Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient indexing for large scale visual search

With the popularity of “bag of visual terms” representations of images, many text indexing techniques have been applied in large-scale image retrieval systems. However, due to a fundamental difference between an image query (e.g. 1500 visual terms) and a text query (e.g. 3–5 terms), the usages of some text indexing techniques, e.g. inverted list, are misleading. In this work, we develop a novel indexing technique for this problem. The basic idea is to decompose a document-like representation of an image into two components, one for dimension reduction and the other for residual information preservation. The computing of similarity of two images can be transferred to measuring similarities of their components. The decomposition has two major merits: 1) these components have good properties which enable them to be efficiently indexed and retrieved; 2) The decomposition has better generalization ability than other dimension reduction algorithms. The decomposition can be achieved by either a graphical model or a matrix factorization approach. Theoretic analysis and extensive experiments over a 2.3 million image database show that this framework is scalable to index large scale image database to support fast and accurate visual search.
Cunning circuits confound crooks

circuit you developed just weeks or months ago has suspiciously appeared in another company’s device or system in a slightly modified or enhanced form. Or maybe you’re an IP provider, and you’ve heard through the grapevine that your customer is building more chips or boxes than the license agreement they signed with you gives them the right to do. All of these situations, as well as other detrimental scenarios that a few minutes of “what-if ” thinking will uncover, unfortunately suggest that you’re the victim of design theft. In one manifestation of the general concept, someone might make an exact duplicate of your entire design, a technique that this article refers to as “cloning. ”The other harder but possibly even more damaging form of theft is “reverse-engineering. ” In that scenario, someone uses the information stored in the programmable-logic device to reconstruct the original circuit details and then alters and incorporates those details in part or whole into other designs. Design theft has historically been of greatest concern with military projects. But, as programmable logic’s integration capability and performance rise and as cost per gate plummets, the potential for cloning and reverse-engineering becomes a concern for an increasing number and variety of broader-based applications. Fundamental limits still exist as to the AS PROGRAMMABLE LOGIC INCREASINGLY ENCROACHES ON HIGH-VOLUME TERRITORY
Carbon nanoparticles downregulate expression of basic fibroblast growth factor in the heart during embryogenesis

Carbon nanoparticles, with their high biocompatibility and low toxicity, have recently been considered for biomedical applications, including antiangiogenic therapy. Critical to normal development and tumor formation, angiogenesis is the process of forming capillary blood vessels from preexisting vessels. In the present study, we evaluated the effects of diamond and graphite nanoparticles on the development of chicken embryos, as well as vascularization of the chorioallantoic membrane and heart at the morphological and molecular level. Nanoparticles did not affect either body/heart weight or serum indices of the embryos’ health. However, vascularization of the heart and the density of branched vessels were significantly reduced after treatment with diamond nanoparticles and, to a lesser extent, graphite nanoparticles. Application of nanoparticles significantly downregulated gene and protein expression of the proangiogenic basic fibroblast growth factor, indicating that both diamond and graphite nanoparticles inhibit angiogenesis.
Multi-slot and multi-level coding technique over amplitude-shift keying modulation for optical communication links

A new multi-slot and multi-level coding technique, which is a combination of multiplexing and coding technique, known as Multi Slot Amplitude Coding (MSAC) is presented. This technique offers better flexibility in terms of defining the symbol based on time slot and signal level in order to generate more unique symbols as a line code with better clock information since all the symbols have zero level (return-to-zero) at the beginning. In this paper, 3 tributary channels at 10Gbps per channel is simulated for optical communication link with amplitude-shift keying (ASK) modulation. The performance of 3×10Gbps MSAC is compared with 30Gbps RZ ETDM in terms of spectral width, OSNR and chromatic dispersion tolerance. The results show that 3×10Gbps MSAC occupy 47% less spectral width, thus a better chromatic dispersion tolerance compared to 30Gbps RZ ETDM with 10.5dB OSNR penalty at the BER of 10−9. If the channel bit rate is increased to 40Gbps, the aggregate bit rate will be 120Gbps with 5.6dB OSNR penalty.
1 Beliefs , Belief Revision , and Noisy Sensors

