Miracle-dz/UniFAR-FTUs huggingface dataset:
RESEARCH OUTPUTS / RÉSULTATS DE RECHERCHE

Mutual information is a widely used performance criterion for ﬁlter feature selection. However, despite its popularity and its appealing properties, mutual information is not always the most appropriate criterion. Indeed, contrarily to what is sometimes hypothesized in the literature, looking for a feature subset maximizing the mutual information does not always guarantee to decrease the misclassiﬁcation probability, which is often the objective one is interested in. The ﬁrst objective of this paper is thus to clearly illustrate this potential inadequacy and to emphasize the fact that the mutual information remains a heuristic, coming with no guarantee in terms of classiﬁcation accuracy. Through extensive experiments, a deeper analysis of the cases for which the mutual information is not a suitable criterion is then conducted. This analysis allows us to conﬁrm the general interest of the mutual information for feature selection. It also helps us better apprehending the behaviour of mutual information throughout a feature selection process and consequently making a better use of it as a feature selection criterion.
Seasonal Variation and Preliminary Risk Assessment of Trace Element Pollution in Surface Water from Langat River, Malaysia

Heavy metals are widely used in various industries and became a great concern all over the world due to environmental contamination. This study provides an assessment of seasonal variability and risks to human health associated with the exposure to heavy metals concentrated in Langat river water of Malaysia. The Department of Environment (DOE) Malaysia kindly provided the heavy metal concentration data in water for this study. Several multivariate estimation such as an independent t test, box-and-whisker plot and Principal component analysis were carried out to evaluate the seasonal variation of metals concentration in water. The average value of ten analyzed metals was 250.81 µg/l and followed in order of abundance by August > Jun > February > October > April > December > March > May > September > January > July > November. The calculated HPI was 123.42, which is far above the critical index value of 100, indicating pollution with respect to heavy metals. Estimates of health risks associated with river water were summarized according to non-carcinogenic and carcinogenic health effects. No potential threat was detected for noncarcinogenic risk as the HI values calculated were <1. Potential carcinogenic risks associated with the ingestion and dermal absorption of heavy metals in water were evaluated probabilistically by performing 10,000 trails for Monte Carlo simulation where potential carcinogenic risk exists in case of Cd and As.It is necessary to take proper steps to reduce the pollution of heavy metals in Langat River.
Geophysical and petrological constraints on ocean plate dynamics

This thesis investigates the formation and subsequent motion of oceanic lithospheric plates through geophysical and petrological methods. Ocean crust and lithosphere forms at mid-ocean ridges as the underlying asthenosphere rises, melts, and flows away from the ridge axis. In Chapters 2 and 3, I present the results from partial melting experiments of mantle peridotite that were conducted in order to examine the mantle melting point, or solidus, beneath a mid-ocean ridge. Chapter 2 determines the peridotite solidus at a single pressure of 1.5 GPa and concludes that the oceanic mantle potential temperature must be ~60 oC hotter than current estimates. Chapter 3 goes further to provide a more accurate parameterization of the anhydrous mantle solidus from experiments over a range of pressures. This chapter concludes that the range of potential temperatures of the mantle beneath mid-ocean ridges and plumes is smaller than currently estimated. Once formed, the oceanic plate moves atop the underlying asthenosphere away from the ridge axis. Chapter 4 uses seafloor magnetotelluric data to investigate the mechanism responsible for plate motion at the lithosphere-asthenosphere boundary. The resulting two dimensional conductivity model shows a simple layered structure. By applying petrological constraints, I conclude that the upper asthenosphere does not contain substantial melt, which suggests that either a thermal or hydration mechanism supports plate motion. Oceanic plate motion has dramatically changed the surface of the Earth over time, and evidence for ancient plate motion is obvious from detailed studies of the longer lived continental lithosphere. In Chapter 5, I investigate past plate motion by inverting magnetotelluric data collected over eastern Zambia. The conductivity model probes the Zambian lithosphere and reveals an ancient subduction zone previously suspected from surface studies. This chapter elucidates the complex lithospheric structure of eastern Zambia and the geometry of the tectonic elements in the region, which collided as a result of past oceanic plate motion. Combined, the chapters of this thesis provide critical constraints on ocean plate dynamics. Thesis Supervisors: Rob L. Evans Title: Senior Scientist, Department of Geology and Geophysics, WHOI Glenn A. Gaetani Title: Associate Scientist, Department of Geology and Geophysics, WHOI
Title: Online Learning Mechanisms for Bayesian Models of Word Segmentation

