Miracle-dz/UniFAR-FTUs huggingface dataset:
Applications of Ultrasound Stimulated Vibro-acoustography

Vibro-acoustography is a method of imaging and measurement that uses ultrasound to produce radiation force to vibrate objects. The radiation force is concentrated laterally by focusing the ultrasound beam. The radiation force is limited in depth by intersecting two beams at different frequencies so that there is interference between the beams at the difference frequency only at their intersection. This results in a cyclic radiation stress of limited spatial extent on or within the object of interest. The resulting harmonic displacement of the object is detected by its acoustic emission, with ultrasound Doppler measurement, with a laser interferometer or with a hydrophone. The displacement is a complicated function of the object material parameters. However, significant images and measurements can be made with this arrangement. Vibro-acoustography can produce images of biologically relevant objects such as breast microcalcification, vessel calcifications, heart valves, and normal arteries. Specific examples of these results will be described.
Typestate-oriented programming

Objects model the world, and state is fundamental to a faithful modeling. Engineers use state machines to understand and reason about state transitions, but programming languages provide little support for reasoning about or implementing these state machines, causing software defects and lost productivity when objects are misused. We propose Typestate-Oriented Programming as a natural extension to the object paradigm, where objects are modeled not just in terms of classes, but in terms of changing states. Each state may have its own representation and methods which may transition the object into a new state. A flow-sensitive, permission-based type system helps developers track which state objects are in. First-class typestates are a powerful abstraction that will help developers model and reuse objects more efficiently and correctly.
Bilingually Motivated Word Segmentation for SMT

We introduce a bilingually motivated word segmentation approach to languages where word boundaries are not orthographically marked, with application to Phrase-Based Statistical Machine Translation (PB-SMT). Our approach is motivated from the insight that PB-SMT systems can be improved by optimising the input representation to reduce the predictive power of translation models. We firstly present an approach to optimise the existing segmentation of both source and target languages for PBSMT and demonstrate the effectiveness of this approach using a Chinese– English MT task, i.e. to measure the influence of the segmentation on the performance of PB-SMT systems. We report a 5.44% relative increase in Bleu score and a consistent increase according to other metrics. We then generalise this method for Chinese word segmentation without relying on any segmenters and show that using our segmentation PB-SMT can achieve more consistent state-of-the-art performance across two domains. There are two main advantages of our approach. First of all, it is adapted to the specific translation task at hand by taking the corresponding source (target) language into account. Secondly, this approach does not rely on manually segmented training data so that it can be automatically adapted for different domains.
Aligning and Using an English-Inuktitut Parallel Corpus

A parallel corpus of texts in English and in Inuktitut, an Inuit language, is presented. These texts are from the Nunavut Hansards. The parallel texts are processed in two phases, the sentence alignment phase and the word correspondence phase. Our sentence alignment technique achieves a precision of 91.4% and a recall of 92.3%. Our word correspondence technique is aimed at providing the broadest coverage collection of reliable pairs of Inuktitut and English morphemes for dictionary expansion. For an agglutinative language like Inuktitut, this entails considering substrings, not simply whole words. We employ a Pointwise Mutual Information method (PMI) and attain a coverage of 72.3% of English words and a precision of 87%.
Two Semantic Issues in a Probabilistic Rough Set Model

Probabilistic rough set models are quantitative generalizations of the classical and qualitative Pawlak model by considering degrees of overlap between equivalence classes and a set to be approximated. The extensive studies, however, have not sufficiently addressed some semantic issues in a probabilistic rough set model. This paper examines two fundamental semantics-related questions. One is the interpretation and determination of the required parameters, i.e., thresholds on probabilities, for defining the probabilistic lower and upper approximations. The other is the interpretation of rules derived from the probabilistic positive, boundary and negative regions. We show that the two questions can be answered within the framework of a decision-theoretic rough set model. Parameters for defining probabilistic rough sets are interpreted and determined in terms of loss functions based on the well established Bayesian decision procedure. Rules constructed from the three regions are associated with different actions and decisions, which immediately leads to the notion of three-way decision rules. A positive rule makes a decision of acceptance, a negative rule makes a decision of rejection, and a boundary rules makes a decision of deferment. The three-way decisions are, again, interpreted based on the loss functions. (This work is partially supported by a Discovery Grant from NSERC Canada. The author thanks the reviewers for their constructive comments.)
Sunitinib inhibits inflammatory corneal lymphangiogenesis.

