and their parameters among near duplicate image. The Technique is applicable to images containing two or more people.
Characterization of cell death inducing Phytophthora capsici CRN effectors suggests diverse activities in the host nucleus

Plant-Microbe interactions are complex associations that feature recognition of Pathogen Associated Molecular Patterns by the plant immune system and dampening of subsequent responses by pathogen encoded secreted effectors. With large effector repertoires now identified in a range of sequenced microbial genomes, much attention centers on understanding their roles in immunity or disease. These studies not only allow identification of pathogen virulence factors and strategies, they also provide an important molecular toolset suited for studying immunity in plants. The Phytophthora intracellular effector repertoire encodes a large class of proteins that translocate into host cells and exclusively target the host nucleus. Recent functional studies have implicated the CRN protein family as an important class of diverse effectors that target distinct subnuclear compartments and modify host cell signaling. Here, we characterized three necrosis inducing CRNs and show that there are differences in the levels of cell death. We show that only expression of CRN20_624 has an additive effect on PAMP induced cell death but not AVR3a induced ETI. Given their distinctive phenotypes, we assessed localization of each CRN with a set of nuclear markers and found clear differences in CRN subnuclear distribution patterns. These assays also revealed that expression of CRN83_152 leads to a distinct change in nuclear chromatin organization, suggesting a distinct series of events that leads to cell death upon over-expression. Taken together, our results suggest diverse functions carried by CRN C-termini, which can be exploited to identify novel processes that take place in the host nucleus and are required for immunity or susceptibility.
Sunyaev-Zeldovich Effect-derived Distances to the High-Redshift Clusters MS 0451.6–0305 and Cl 0016+16

We determine the distances to the z ≃ 0.55 galaxy clusters MS 0451.6-0305 and Cl 0016+16 from a maximum-likelihood joint fit to interferometric Sunyaev-Zeldovich effect (SZE) and X-ray observations. We model the intracluster medium (ICM) using a spherical isothermal β model. We quantify the statistical and systematic uncertainties inherent to these direct distance measurements, and we determine constraints on the Hubble parameter for three different cosmologies. For an ΩM = 0.3, ΩΛ = 0.7 cosmology, these distances imply a Hubble constant of 63 km s-1 Mpc-1, where the uncertainties correspond to statistical followed by systematic at 68% confidence. The best-fit H0 is 57 km s-1 Mpc-1 for an open (ΩM = 0.3) universe and 52 km s-1 Mpc-1 for a flat (ΩM = 1) universe.
Improved Minimum Phone Error based Discriminative Training of Acoustic Models for Mandarin Large Vocabulary Continuous Speech Recognition

This paper considers minimum phone error (MPE) based discriminative training of acoustic models for Mandarin broadcast news recognition. We present a new phone accuracy function based on the frame-level accuracy of hypothesized phone arcs instead of using the raw phone accuracy function of MPE training. Moreover, a novel data selection approach based on the frame-level normalized entropy of Gaussian posterior probabilities obtained from the word lattice of the training utterance is explored. It has the merit of making the training algorithm focus much more on the training statistics of those frame samples that center nearly around the decision boundary for better discrimination. The underlying characteristics of the presented approaches are extensively investigated, and their performance is verified by comparison with the standard MPE training approach as well as the other related work. Experiments conducted on broadcast news collected in Taiwan demonstrate that the integration of the frame-level phone accuracy calculation and data selection yields slight but consistent improvements over the baseline system.
Bag Of ARCS: New representation of speech segment features based on finite state machines

