Miracle-dz/UniFAR-FTUs huggingface dataset:
Finding and Leveraging Structure in Learning Problems

The problem of learning from noisy and high dimensional data is an important challenge that has received much attention in the modern machine learning and statistics literature. These problems arise in numerous applications: large scale collaborative filtering, learning gene regulatory networks and genome wide association studies to name a few. This thesis focuses on understanding the statistical and computational aspects of finding and leveraging structure in complex high-dimensional datasets. The main goal of this thesis is to show that exploiting the inherent structure of real datasets allows us to achieve statistical consistency in a variety of extremely noisy and high-dimensional problems. We begin with an empirical study of structure learning in discrete Markov Random Fields (MRFs) applied to learning statistical models from multiple sequence alignments (MSAs). Our main results show that despite the high dimensionality of the parameter space we are able to learn accurate and interpretable models by exploiting sparsity. However, sparsity is not the only notion of structure we can exploit. We then study the performance of spectral clustering on extremely noisy similarity matrices. Our main results establish the minimax rates for the clustering of certain hierarchically and flat (k-way) structured similarity matrices, and also establish rates for spectral clustering on these matrices. We continue with a study of bi-clustering, where the goal is to cluster both features and data points. We model this problem as a structured normal means problem and use minimax theory to characterize the tradeoffs between signal strength and computation. In both of these cases we show that exploiting our knowledge of the existence of a cluster structure allows us to reliably cluster much noisier problem instances. We then shift focus to the problem of learning the homology groups of manifolds from noisy samples. Once again our main results establish tight minimax rates under a variety of noise models. Here we show that by exploiting the (low-dimensional) manifold structure it is possible to learn its homology from relatively fewer samples under a variety of noise models. We finally turn our attention to the problem of high dimensional non-parametric canonical correlations analysis (CCA). We present two approaches to this problem, and establish theoretical properties of our estimators in the high dimensional setting. We conclude each chapter with a description of the proposed work and future research directions.
The Genetic Signature of Conditional Expression

Conditionally expressed genes have the property that every individual in a population carries and transmits the gene, but only a fraction, ϕ, expresses the gene and exposes it to natural selection. We show that a consequence of this pattern of inheritance and expression is a weakening of the strength of natural selection, allowing deleterious mutations to accumulate within and between species and inhibiting the spread of beneficial mutations. We extend previous theory to show that conditional expression in space and time have approximately equivalent effects on relaxing the strength of selection and that the effect holds in a spatially heterogeneous environment even with low migration rates among patches. We support our analytical approximations with computer simulations and delineate the parameter range under which the approximations fail. We model the effects of conditional expression on sequence polymorphism at mutation–selection–drift equilibrium, allowing for neutral sites, and show that sequence variation within and between species is inflated by conditional expression, with the effect being strongest in populations with large effective size. As ϕ decreases, more sites are recruited into neutrality, leading to pseudogenization and increased drift load. Mutation accumulation diminishes the degree of adaptation of conditionally expressed genes to rare environments, and the mutational cost of phenotypic plasticity, which we quantify as the plasticity load, is greater for more rarely expressed genes. Our theory connects gene-level relative polymorphism and divergence with the spatial and temporal frequency of environments inducing gene expression. Our theory suggests that null hypotheses for levels of standing genetic variation and sequence divergence must be corrected to account for the frequency of expression of the genes under study.
Preparation behaviour synthesis with reinforcement learning