In recent years, Bayesian models have become increasingly popular as a way of understanding human cognition. Ideal learner Bayesian models assume that cognition can be usefully understood as optimal behavior under uncertainty, a hypothesis that has been supported by a number of modeling studies across various domains (e.g., Griffiths & Tenenbaum, 2005; Xu & Tenenbaum, 2007). The models in these studies aim to explain why humans behave as they do given the task and data they encounter, but typically avoid some questions addressed by more traditional psychological models, such as how the observed behavior is produced given constraints on memory and processing. Here, we use the task of word segmentation as a case study for investigating these questions within a Bayesian framework. We consider some limitations of the infant learner, and develop several online learning algorithms that take these limitations into account. Each algorithm can be viewed as a different method of approximating the same ideal learner. When tested on corpora of English child-directed speech, we find that the constrained learner’s behavior depends non-trivially on how the learner's limitations are implemented. Interestingly, sometimes biases that are helpful to an ideal learner hinder a constrained learner, and in a few cases, constrained learners perform equivalently or better than the ideal learner. This suggests that the transition from a computational-level solution for acquisition to an algorithmic-level one is not straightforward.
Spatial variability in response of deltaic baldcypress forests to hydrology and climate

x INTRODUCTION . 1 Objectives . 4 General Dendrochronological Methods . 5 METHODS AND MATERIALS . 7 Tree-ring Data Source . 7 Tree-ring Width Modeling . 8 Regional Curve Standardization for Estimating Biolo gical Growth Trend . 9 Parameter Estimation for RCS Curve Fitting and Detr ending of Tree-ring Data . 10 Climate Data . 11 River Stage Data . 11 Coastal Water Level Data. 12 Grouping Climatic and Hydrologic Variables. 16 Correlation Analysis and Spatial Assessment of Patt ern of Linear Relationships . 17 Redundancy Discriminant Analysis (RDA) . 18 RESULTS AND DISCUSSION . 20 Modeling Age Related Growth Trend . 20 Temporal Variation in Tree-ring Growth . 21 Redundancy Discriminant Analysis Result . 24 River Stage and Tree Radial Growth Relationship . 31 Coastal Water Level and Tree Radial Growth Relation ship . 34 Temperature and Tree Radial Growth Relationship. 37 Precipitation and Tree Radial Growth Relationship. 41 PDSI and Tree Radial Growth Relationship . 44 PHDI and Tree Radial Growth Relationship . 45 River Stage and Coastal Water Level Relationship . 47 General Discussion of Results from This Study in Re lation to Other Comparable Studies . 48 CONCLUSIONS. 50 LITERATURE CITED . 51 APPENDIX A: SITES, CODES, FIELD SAMPLER AND LAB PRO CESSOR OF THE DATA SOURCES USED IN THIS STUDY . 57
Evaluating some Feature Selection Methods for an Improved SVM Classifier

documents into a set of predefined classes. After a preprocessing step the documents are typically represented as large sparse vectors. When training classifiers on large collections of documents, both the time and memory restrictions can be quite prohibitive. This justifies the application of features selection methods to reduce the dimensionality of the document-representation vector. Four feature selection methods are evaluated: Random Selection, Information Gain (IG), Support Vector Machine (called SVM_FS) and Genetic Algorithm with SVM (GA_FS). We showed that the best results were obtained with SVM_FS and GA_FS methods for a relatively small dimension of the features vector comparative with the IG method that involves longer vectors, for quite similar classification accuracies. Also we present a novel method to better correlate SVM kernel's parameters (Polynomial or Gaussian kernel).
AVEC 2017 – Real-life Depression , and A ect Recognition Workshop and Challenge

e Audio/Visual Emotion Challenge and Workshop (AVEC 2017) “Real-life depression, and aect” will be the seventh competition event aimed at comparison of multimedia processing and machine learning methods for automatic audiovisual depression and emotion analysis, with all participants competing under strictly the same conditions. e goal of the Challenge is to provide a common benchmark test set for multimodal information processing and to bring together the depression and emotion recognition communities, as well as the audiovisual processing communities, to compare the relative merits of the various approaches to depression and emotion recognition from real-life data. is paper presents the novelties introduced this year, the challenge guidelines, the data used, and the performance of the baseline system on the two proposed tasks: dimensional emotion recognition (time and value-continuous), and dimensional depression estimation (value-continuous). ∗e author is further aliated with the Department of Computing, Imperial College London, London, UK and the Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Germany. †e author is further aliated with Twente University, EEMCS, Twente, e Netherlands. ACM acknowledges that this contribution was authored or co-authored by an employee, or contractor of the national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only. Permission to make digital or hard copies for personal or classroom use is granted. Copies must 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. To copy otherwise, distribute, republish, or post, requires prior specic permission and/or a fee. Request permissions from permissions@acm.org. AVEC’17, Mountain View, CA, USA © 2017 ACM. 978-1-4503-5502-5/17/10. $15.00 DOI: 10.1145/3133944.3133953 CCS CONCEPTS •General and reference → Performance; •Computing methodologies→ Biometrics;
, 39.9 us