This paper proposes a new feature representation, Bag Of Arcs (BOA) for speech segments. A speech segment in BOA is simply represented as a set of counts for unique arcs in a finite state machine. Similar to the Bag Of Words model (BOW), BOA disregards the order of arcs, and thus, efficiently models speech segments. A strong motivation to use BOA is provided by a fact that the BOA representation is tightly connected to the output of a Weighted Finite State Transducer (WFST) based ASR decoder. Thus, BOA directly represents elements in the search network of a WFST-based ASR decoder, and can include information about context-dependent HMM topologies, lexicons, and back-off smoothed n-gram networks. In addition, the counts of BOA are accumulated by using the WFST decoder output directly, and we do not require an additional overhead and a change of decoding algorithms to extract the features. Consequently, we can combine the ASR decoder and post-processing without a process to extract word features from the decoder outputs or re-compiling WFST networks. We show the effectiveness of the proposed approach for some ASR post-processing applications in utterance classification experiments, and in speaker adaptation experiments by achieving absolute 1% improvement in WER from baseline results. We also show examples of latent semantic analysis for BOA by using latent Dirichlet allocation.
Light Field Techniques for Reflections and Refractions

Reflections and refractions are important visual effects that have long been considered too costly for interactive applications. Although most contemporary graphics hardware supports reflections off curved surfaces in the form of environment maps, refractions in thick, solid objects cannot be handled with this approach, and the simplifying assumptions of environment maps also produce visible artifacts for reflections. Only recently have researchers developed techniques for the interactive rendering of true reflections and refractions in curved objects. This paper introduces a new, light field based approach to achieving this goal. The method is based on a strict decoupling of geometry and illumination. Hardware support for all stages of the technique is possible through existing extensions of the OpenGL rendering pipeline. In addition, we also discuss storage issues and introduce methods for handling vector-quantized data with graphics hardware.
Optimal Policy for Macro-Financial Stability

There is a new and now large literature analyzing government policies for financial stability based on models with endogenous borrowing constraints. These normative analyses build upon the concept of constrained efficient allocation where the social planner is constrained by the same borrowing limit that agents face. In this paper, we show that there exists at least one set of tools implementing the constrained efficient allocation that can also be used by a Ramsey planner to replicate an unconstrained allocation, achieving higher welfare. Constrained efficiency may lead to inaccurate characterizations of welfare maximizing policies relative to Ramsey optimal policy. (JEL E32, E44, E61, G01, H21)
Road vehicle detection and classification based on Deep Neural Network

The deep learning is a growing multi-layer neural network learning algorithm in the field of machine learning in recent years. Firstly, this paper analyzes the superiority of the deep learning at the aspect of feature extraction. Aimed at the lack of feature expression capacity and curse of dimensionality results from excessive feature dimensions of shallow learning, this paper proposes that using deep learning can extract high-lever features from low-lever features though its given layer structure. Secondly, the deep learning algorithm is applied in the case of road vehicle detection. Based on the traditional method, such as neural network the deep learning structure is further studied to increase the performance of feature extraction and classification recognition. Also, some tests are run in the Matlab software. The tests results show that with the increasing the amount of the data, the mean error and misclassification rate gradually decrease, so this algorithm based on the neural network has good superiority and adaptability of the deep learning. Finally, this paper proposes some suggestions for the improvement of the algorithm and prospects the development direction of the deep learning in the field of machine learning and artificial intelligence.
Evaluation of ECG: Comparison of Decision Tree and Fuzzy Rules Induction

This paper compares two different approaches to computer-aided analysis of ECG signals. ECG records are preprocessed by the wavelet transform, and the machine learning method of decision trees and fuzzy rules induction are used for classification. The wavelet transform allows good localisation of QRS complexes, P and T waves in time and amplitude. The average accuracy of detection of all events is above 87 per cent. For learning and further classification we use Quinlan's See5 application and FURL (FUzzy Rule Learner). We used the MIT-BIH database for experiments. Diverse settings of the parameters for decision tree generation (tree pruning, attribute selection, class sets) were examined. Two datasets and diverse settings of fuzzysets were examined as well.
Disruption of amylase genes by RNA interference affects reproduction in the Pacific oyster Crassostrea gigas