When humans perform a series of motions, they prepare for the next motion in advance so as to enhance the response time of their movements. This kind of preparation behaviour results in a natural and smooth transition of the overall movement. In this paper, we propose a new method to synthesize the behaviour using reinforcement learning. To create preparation movements, we propose a customized motion blending algorithm that is governed by a single numerical value, which we called the level of preparation. During the offline process, the system learns the optimal way to approach a target, as well as the realistic behaviour to prepare for interaction considering the level of preparation. At run-time, the trained controller indicates the character to move to a target with the appropriate level of preparation, resulting in human-like movements. We synthesized scenes in which the character has to move in a complex environment and interact with objects, such as a character crawling under and jumping over obstacles while walking. The method is useful not only for computer animation, but also for real-time applications such as computer games, in which the characters need to accomplish a series of tasks in a given environment.
Expansion of Human Mesenchymal Stromal Cells from Fresh Bone Marrow in a 3D Scaffold-Based System under Direct Perfusion

Mesenchymal stromal/stem cell (MSC) expansion in conventional monolayer culture on plastic dishes (2D) leads to progressive loss of functionality and thus challenges fundamental studies on the physiology of skeletal progenitors, as well as translational applications for cellular therapy and molecular medicine. Here we demonstrate that 2D MSC expansion can be entirely bypassed by culturing freshly isolated bone marrow nucleated cells within 3D porous scaffolds in a perfusion-based bioreactor system. The 3D-perfusion system generated a stromal tissue that could be enzymatically treated to yield CD45- MSC. As compared to 2D-expanded MSC (control), those derived from 3D-perfusion culture after the same time (3 weeks) or a similar extent of proliferation (7–8 doublings) better maintained their progenitor properties, as assessed by a 4.3-fold higher clonogenicity and the superior differentiation capacity towards all typical mesenchymal lineages. Transcriptomic analysis of MSC from 5 donors validated the robustness of the process and indicated a reduced inter-donor variability and a significant upregulation of multipotency-related gene clusters following 3D-perfusion- as compared to 2D-expansion. Interestingly, the differences in functionality and transcriptomics between MSC expanded in 2D or under 3D-perfusion were only partially captured by cytofluorimetric analysis using conventional surface markers. The described system offers a multidisciplinary approach to study how factors of a 3D engineered niche regulate MSC function and, by streamlining conventional labor-intensive processes, is prone to automation and scalability within closed bioreactor systems.
Dementia Rating Scale psychometric study and its applicability in long term care institutions in Brazil.

OBJECTIVE To evaluate the diagnostic sensitivity, specificity, and agreement of the Dementia Rating Scale with clinical diagnosis of cognitive impairment and to compare its psychometric measures with those from Mini Mental State Examination. METHODS Eighty-six elders from a long-term care institution were invited to participate in a study, and fifty-eight agreed to participate. The global health assessment protocol applied to these elders contained Mini Mental State Examination and Dementia Rating Scale. Clinical diagnose of cognitive impairment was performed by experts using the DSM-IV criteria. Dementia Rating Scale score was correlated to the Mini Mental State Examination. Sensitivity and specificity of the Dementia Rating Scale and the Mini Mental State Examination were calculated. RESULTS Twenty-two (37.9%) elders had cognitive impairment (8.6% presented with mental cognitive impairment and 29.3% dementia). The Mini Mental State Examination and the Dementia Rating Scale classified 31% and 55.2% as presenting with cognitively impaired. Dementia Rating Scale and the Mini Mental State Examination scores presented a strong correlation. Dementia Rating Scale had a higher sensitivity than Mini Mental State Examination (86.4% versus 61.9%). Dementia Rating Scale presented lower diagnostic specificity than Mini Mental State Examination (63.9% versus 86.5%). Considering Dementia Rating Scale and Mini Mental State Examination, the diagnostic agreement between them and the clinical diagnosis was similar. CONCLUSIONS Dementia Rating Scale showed a high sensitivity to detect cognitive impairment in this population, and it could be a useful tool in these settings.
Annotating Underquantification

Many noun phrases in text are ambiguously quantified: syntax doesn't explicitly tell us whether they refer to a single entity or to several, and what portion of the set denoted by the Nbar actually takes part in the event expressed by the verb. We describe this ambiguity phenomenon in terms of underspecification, or rather underquantification. We attempt to validate the underquantification hypothesis by producing and testing an annotation scheme for quantification resolution, the aim of which is to associate a single quantifier with each noun phrase in our corpus.
Feature Vector Compression Based on Least Error Quantization