PURPOSE To evaluate the antilymphangiogenic potential of multi-target tyrosine kinase inhibitor sunitinib in corneal neovascularization (NV). METHODS Inflammatory corneal NV was induced by thermal cauterization applied in the central cornea of mice, to which sunitinib malate was daily administered by gavage or not. At days 6, 11, or 17 post cauterization, lymphatic and blood vessels, as well as inflammatory cells were immunostained and quantified in whole-mounted corneas. RT-PCRs were performed to evidence VEGF-A, VEGF-C, VEGF-D, placental growth factor (PlGF), and soluble vascular endothelial growth factor receptor (VEGFR)-1 and -2 (sVEGFR-1, sVEGFR-2) expressions. Macrophages were isolated from mice peritoneal cavity following thioglycollate injection to produce conditioned medium. The effects of sunitinib were evaluated in vitro in the aortic and lymphatic ring assays in the presence or not of macrophage conditioned medium. RESULTS Sunitinib treatment drastically reduced pathologic corneal lymphangiogenesis and angiogenesis. Reduced F4/80+ cell infiltration was evidenced in sunitinib-treated mice and was associated to decreased VEGF-A (by 50%, P < 0.01) and VEGF-C (by 35%, P < 0.01) expressions, while VEGF-D and sVEGFR-2 expressions were not affected. In vitro, sunitinib dose-dependently inhibited aortic ring outgrowth, but failed to affect lymphangiogenesis in the lymphatic ring assay. However, macrophage conditioned medium-enhanced angiogenesis and lymphangiogenesis were both strongly counteracted by sunitinib treatment. Mechanistically, sunitinib blocked VEGFR-2 phosphorylation induced by VEGF-A released by macrophages. CONCLUSIONS Sunitinib exerts antihemangiogenic and antilymphangiogenic effects in vivo by reducing F4/80+ cell recruitment and interacting with their released factors.
Bias aware lexicon-based Sentiment Analysis of Malay dialect on social media data: A study on the Sabah Language

Sentiment Analysis (SA) has gained its popularity over the years for the benefit it brings to the development of economy, sociology and politic. SA enables observation, experiment, and quantification of emotions of the public toward a particular issue. However, there is not much SA done with respect to the Malay Language, especially in the context of the Malay dialects used in social media. The research presented in this paper aims to perform SA on one of the derivatives of the Malay language, namely Sabah Language. The Sabah Language, unlike many other languages, does not have a fixed spelling and, when used in an unstructured form as in the case of social media, poses particular difficulties for SA. This paper takes a lexicon-based approach to SA of the Sabah Language as used on social media. For the investigation, the corpuses selected were Facebook posts and tweets written in the Sabah language, 443 posts and tweets in total. Each was manually annotated as positive, negative or neutral by three annotators. As Sabah Language is a derivative of Malay language, the words used in Sabah Language contains most of Malay words. That is why, in Sentiment-Lexicon (SL) construction process, opinion-bearing Malay SL is retrieved, modified and expanded to build Sabah SL. Three different methods of assigning scores to the words in SL (opinion-bearing words) were employed during SL construction: (i) Simple PSA, (ii) Simple PSA with Switch Negation (PSA-SN) and (iii) Strength-based PSA. In this paper, pre-processing phase that includes spellchecker and shortform corrector is also implemented to reduce distinct word to be analyzed for SA. In classification phase, two classification methods, simple and bias aware classifications, were used to classify the posts. Experiments are conducted to show the effect of SL modification and expansion, the effect of pre-processing as well as the effect of bias-aware classification to the SA performed. Results show the highest accuracy of 85.10% was achieved using bias-aware classification with the modified and expanded SL, scores are assigned using Simple PSA and the pre-processed text.
MODIST: Transparent Model Checking of Unmodified Distributed Systems

MODIST is the first model checker designed for transparently checking unmodified distributed systems running on unmodified operating systems. It achieves this transparency via a novel architecture: a thin interposition layer exposes all actions in a distributed system and a centralized, OS-independent model checking engine explores these actions systematically. We made MODIST practical through three techniques: an execution engine to simulate consistent, deterministic executions and failures; a virtual clock mechanism to avoid false positives and false negatives; and a state exploration framework to incorporate heuristics for efficient error detection. We implemented MODIST on Windows and applied it to three well-tested distributed systems: Berkeley DB, a widely used open source database; MPS, a deployed Paxos implementation; and PACIFICA, a primary-backup replication protocol implementation. MODIST found 35 bugs in total. Most importantly, it found protocol-level bugs (i.e., flaws in the core distributed protocols) in every system checked: 10 in total, including 2 in Berkeley DB, 2 in MPS, and 6 in PACIFICA.
Development and Utility of an Internal Threshold Control (ITC) Real-Time PCR Assay for Exogenous DNA Detection