In logical AI, an agent’s beliefs are typically categorical, in that they are specified by a set of formulas. An agent may change its beliefs as a result of being informed in one fashion or another about some aspect of the world, or following the execution of some action. The areas of belief revision and reasoning about action deal with just such change in belief. However, most information about the real world is not categorical. While there are well-established accounts for accommodating noncategorical information via probability theory, it is worth asking whether probabilistic information may be reconciled with the logical accounts of belief change. We present such an account in this paper. An agent receives uncertain information as input and its probabilities, expressed as probabilities on possible worlds, are updated via Bayesian conditioning. A set of formulas among the (noncategorical) beliefs is identified as the agent’s (categorical) belief set. This set is defined in terms of the most probable worlds such that the summed probability of these worlds exceeds a given threshold. The effect of this updating on the belief set is examined with respect to its appropriateness as a revision operator. It proves to be the case that a subset of the classical AGM belief revision postulates are satisfied. Most significantly, the success postulate is not guaranteed to hold. However it does hold after a sufficient number of iterations. Not is it the case that in revising by a formula consistent with the agent’s beliefs, revision corresponds to expansion. On the other hand, limiting cases of the presented approach correspond to specific approaches to revision that have appeared in the literature. It is a great pleasure to dedicate this paper to Hector Levesque on the occasion of his 60th birthday. While Hector’s work has broadly focussed on representational aspects of an agent’s beliefs together with accounts of reasoning – whether epistemic, nonmonotonic, limited, in a theory of action, or otherwise – it has certainly touched on many other areas over the years. This paper outlines a possible linking of two such areas, that of reasoning about noisy sensors on the one hand [Bacchus, Halpern, and Levesque 1999], and revision in the presence of (categorical) observations on the other [Shapiro, Pagnucco, Lespérance, and Levesque 2011].
A multi-stage sound-to-letter recognizer

The task of sound-to-letter recognition of unknown utterances is a difficult and important problem for Natural Language systems. This thesis presents a multi-stage sound-to-letter recognizer that is based on Finite State Transducer (FST) architecture, and relies on components of SUMMIT and ANGIE systems developed in the Spoken Language Systems Group. Specifically, we focus on isolated-word spelling recognition on the Phonebook corpus of single telephone utterances that offer a realistic model of real-life speech. The proposed recognition framework is developed around a new class of sublexical units, called spellnemes, which seamlessly integrate phonemic and spelling information. The advantages of the proposed multi-stage approach include improved performance due to tight linguistic constraint on recognition hypothesis, and promising generalization performance. We achieve a Letter Accuracy Rate of 69.5 percent on out-of-vocabulary utterances, which improves to 89.4 percent for the pure phonetic recognition task. Thesis Supervisor: Stephanie Seneff Title: Principal Research Scientist
Small footprint discrete-return LiDAR in tree species recognition

In forest inventories, the species information is crucial for economical, ecological and technical reasons. Species recognition is currently a bottleneck in practical remote sensing applications. Here, we examined species discrimination using tree-level LiDAR features in discrete-return data. The aim was to examine the robustness and explanatory power of the intensity and height distribution features. A dataset consisting of 13890 trees from 117 stands in southern Finland (61°50'N, 24°20'E) was used. The data of two LiDAR sensors was fused using intensity normalization in natural targets. Age dependency of first-return intensity was observed in spruce and birch trees, which needs to be considered in using LiDAR intensity metrics. Classification of Scots pine, Norway spruce and birch was tested and accuracy was 81−85%. Separation of pine and spruce was more accurate, 91−93%. We also present results for 15 rare conifer and broadleaved species. To enhance the classification accuracy of birch, we propose co-use of image features.
Drizzle: A Method for the Linear Reconstruction of Undersampled Images

We have developed a method for the linear reconstruction of an image from undersampled, dithered data. The algorithm, known as Variable‐Pixel Linear Reconstruction, or informally as “Drizzle,” preserves photometry and resolution, can weight input images according to the statistical significance of each pixel, and removes the effects of geometric distortion on both image shape and photometry. This paper presents the method and its implementation. The photometric and astrometric accuracy and image fidelity of the algorithm as well as the noise characteristics of output images are discussed. In addition, we describe the use of drizzling to combine dithered images in the presence of cosmic rays.
A Multi-Layer MRF Model for Object-Motion Detection in Unregistered Airborne Image-Pairs

In this paper, we give a probabilistic model for automatic change detection on airborne images taken with moving cameras. To ensure robustness, we adopt an unsupervised coarse matching instead of a precise image registration. The challenge of the proposed model is to eliminate the registration errors, noise and the parallax artifacts caused by the static objects having considerable height (buildings, trees, walls etc.) from the difference image. We describe the background membership of a given image point through two different features, and introduce a novel three-layer Markov random field (MRF) model to ensure connected homogenous regions in the segmented image.
Double Porosity Models for Absolutely Rigid Body via Reiterated Homogenization

Double porosity models for the liquid filtration in an absolutely rigid body is derived from homogenization theory. The governing equations of the fluid dynamics on the microscopic level consist of the Stokes system for a slightly compressible viscous fluid, occupying a crack -- pore space. In turn, this domain is a union of two independent systems of cracks (fissures) and pores. We suppose that the dimensionless size $\delta$ of pores depends on the dimensionless size $\varepsilon$ of cracks: $\delta=\varepsilon^{r}$ with $r>1$. The rigorous justification is fulfilled for homogenization procedure as the dimensionless size of the cracks tends to zero, while the solid body is geometrically periodic. As a result, for the long-time process we derive the usual Darcy equations of filtration for the liquid in cracks, while the liquid in pores is blocked and unmoved. For the short-time processes the homogenized system consists of acoustic equations, describing a two-velocity continuum with three independent parameters: the liquid velocity in pores, the liquid velocity in cracks and the common pressure. The proofs are based on the method of reiterated homogenization, suggested by G. Allaire and M. Briane.
LaraDB: A Minimalist Kernel for Linear and Relational Algebra Computation