—This paper presents a deep learning-based classiﬁer IC for keyword spotting (KWS) in 65nm CMOS designed using an algorithm-hardware co-design approach. First, a Recurrent Attention Model (RAM) algorithm for the KWS task (the KeyRAM algorithm) is proposed. The KeyRAM algorithm enables accuracy vs. energy scalability via a conﬁdence-based computation (CC) scheme, leads to a 2 . 5 × reduction in computational complexity compared to state-of-the-art (SOTA) neural networks, and is well-suited for in-memory computing (IMC) since the bulk ( 89% ) of its computations are 4b matrix-vector multiplies. The KeyRAM IC comprises a multi-bit multi-bank in-memory computing (IMC) architecture with a digital co-processor. A sparsity-aware summation scheme is proposed to alleviate the challenge faced by IMCs when summing sparse activations. The digital co-processor employs diagonal major weight storage to compute without any stalls. This combination of the IMC and digital processors enables a balanced trade-off between energy efﬁciency and high accuracy computation. The resultant KWS IC achieves SOTA decision latency of 39 . 9 µ s with a decision energy < 0 . 5 µ J / dec which translates to more than 24 × savings in the energy-delay product (EDP) of decisions over existing KWS ICs.
Admixture with cultivated sunflower likely facilitated establishment and spread of wild sunflower (H. annuus) in Argentina

A better understanding of the genetic and ecological factors underlying successful invasions is critical to mitigate the negative impacts of invasive species. Here, we study the invasion history of Helianthus annuus populations from Argentina, with particular emphasis on the role of post-introduction admixture with cultivated sunflower (also H. annuus) and climate adaptation driven by large haploblocks. We conducted genotyping-by-sequencing of samples of wild populations as well as Argentinian cultivars and compared them with wild (including related annual Helianthus species) and cultivated samples from the native range. We also characterized samples for 11 known haploblocks associated with environmental variation in native populations to test whether haploblocks contributed to invasion success. Population genomics analyses supported two independent geographic sources for Argentinian populations, the central United States and Texas, but no significant contribution of related annual Helianthus species. We found pervasive admixture with cultivated sunflower, likely as result of post-introduction hybridization. Genomic scans between invasive populations and their native sources identified multiple genomic regions with evidence of selection in the invaded range. These regions significantly overlapped between the two native-invasive comparisons and showed disproportionally high crop ancestry, suggesting that crop alleles contributed to invasion success. We did not find evidence of climate adaptation mediated by haploblocks, yet outliers of genome scans were enriched in haploblock regions and, for at least two haploblocks, the cultivar haplotype was favored in the invaded range. Our results show that admixture with cultivated sunflower played a major role in the establishment and spread of H. annuus populations in Argentina.
Responses of neurons in macaque V4 to object and texture images

Humans and monkeys can effortlessly recognize objects in everyday scenes. This ability relies on neural computations in the ventral stream of visual cortex. The intermediate computations that lead to object selectivity are not well understood, but previous studies implicate V4 as an early site of selectivity for object shape. To explore the mechanisms of this selectivity, we generated a continuum of images between “scrambled” textures and photographic images of both natural and manmade environments, using techniques that preserve the local statistics of the original image while discarding information about scene and shape. We measured the responses of single units in awake macaque V4 to these images. On average, V4 neurons were slightly more active in response to photographic images than to their scrambled counterparts. However, responses in V4 varied widely both across different cells and different sets of images. An important determinant of this variation was the effectiveness of image families at driving strong neural responses. Across the full V4 population, a cell’s average evoked firing rate for a family reliably predicted that family’s preference for photographic over scrambled images. Accordingly, the cells that respond most strongly to each image family showed a much stronger difference between photographic and scrambled images and a graded level of modulation for images scrambled at intermediate levels. This preference for photographic images was not evident until ∼50 ms after the onset of neuronal activity and did not peak in strength until 140 ms after activity onset. Finally, V4 neural responses seemed to categorically separate photographic images from all of their scrambled counterparts, despite the fact that the least scrambled images in our set appear similar to the originals. When these same images were analyzed with DISTS (Deep Image Structure and Texture Similarity), an image-computable similarity metric that predicts human judgements of image degradation, this same pattern emerged. This suggests that V4 responses are highly sensitive to small deviations from photographic image structure.
Contracted Functional Connectivity Profiles in Autism