Sensitive and specific tests for detecting exogenous DNA molecules are useful for infectious disease diagnosis, gene therapy clinical trial safety, and gene doping surveillance. Taqman real-time PCR using specific sequence probes provides an effective approach to accurately and quantitatively detect exogenous DNA. However, one of the major challenges in these analyses is to eliminate false positive signals caused by either non-targeted exogenous or endogenous DNA sequences, or false negative signals caused by impurities that inhibit PCR. Although multiplex Taqman PCR assays have been applied to address these problems by adding extra primer-probe sets targeted to endogenous DNA sequences, the differences between targets can lead to different detection efficiencies. To avoid these complications, a Taqman PCR-based approach that incorporates an internal threshold control (ITC) has been developed. In this single reaction format, the target sequence and ITC template are co-amplified by the same primers, but are detected by different probes each with a unique fluorescent dye. Sample DNA, a prescribed number of ITC template molecules set near the limit of sensitivity, a single pair of primers, target probe and ITC probe are added to one reaction. Fluorescence emission signals are obtained simultaneously to determine the cycle thresholds (Ct) for amplification of the target and ITC sequences. The comparison of the target Ct with the ITC Ct indicates if a sample is a true positive for the target (i.e. Ct less than or equal to the ITC Ct) or negative (i.e. Ct greater than the ITC Ct). The utility of this approach was demonstrated in a nonhuman primate model of rAAV vector mediated gene doping in vivo and in human genomic DNA spiked with plasmid DNA.
This Paper Is Included in the Proceedings of the 11th Usenix Symposium on Networked Systems Design and Implementation (nsdi '14). Senic: Scalable Nic for End-host Rate Limiting Senic: Scalable Nic for End-host Rate Limiting

Rate limiting is an important primitive for managing server network resources. Unfortunately, software-based rate limiting suffers from limited accuracy and high CPU overhead, and modern NICs only support a handful of rate limiters. We present SENIC, a NIC design that can natively support 10s of thousands of rate limiters—100x to 1000x the number available in NICs today. The key idea is that the host CPU only classifies packets, en-queues them in per-class queues in host memory, and specifies rate limits for each traffic class. On the NIC, SENIC maintains class metadata, computes the transmit schedule, and only pulls packets from host memory when they are ready to be transmitted (on a real time basis). We implemented SENIC on NetFPGA, with 1000 rate lim-iters requiring just 30KB SRAM, and it was able to accurately pace packets. Further, in a memcached benchmark against software rate limiters, SENIC is able to sustain up to 250% higher load, while simultaneously keeping tail latency under 4ms at 90% network utilization.
Learning and Extracting Initial Mealy Automata with a Modular Neural Network Model

A hybrid recurrent neural network is shown to learn small initial mealy machines (that can be thought of as translation machines translating input strings to corresponding output strings, as opposed to recognition automata that classify strings as either grammatical or nongrammatical) from positive training samples. A well-trained neural net is then presented once again with the training set and a Kohonen self-organizing map with the star topology of neurons is used to quantize recurrent network state space into distinct regions representing corresponding states of a mealy machine being learned. This enables us to extract the learned mealy machine from the trained recurrent network. One neural network (Kohonen self-organizing map) is used to extract meaningful information from another network (recurrent neural network).
CLASSIFICATION USING CASCADED PROBABILISTIC LATENT SEMANTIC ANALYSIS Emrah

In this paper we propose a novel approach of image representation for weakly supervised scene classification that mainly combine two popular methods in the literature: Bag-of-Words (BoW) modeling and probabilistic Latent Semantic Analysis (pLSA) modeling. The new image representation scheme called Cascaded pLSA performs pLSA in a hierarchical sense after the BoW representation based on SIFT features is extracted. We associate location information with the conventional BoW/pLSA algorithm by subdividing each image into sub-regions iteratively at different resolution levels and implementing a pLSA model for each sub-region individually. Finally, an image is represented by concatenated topic distributions of each sub-region. The performance of our method is compared with the most successful methods in the literature using the same dataset. In the experiments, it has been seen that the proposed method outperforms the others in that particular dataset. BASAMAKLI OLASILIKSAL GİZLİ SEMANTİK ANALİZ İLE SAHNE SINIFLANDIRILMASI
J-ALINAs: A JADE-based Architecture for Linguistic Agents