We propose a distinctive feature vector compression method based on least error quantization. This method can be applied to several biometrics methods using feature vectors, and allows us to significantly reduce the memory size of feature vectors without degrading the recognition performance. In this paper, we prove that minimizing quantization error between the compressed and original vectors is most effective to control the performance in face recognition. A conventional method uses non-uniform quantizer which minimizes the quantization error in terms of L2-distance. However, face recognition methods often use metrics other than L2-distance. Our method can calculate the quantized vectors in arbitrary metrics such as Lp-distance (0
Simultaneous inhibition of deubiquitinating enzymes (DUBs) and autophagy synergistically kills breast cancer cells

Breast cancer is one of the leading causes of cancer death among women in the United States. Patients expressing the estrogen and progesterone receptor (ER and PR) and human epidermal growth factor 2 (HER-2) tumor markers have favorable prognosis and efficacious therapeutic options. In contrast, tumors that are negative for these markers (triple-negative) have a disproportionate share of morbidity and mortality due to lack of a validated molecular target. Deubiquitinating enzymes (DUBs) are a critical component of ubiquitin-proteasome-system degradation and have been shown to be differentially expressed and activated in a number of cancers, including breast, with their aberrant activity linked to cancer prognosis and clinical outcome. We evaluated the effect of the DUB inhibitors b-AP15 and RA-9 alone and in combination with early- and late-stage lysosomal inhibitors on cell viability in a panel of triple negative breast cancer (TNBC) cell lines. Our results indicate small-molecule DUB inhibitors have a profound effect on TNBC viability and lead to activation of autophagy as a cellular mechanism to compensate for ubiquitin-proteasome-system stress. Treatment with sub-optimal doses of DUB and lysosome inhibitors synergistically kills TNBC cells. This supports the evaluation of DUB inhibition, in combination with lysosomal inhibition, as a therapeutic approach for the treatment of TNBC.
Revealing Degas’s process and material choices in a late pastel on tracing paper with visible-to-near-infrared reflectance imaging spectroscopy

ABSTRACT Visible-to-near-infrared reflectance imaging spectroscopy (417–2500 nm), the collection of hundreds of images in contiguous narrow spectral bands calibrated to reflectance, was employed to characterize drawing materials in Ballet Scene, a large-scale pastel on tracing paper by Edgar Degas. The spectral range from 417 to 950 nm proved useful in identifying and mapping the complex pigment application. The near-infrared (NIR) range from 962 to 2500 nm allowed visualization of underdrawing below multiple layers of pastel and fixative that depict positions of the dancers’ bodies and costumes in the pastel’s early developmental stages. This study compares the visualization of underdrawing in images gathered from reflectance imaging spectroscopy in narrow spectral bands with those obtained from infrared reflectography (IRR) in broad spectral bands, a technique widely used in the art conservation field. Passages in which Degas explores various limb positions are better visualized with image products derived from reflectance imaging spectroscopy than broadband IRR images. For example, false-color reflectance images that display narrow NIR spectral bands were useful to separate and visualize the distribution of materials and more clearly convey Degas’s processes of compositional refinement. These results provide a deeper understanding of Degas’s working method and explore the relationship of Ballet Scene with similar works.
Bounded-Error Interactive Ray Tracing