Analytics tasks manipulate structured data with variants of relational algebra (RA) and quantitative data with variants of linear algebra (LA). The two computational models have overlapping expressiveness, motivating a common programming model that affords unified reasoning and algorithm design. At the logical level we propose LARA, a lean algebra of three operators, that expresses RA and LA as well as relevant optimization rules. We show a series of proofs that position LARA at just the right level of expressiveness for a middleware algebra: more explicit than MapReduce but more general than RA or LA. At the physical level we find that the LARA operators afford efficient implementations using a single primitive that is available in a variety of backend engines: range scans over partitioned sorted maps. To evaluate these ideas, we implemented the LARA operators as range iterators in Apache Accumulo, a popular implementation of Google's BigTable. First we show how LARA expresses a sensor quality control task, and we measure the performance impact of optimizations LARA admits on this task. Second we show that the LARADB implementation outperforms Accumulo's native MapReduce integration on a core task involving join and aggregation in the form of matrix multiply, especially at smaller scales that are typically a poor fit for scale-out approaches. We find that LARADB offers a conceptually lean framework for optimizing mixed-abstraction analytics tasks, without giving up fast record-level updates and scans.
A New Morphological Algorithm for Round Contour Extraction of Brinell Hardness Indent Image

This Paper described a new algorithm for round contour extraction of indent image on metal surface after Brinell hardness test. Morphological theory was employed to extract round contour of indent image effectively. Based on characteristic of grayscale image, structural element was chose according to its shape feature and grayscale feature in order to acquire better result. Before the stage of contour extraction, fuzzy theory was employed to enhance indent image. Validity of this approach is demonstrated using several indent images.
Case-based Reenement of Knowledge-based Neural Networks

Knowledge-Based Neural Networks (KBNN) are concerned with the use of domain knowledge to determine the initial structure of Neural Networks(NN). KBNN are shown to classify better unseen examples than randomly initialized NN. In this paper we study the potential of Case-Based Reasoning (CBR) for further improvement of a trained KBNN. The idea is to apply CBR only for correction of KBNN solutions that seem to be wrong. Potential corrections are searched by matching the current situation against the stored cases formed from the KBNN training set examples. The approach is tested on ve well-known machine learning (ML) benchmarkdata sets described with numerical attributes glass , diabetes, liver disorders, breast cancer and iris. Experiments indicate an increased classiication accuracy in the case of three data sets and practically the same performance in the rest 2 data sets.
Application of a Genetically Encoded Biosensor for Live Cell Imaging of L-Valine Production in Pyruvate Dehydrogenase Complex-Deficient Corynebacterium glutamicum Strains

The majority of biotechnologically relevant metabolites do not impart a conspicuous phenotype to the producing cell. Consequently, the analysis of microbial metabolite production is still dominated by bulk techniques, which may obscure significant variation at the single-cell level. In this study, we have applied the recently developed Lrp-biosensor for monitoring of amino acid production in single cells of gradually engineered L-valine producing Corynebacterium glutamicum strains based on the pyruvate dehydrogenase complex-deficient (PDHC) strain C. glutamicum ΔaceE. Online monitoring of the sensor output (eYFP fluorescence) during batch cultivation proved the sensor's suitability for visualizing different production levels. In the following, we conducted live cell imaging studies on C. glutamicum sensor strains using microfluidic chip devices. As expected, the sensor output was higher in microcolonies of high-yield producers in comparison to the basic strain C. glutamicum ΔaceE. Microfluidic cultivation in minimal medium revealed a typical Gaussian distribution of single cell fluorescence during the production phase. Remarkably, low amounts of complex nutrients completely changed the observed phenotypic pattern of all strains, resulting in a phenotypic split of the population. Whereas some cells stopped growing and initiated L-valine production, others continued to grow or showed a delayed transition to production. Depending on the cultivation conditions, a considerable fraction of non-fluorescent cells was observed, suggesting a loss of metabolic activity. These studies demonstrate that genetically encoded biosensors are a valuable tool for monitoring single cell productivity and to study the phenotypic pattern of microbial production strains.
A self calibration technique for monolithic high-resolution D/A converters