Objective Autism spectrum disorder (ASD) is a pervasive neurodevelopmental condition that is associated with atypical brain network organization, with prior work suggesting differential connectivity alterations with respect to functional connection length. Here, we tested whether functional connectopathy in ASD specifically relates to disruptions in long-relative to short-range functional connectivity profiles. Our approach combined functional connectomics with geodesic distance mapping, and we studied associations to macroscale networks, microarchitectural patterns, as well as socio-demographic and clinical phenotypes. Methods We studied 211 males from three sites of the ABIDE-I dataset comprising 103 participants with an ASD diagnosis (mean±SD age=20.8±8.1 years) and 108 neurotypical controls (NT, 19.2±7.2 years). For each participant, we computed cortex-wide connectivity distance (CD) measures by combining geodesic distance mapping with resting-state functional connectivity profiling. We compared CD between ASD and NT participants using surface-based linear models, and studied associations with age, symptom severity, and intelligence scores. We contextualized CD alterations relative to canonical networks and explored spatial associations with functional and microstructural cortical gradients as well as cytoarchitectonic cortical types. Results Compared to NT, ASD participants presented with widespread reductions in CD, generally indicating shorter average connection length and thus suggesting reduced long-range connectivity but increased short-range connections. Peak reductions were localized in transmodal systems (i.e., heteromodal and paralimbic regions in the prefrontal, temporal, and parietal and temporo-parieto-occipital cortex), and effect sizes correlated with the sensory-transmodal gradient of brain function. ASD-related CD reductions appeared consistent across inter-individual differences in age and symptom severity, and we observed a positive correlation of CD to IQ scores. Conclusions Our study showed reductions in CD as a relatively stable imaging phenotype of ASD that preferentially impacted paralimbic and heteromodal association systems. CD reductions in ASD corroborate previous reports of ASD-related imbalance between short-range overconnectivity and long-range underconnectivity.
Mutational Analysis of an Antimalarial Drug Target PfATP4

Among new antimalarials discovered over the past decade are multiple chemical scaffolds that target Plasmodium falciparum P-type ATPase (PfATP4). This essential protein is a Na+ pump responsible for the maintenance of Na+ homeostasis. PfATP4 belongs to the type 2D subfamily of P-type ATPases, for which no structures have been determined. To gain better insight into the structure/function relationship of this validated drug target, we generated a homology model of PfATP4 based on SERCA, a P2A-type ATPase, and refined the model using molecular dynamics in its explicit membrane environment. This model predicted several residues in PfATP4 critical for its function, as well as those that impart resistance to various PfATP4 inhibitors. To validate our model, we developed a genetic system involving merodiploid states of PfATP4 in which the endogenous gene was conditionally expressed, and the second allele was mutated to assess its effect on the parasite. Our model predicted residues involved in Na+ coordination as well as the phosphorylation cycle of PfATP4. Phenotypic characterization of these mutants involved assessment of parasite growth, localization of mutated PfATP4, response to treatment with known PfATP4 inhibitors, and evaluation of the downstream consequences of Na+ influx. Our results were consistent with modeled predictions of the essentiality of the critical residues. Additionally, our approach confirmed the phenotypic consequences of resistance-associated mutations as well as a potential structural basis for the fitness cost associated with some mutations. Taken together, our approach provides a means to explore the structure/function relationship of essential genes in haploid organisms. Significance Statement Plasmodium falciparum ATP4 (PfATP4) is a Na+ efflux pump and represents an important target for antimalarial drugs with nanomolar potency. However, the structure of PfATP4 is unknown, prompting the development of new methodologies to investigate the structure/function relationship. Here, we introduce a dynamic homology modeling approach to (a) identify key residues essential for PfATP4 function and (b) provide a structural basis to understand resistance-associated mutations. To validate these predictions, we developed a genetic system to manipulate the PfATP4 gene to assess the phenotypic consequences of such changes. Our results support the utility of combining homology modeling and genetics to gain functional insights into an antimalarial drug target.
Evaluating the patterns of spatiotemporal trends of root zone soil moisture in major climate regions in East Asia

Root zone soil moisture (RZSM) is a crucial variable in land‐atmosphere interactions. Evaluating the spatiotemporal trends and variability patterns of RZSM are essential for discerning the anthropogenic and climate change effects on the regional and global hydrological cycles. In this study, the trends of RZSM, computed by the exponential filter from the European Space Agency's Climate Change Initiative soil moisture, were evaluated in major climate regions of East Asia from 1982 to 2014. Moreover, the trends of RZSM were compared to the trends of precipitation (P), skin temperature (Tskin), and actual evapotranspiration (AET) to investigate how they influence the RZSM trends in each climate region. Drying trends were predominant in arid and continental regions, whereas wetting trends were found in the tropical and temperate regions. The increasing trends of Tskin and AET cause drying in arid and continental regions, whereas in tropical regions, these cause wetting trends, which might be due to convective P. In temperate regions, despite decreasing P and increasing Tskin, the RZSM trend was increasing, attributed to the intensive irrigation activities in these regions. This is probably the first time to analyze the long‐term trends of RZSM in different climate regions. Hence, the results of this study will improve our understanding of the regional and global hydrological cycles. Despite certain limitations, the results of this study may be useful for improving and developing climate models and predicting long‐term vast scale natural disasters such as drought, dust outbreaks, floods, and heat waves.