In this paper we present J-ALINAs, a JADE-based Architecture for LINguistic Agents. The purpose of this architecture is to support communication between agents whose beliefs and intentions are driven by different, heterogeneous, knowledge models. This objective, often referred in literature as semantic coordination, can be carried on through the identification of specific agent roles and behaviors dedicated to the mediation of agents' knowledge. In particular, such minimal hypotheses suggest an intelligent exploitation of natural language based technologies (resources and systems) as a necessary choice for capturing those similarities between the different knowledge models of agents trying to communicate, which are not in any way formally ratified. We aim to provide a flexible framework to be adopted in open multi-agent environments across different scenarios, providing a further abstraction level from the underlying details related to specific semantic coordination approaches; a high-cohesion and low-coupling design, and an agent-interaction protocol make possible for the architecture to face non-ideal use cases optimizing the communications among the agents. We discuss significant design issues, provide a prototypical implementation based on the JADE platform and a case study - MAPLE - integrating an ontology mapping component in the framework, showing flexibility of the architecture in real applications and its independence from any specific mapping algorithm. Finally we will look at the semantic coordination protocol we designed from a strictly formal perspective, providing a CCS (Milner's Calculus for Communicating Systems) description of the protocol itself.
Solving SDD linear systems in nearly mlog1/2n time

We show an algorithm for solving symmetric diagonally dominant (SDD) linear systems with m non-zero entries to a relative error of ε in O(m log1/2 n logc n log(1/ε)) time. Our approach follows the recursive preconditioning framework, which aims to reduce graphs to trees using iterative methods. We improve two key components of this framework: random sampling and tree embeddings. Both of these components are used in a variety of other algorithms, and our approach also extends to the dual problem of computing electrical flows. We show that preconditioners constructed by random sampling can perform well without meeting the standard requirements of iterative methods. In the graph setting, this leads to ultra-sparsifiers that have optimal behavior in expectation. The improved running time makes previous low stretch embedding algorithms the running time bottleneck in this framework. In our analysis, we relax the requirement of these embeddings to snowflake spaces. We then obtain a two-pass approach algorithm for constructing optimal embeddings in snowflake spaces that runs in O(m log log n) time. This algorithm is also readily parallelizable.
Ceph as a Scalable Alternative to the Hadoop Distributed File System

The Hadoop distributed file System (HDFS) has a single metadata server that sets a hard limit on its maximun size. Ceph, a high-performace distributed file system under development since 2005 and now supported in Linux, bypasses the scaling limits of HDFS. We describe Ceph and its elements and provide instruction for intalling a demostration system that can be used with Hadoop.
Object detection using Hough transform and Conditional Random Field model

Hough transform provides a different and effective way for object detection. This approach has attracted much attention since the implicit shape model (ISM) was proposed. Inspired by the Implicit Shape Model and Conditional Random Field (CRF), we present in this paper a conditional probabilistic model to formulate the relationship between the voting elements and the hypotheses in the Hough transform. Based on this model, an efficient object detection scheme is proposed and experimental results demonstrate the effectiveness of the proposed scheme.
Superstrings on NSNS PP-Waves and Their CFT Duals

We investigate the correspondence between superstring theory on pp-wave background with NSNS-flux and superconformal field theory on a symmetric orbifold. This correspondence can be regarded as the ``Penrose limit'' of AdS_3/CFT_2 correspondence. Superstring theory on the Penrose limit of AdS_3 x S^3 (x M^4) (M^4 = T^4 or K3) with NSNS-flux can be described by a generalization of Nappi-Witten model. We quantize this system in the covariant gauge and obtain the spectrum of superstring theory. In the dual CFT point of view, the Penrose limit means concentrating on the subsector of almost BPS states with large R-charges. We show that stringy states can be embedded in the single-particle Hilbert space of symmetric orbifold theory.
Explore the evolution of development topics via on-line LDA

Software repositories such as revision control systems and bug tracking systems are usually used to manage the changes of software projects. During software maintenance and evolution, software developers and stakeholders need to investigate these repositories to identify what tasks were worked on in a particular time interval and how much effort was devoted to them. A typical way of mining software repositories is to use topic analysis models, e.g., Latent Dirichlet Allocation (LDA), to identify and organize the underlying structure in software documents to understand the evolution of development topics. These previously LDA-based topic analysis models can capture either changes on the strength (popularity) of various development topics over time (i.e., strength evolution) or changes in the content (the words that form the topic) of existing topics over time (i.e., content evolution). Unfortunately, few techniques can capture both strength and content evolution simultaneously. However, both pieces of information are necessary for developers to fully understand how software evolves. In this paper, we propose a novel approach to analyze commit messages within a project's lifetime to capture both strength and content evolution simultaneously via Online Latent Dirichlet Allocation (On-Line LDA). Moreover, the proposed approach also provides an efficient way to detect emerging topics in real development iteration when a new feature request arrives at a particular time, thus helping project stakeholds progress their projects smoothly.