A D/A (digital/analog) and A/D (analog/digital) self-calibrated technique is presented which does not need a calibration period, additional trimming, or external component and is insensitive to process variations. The technique is based on calibration of a current source. The basic block diagram of a 16-bit D/A converter is shown. The common calibration circuitry is also presented. The measured signal-to-noise ratio, including harmonic distortion, versus the output voltage of the 16-bit DAC is shown. <<ETX>>
The Burbea-Rao and Bhattacharyya Centroids

We study the centroid with respect to the class of information-theoretic Burbea-Rao divergences that generalize the celebrated Jensen-Shannon divergence by measuring the non-negative Jensen difference induced by a strictly convex and differentiable function. Although those Burbea-Rao divergences are symmetric by construction, they are not metric since they fail to satisfy the triangle inequality. We first explain how a particular symmetrization of Bregman divergences called Jensen-Bregman distances yields exactly those Burbea-Rao divergences. We then proceed by defining skew Burbea-Rao divergences, and show that skew Burbea-Rao divergences amount in limit cases to compute Bregman divergences. We then prove that Burbea-Rao centroids can be arbitrarily finely approximated by a generic iterative concave-convex optimization algorithm with guaranteed convergence property. In the second part of the paper, we consider the Bhattacharyya distance that is commonly used to measure overlapping degree of probability distributions. We show that Bhattacharyya distances on members of the same statistical exponential family amount to calculate a Burbea-Rao divergence in disguise. Thus we get an efficient algorithm for computing the Bhattacharyya centroid of a set of parametric distributions belonging to the same exponential families, improving over former specialized methods found in the literature that were limited to univariate or “diagonal” multivariate Gaussians. To illustrate the performance of our Bhattacharyya/Burbea-Rao centroid algorithm, we present experimental performance results for k-means and hierarchical clustering methods of Gaussian mixture models.
A 60 kb/s–10 Mb/s Adaptive Frequency Hopping Transceiver for Interference-Resilient Body Channel Communication

An interference-resilient 60 kb/s-10 Mb/s body channel transceiver using the human body as a signal transmission medium is designed for multimedia and medical data transaction in body-area network. The body antenna effect which interferes with signals in the human body channel is examined. The body-induced interferences degrade the SIR of the signal to -22 dB in the worst case. In order to overcome the body antenna effect, a 4-channel adaptive frequency hopping scheme using the 30-120 MHz band is introduced to the body channel transceiver. A direct-switching modulator using dual frequency synthesizers and a DLL-based demodulator are proposed for 10 Mb/s FSK and the 4.2 mus hopping time. The transceiver fabricated with 0.18 mum CMOS withstands -28 dB SIR and its operating distance is over 1.8 m with - 25 dB SIR. Its energy consumption is 0.37 nJ/b with -65 dBm sensitivity.
Explaining Data-Driven Document Classifications

Many document classification applications require human understanding of the reasons for data-driven classification decisions by managers, client-facing employees, and the technical team. Predictive models treat documents as data to be classified, and document data are characterized by very high dimensionality, often with tens of thousands to millions of variables (words). Unfortunately, due to the high dimensionality, understanding the decisions made by document classifiers is very difficult. This paper begins by extending the most relevant prior theoretical model of explanations for intelligent systems to account for some missing elements. The main theoretical contribution is the definition of a new sort of explanation as a minimal set of words (terms, generally), such that removing all words within this set from the document changes the predicted class from the class of interest. We present an algorithm to find such explanations, as well as a framework to assess such an algorithm's performance. We demonstrate the value of the new approach with a case study from a real-world document classification task: classifying web pages as containing objectionable content, with the goal of allowing advertisers to choose not to have their ads appear on those pages. A second empirical demonstration on news-story topic classification shows the explanations to be concise and document-specific, and to be capable of providing understanding of the exact reasons for the classification decisions, of the workings of the classification models, and of the business application itself. We also illustrate how explaining the classifications of documents can help to improve data quality and model performance.
Monolithic low phase noise oscillators for moderate frequency applications

Low noise oscillators are critical building blocks in a wide range of commercial electronics. Increased levels of integration have created a strong need for integrated oscillator solutions despite generally inferior noise performance. The development of non-linear noise models that can accurately and efficiently predict noise in ring oscillators aids designers in optimizing noise performance in integrated oscillator solutions. Extending a piecewise constant model of noise in an oscillator and the resulting timing jitter reveals how the noise at the oscillator nodes changes during each portion of the cycle. The model can then be used to examine the effects of changing various process and design parameters such as threshold voltages and the effective stage gain. This analysis tool provides a means for designers to evaluate potential improvements of their oscillator design. In some cases approximate analytic solutions can be found that provide better insight into the timing jitter. A simple differential oscillator design illustrates the use of this analysis. The oscillator achieves an analog tuning range of 259MHz-314MHz (extendable with switched capacitors) with a normalized jitter of 102ppm. Thesis Supervisor: Charles G. Sodini Title: Professor of Electrical Engineering