ABSTRACT Feeding strategies and digestive capacities can have important implications for variation in energetic pathways associated with ecological and economically important traits, such as growth or reproduction in bivalve species. Here, we investigated the role of amylase in the digestive processes of Crassostrea gigas, using in vivo RNA interference. This approach also allowed us to investigate the relationship between energy intake by feeding and gametogenesis in oysters. Double-stranded (ds)RNA designed to target the two α-amylase genes A and B was injected in vivo into the visceral mass of oysters at two doses. These treatments caused significant reductions in mean mRNA levels of the amylase genes: −50.7% and −59% mRNA A, and −71.9% and −70.6% mRNA B in 15 and 75 µg dsRNA-injected oysters, respectively, relative to controls. Interestingly, reproductive knock-down phenotypes were observed for both sexes at 48 days post-injection, with a significant reduction of the gonad area (−22.5% relative to controls) and germ cell under-proliferation revealed by histology. In response to the higher dose of dsRNA, we also observed reductions in amylase activity (−53%) and absorption efficiency (−5%). Based on these data, dynamic energy budget modeling showed that the limitation of energy intake by feeding that was induced by injection of amylase dsRNA was insufficient to affect gonadic development at the level observed in the present study. This finding suggests that other driving mechanisms, such as endogenous hormonal modulation, might significantly change energy allocation to reproduction, and increase the maintenance rate in oysters in response to dsRNA injection. Summary: Disruption of amylase gene expression reduces absorption efficiency and constrains reproduction in oysters; the reduction in energy intake is insufficient to affect reproduction, suggesting that dsRNA causes disturbances that increase maintenance costs.
Implementation of microcontroller based Maximum Power Point Tracker (MPPT) using SEPIC converter

This paper presents analysis and design of a Maximum Power Point Tracker (MPPT) which is economical and compact in size. The DC to DC converter used in the system is SEPIC converter which eliminates the use of separate ground isolation circuit and reduces tracking time. The efficiency of this converter is better than traditionally used Buck-Boost converter. Here Incremental Conductance method is used to get a stable output in varying solar irradiance level. The proposed system also includes elimination of PI controller for duty cycle modification, elimination of complex circuit for sensing current and elimination of filters for accurate analog to digital conversion of voltage and currents. This system is tested and verified experimentally with 135Wp solar panel in varying insolation levels.
Embedded system controlling microwave generators in hyperthermia and diathermy medical devices

Microwave Hyperthermia (MH) is a medical procedure of rising the human body tissue temperature between 41.5C and 45C using electromagnetic radiation and maintaining the desired temperature during the treatment period. In this paper we present a part of our embedded system that can drive a continuous wave (CW) 2450 MHz magnetron as well as a 915 MHz RF power generator for pulse-modulated waves operating mode, measuring and managing the patient's skin temperature feedback. This embedded system is intended for use in microwave hyper-thermia or microwave diathermy devices, with non-invasive regional or interstitial applicators. Our paper is structured in four chapters. The “Introduction” is briefly presenting the microwave hyperthermia essentials: microwave propagation, penetration depth, SAR and microwave heating mechanism. The “Design configuration” part presents the structure of our hyperthermia device with basic microwave generators and auxiliary equipment used for thermal management. The “Embedded design” chapter presents, in two subsections entitled “Hardware” and “Software”, the principles used for controlling the microwave generators together with the implemented solution and the essential aspects of our firmware as program flow. In “Conclusions”, preliminary results are analyzed in agreement with the IEC60601 standards and with the requested directions for further developments.
A Novel Transcript Mapping Technique for Handwritten Document Images

Transcript mapping refers to the process of aligning meaningful units of a handwritten document image (e.g. Text lines, words, characters) with the corresponding transcription information. It has many applications such as (i) fast generation of ground truth at different granularity levels and (ii) indexing handwritten collections for document retrieval. In this paper, a novel transcript mapping technique is proposed which is guided by the number of words as well as the characters per word of a text line. The proposed method combines the results of a local and a global approach using a scoring algorithm. The efficiency of the proposed method is demonstrated by experimentation conducted on a known, publicly available dataset, achieving word level alignment accuracy of 99.48%.