Ray tracing, which computes radiance, is traditionally regarded as an off-line rendering algorithm that is too slow for interactive use. In this paper, we present an interactive system that uses 4D interpolants to approximate radiance, while providing guaranteed error bounds. Our system exploits the object-space, ray-space, image-space and temporal coherence in radiance to accelerate ray tracing. Our system explicitly decouples the two operations of the ray tracer — shading computation and visibility determination at each pixel, which we call pixel assignment. Rendering is accelerated by approximating the shading computation while guaranteeing correct pixel assignment. Without any pre-processing, the system lazily collects 4D radiance samples, which are quadrilinearly interpolated to approximate radiance. An error predicate conservatively guarantees that the relative error introduced by interpolation is bound by a user-specified . The user can change this parameter to trade off performance for quality. The predicate guarantees the error bound by detecting discontinuities and non-linearities in radiance. If error cannot be guaranteed for an interpolant, the system adaptively refines the interpolant. Pixel assignment is accelerated using a novel algorithm that reprojects 4D interpolants to new viewpoints as the user’s viewpoint changes. Using reprojection, a fast scan-line algorithm achieves high performance without sacrificing image quality. This lazy interpolant system with reprojection speeds up ray tracers substantially for smoothly varying viewpoints. In this paper, we present the design, implementation and results for our system. We expect our techniques to be useful for interactively rendering general scenes as well as for batch off-line rendering processes.
SAR image segmentation based on spatially adaptive weighted possibilistic c-means clustering

Due to the influence of speckle in synthetic aperture radar (SAR) image, statistical dependencies among neighboring pixels should be considered in SAR image segmentation. The spatially adaptive weighted possibilistic c-means (SAW-PCM) clustering algorithm is proposed in which spatial information is introduced into PCM approach to directly adjust the membership. The relationship between the neighboring pixels is described through Markov random fields (MRF). To preserve detail information in SAR images, the directional neighborhood system set is established. The selection of neighborhood systems is based on similarity measurement (SM) between wavelet energies of comprehensive result of steerable wavelet transform. Among the different neighborhood alternatives, the one with the highest SM value is chosen to compute the weight value. The experimental results on real SAR images demonstrate the merit of the proposed method, especially in the preservation of details within a SAR image.
RF-MEMS Enabled RF-Signal Source For Low-Power Consumption Ultrawideband Communication Systems

RF-MEMS based components such as inductors, variable capacitors, transmission line, filter, switches, phase-shifter, and resonators are superior in performances in term of quality factor, noise, linearity, power consumption, size, and cost, which cannot be achieved by conventional approach, thereby, prime candidate for ubiquitous connectivity. This work discusses the recent trends in wireless connectivity and gives the brief overview of the RF-MEMS enabled front ends, especially, RF signal source (VCOs) that can be extended to microwave, and is also amenable for integrated circuit (IC) fabrication
Combining models for business decisions and software development

Today there is a number of established software development lifecycle models (SDLM) supporting software development. Correct implementation of these models helps develop software products the right way, but this does not ensure that the right products are developed. Successful product development companies often use business decision models (BDM) to facilitate the selection of products and projects for investment, but these models do not necessarily facilitate actual development of the software. One of the current challenges in the software community is to combine BDM and SDLM, including mapping of business decision gates and major lifecycle milestones. This is needed to achieve synergies between the two model types and to support the development the right products the right way, as well as to gain control over company investments. This paper analyzes two RDM, proposes mappings to an established SDLM, and describes experiences of using them in a large, multinational engineering company.
Efficient Visualization of Content and Contextual Information of an Online Multimedia Digital Library for Effective Browsing

In this paper, we present a few innovative techniques for visualization of content and contextual information of a multimedia digital library for effective browsing. A traditional collection visualization portal often depicts some metadata or a short synopsis, which is quite inadequate for assessing the documents. We have designed a novel web portal that incorporates a few preview facilities to disclose an abstract of the contents. Moreover, we place the documents on Google Maps to make its geographical context explicit. A semantic network, created automatically around the collection, brings out other contextual information from external knowledge resources like Wikipedia which is used for navigating collection. This paper also reports economical hosting techniques using Amazon Cloud.
Nicotine Withdrawal, Relapse of Mental Illness, or Medication Side-Effect? Implementing a Monitoring Tool for People With Mental Illness Into Quitline Counseling

ABSTRACT Background: Smokers with mental illness and their health care providers are often concerned that smoking cessation will worsen mental health. Smokers with mental illness tend to be more nicotine-dependent and experience more severe symptoms of nicotine withdrawal, some of which are difficult to distinguish from psychiatric symptoms. In addition, smoking cessation can increase the blood levels and hence side effects of some psychotropic medications. Improved monitoring of nicotine withdrawal and medication side effects may help distinguish temporary withdrawal symptoms from psychiatric symptoms and facilitate targeted treatment to help smokers with mental illness manage the acute phase of nicotine withdrawal. Objective: The aim of this research was to examine the acceptability and feasibility to quitline counselors of implementing structured assessments of nicotine withdrawal and common medication side effects in people with mental illness who are quitting smoking using a telephone smoking cessation service. Methods: Monitoring involves administering (once pre-cessation and at each contact post-cessation) (1) the Minnesota Nicotine Withdrawal Scale, assessing eight symptoms: anger, anxiety, depression, cravings, difficulty concentrating, increased appetite, insomnia, and restlessness and (2) an adverse side effects checklist of 5 to 10 symptoms, for example, dry mouth and increased thirst. Following a 1-day update training in mental health, quitline counselors were asked to offer these assessments to callers disclosing mental illness in addition to usual counseling. Group interviews with counselors were conducted 2 months later to examine implementation barriers and benefits. Results: Barriers included awkwardness in integrating a new structured practice into counseling, difficulty in limiting some callers to only the content of new items, and initial anxieties about how to respond to changes in some symptoms. Benefits included the ability to provide objective feedback on changes in symptoms, as this identified early benefits of quitting, provided reassurance for clients, and provided an opportunity for early intervention where symptoms worsened. Conclusions: Structured monitoring of withdrawal symptoms and medication side effects was able to be integrated into the quitline's counseling and was valued by counselors and clients. Given evidence of its benefits in this limited pilot study, we recommend it be considered for larger-scale adoption by quitlines.
Stance Classification in Online Debates by Recognizing Users’ Intentions

Online debate forums provide a rich collection of differing opinions on various topics. In dual-sided debates, users present their opinions or judge other’s opinions to support their stance. In this paper, we examine the use of users’ intentions and debate structure for stance classification of the debate posts. We propose a domain independent approach to capture users’ intent at sentence level using its dependency parse and sentiWordNet and to build the intention structure of the post to identify its stance. To aid the task of classification, we define the health of the debate structure and show that maximizing its value leads to better stance classification accuracies.
Intravenously and iontophoretically administered naloxone reverses ischemic changes in rat hippocampus.

Forty rats under urethane anesthesia were subjected to cerebral ischemia by ligation of the right carotid, the right plus the left carotid, or the right carotid plus two vertebral arteries. Ischemia caused three types of changes in the field potential of the right hippocampal CA1 region evoked by fimbrial stimulation: 1) completely reversible deterioration (57% and 16% of the rats with unilateral and bilateral carotid artery ligation, respectively), 2) moderate deterioration (37% and 24% of the rats with unilateral and bilateral carotid artery ligation) and 3) irreversible loss of the evoked activity (6% and 60% of the rats with unilateral and bilateral carotid artery ligation and all the rats subjected to three-vessel occlusion). Naloxone improved the moderate deterioration in 10 of 11 rats (1-3 mg/kg i.v.) and in 15 of 16 (50-150 nA) iontophoretic applications, but naloxone did not restore the lost evoked activity. Intravenous morphine (10 mg/kg) aggravated the ischemic changes, and this effect was reversed by naloxone, while iontophoretic administration of morphine caused only excitation. These findings suggest that naloxone has a favorable effect on cerebral ischemia not severe enough to cause transmission failure. The reversal of ischemic changes by iontophoretic naloxone indicates that its site of action is at the neuronal or microcirculatory level.