Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust multiple-sensing-modality data fusion using Gaussian Process Implicit Surfaces

The ability to build high-fidelity 3D representations of the environment from sensor data is critical for autonomous robots. Multi-sensor data fusion allows for more complete and accurate representations. Furthermore, using distinct sensing modalities (i.e. sensors using a different physical process and/or operating at different electromagnetic frequencies) usually leads to more reliable perception, especially in challenging environments, as modalities may complement each other. However, they may react differently to certain materials or environmental conditions, leading to catastrophic fusion. In this paper, we propose a new method to reliably fuse data from multiple sensing modalities, including in situations where they detect different targets. We first compute distinct continuous surface representations for each sensing modality, with uncertainty, using Gaussian Process Implicit Surfaces (GPIS). Second, we perform a local consistency test between these representations, to separate consistent data (i.e. data corresponding to the detection of the same target by the sensors) from inconsistent data. The consistent data can then be fused together, using another GPIS process, and the rest of the data can be combined as appropriate. The approach is first validated using synthetic data. We then demonstrate its benefit using a mobile robot, equipped with a laser scanner and a radar, which operates in an outdoor environment in the presence of large clouds of airborne dust and smoke.
Analyzing and Improving Statistical Language Models for Speech Recognition

A speech recognizer is a device that translates speech into text. Many current speech recognizers contain two components, an acoustic model and a statistical language model. The acoustic model indicates how likely it is that a certain word corresponds to a part of the acoustic signal (e.g. the speech). The statistical language model indicates how likely it is that a certain word will be spoken next, given the words recognized so far. Even though the acoustic model might for example not be able to decide between the acoustically similar words "peach" and "teach", the statistical language model can indicate that the word "peach" is more likely if the previously recognized words are "He ate the". Current speech recognizers perform well on constrained tasks, but the goal of continuous, speaker independent speech recognition in potentially noisy environments with a very large vocabulary has not been reached so far. How can statistical language models be improved so that more complex tasks can be tackled? This is the question addressed in this thesis. Since the knowledge of the weaknesses of any theory often makes improving the theory easier, the central idea of this thesis is to analyze the weaknesses of existing statistical language models in order to subsequently improve them. To that end, we formally define a weakness of a statistical language model in terms of the logarithm of the total probability, LTP, a term closely related to the standard perplexity measure used to evaluate statistical language models. This definition is applicable to many probabilistic models, including almost all of the currently used statistical language models. We apply our definition of a weakness to a frequently used statistical language model, called a bi-pos model. This results, for example, in a new modeling of unknown words which improves the performance of the model by 14% to 21%. Moreover, one of the identified weaknesses has prompted the development of our generalized N-pos language model, which is also outlined in this thesis. It can incorporate linguistic knowledge even if it extends over many words and this is not feasible in a traditional N-pos model. This leads to a discussion of what knowledge should be added to statistical language models in general and we give criteria for selecting potentially useful knowledge. These results show the usefulness of both our definition of a weakness and of performing an analysis of weaknesses of statistical language models in general.
Force-Sensing Silicone Retractor for Attachment to Surgical Suction Pipes

This paper presents a novel force-sensing silicone retractor that can be attached to a surgical suction pipe to improve the usability of the suction and retraction functions during neurosurgery. The retractor enables simultaneous utilization of three functions: suction, retraction, and retraction-force sensing. The retractor also reduces the number of tool changes and ensures safe retraction through visualization of the magnitude of the retraction force. The proposed force-sensing system is based on a force visualization mechanism through which the force is displayed in the form of motion of a colored pole. This enables surgeons to estimate the retraction force. When a fiberscope or camera is present, the retractor enables measurement of the retraction force with a resolution of 0.05 N. The retractor has advantages of being disposable, inexpensive, and easy to sterilize or disinfect. Finite element analysis and experiments demonstrate the validity of the proposed force-sensing system.
Efficacy of an Accelerated Hepatitis B Vaccination Program During Pregnancy

OBJECTIVE: To estimate the feasibility and immunogenicity of an accelerated hepatitis B vaccination schedule of 0, 1, and 4 months in high-risk pregnant women. METHODS: We conducted a prospective clinical trial of high-risk pregnant women who were hepatitis B surface antigen-negative at presentation for prenatal care. A detailed questionnaire was administered and eligible women received a hepatitis B vaccine intramuscularly on a 0-, 1-, and 4-month schedule. Adverse reactions and hepatitis B surface antigen seroconversion rates were documented. Factors influencing seroconversion were determined. RESULTS: Two hundred high-risk pregnant women were enrolled; 84% completed the three-dose vaccine series. Seroconversion (hepatitis B surface antigen 10 milli-international units/mL or greater) after one dose was 56% (95% confidence interval [CI], 49–63%), 77% (95% CI, 71–83%) after two doses, and 90% (95% CI, 85–94%) after completing three doses. Body mass index was inversely associated with seroconversion rates (P<.001). There was no single body mass index above which seroconversion did not occur. There were no serious adverse events; injection site discomfort was the most prevalent complaint (10.5%). CONCLUSION: An accelerated hepatitis B vaccination schedule at 0, 1, and 4 months in high-risk pregnant women is effective, practical, and well tolerated. This accelerated vaccine strategy can be completed during the course of pregnancy and provides another means of decreasing hepatitis B virus disease and transmission. LEVEL OF EVIDENCE: II
Identity and Location Priming Effects and their Temporal Stability in Young and Older Adults

ABSTRACT Unlike previous studies, the present experiment was designed to directly compare negative (NP) and positive priming (PP) in 37 young (mean age: 21.5 years) and 37 older adults (mean age: 69.4 years) by means of two independent identity- and location-based priming tasks. While identity PP was shown to be reduced in older adults, no age-related differences were found for location PP. This pattern of results supports the notion that mechanisms involved in identity- and location-based PP are largely independent of each other. Both age groups exhibited reliable identity and location NP although location NP was decreased in older adults. Extremely low temporal test–retest coefficients indicated that mechanisms underlying identity and location NP represent situational states rather than stable traits. This lack of temporal stability may also account for highly ambiguous results obtained in previous studies.
A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence

Multiagent systems is an expanding field that blends classical fields like game theory and decentralized control with modern fields like computer science and machine learning. This monograph provides a concise introduction to the subject, covering the theoretical foundations as well as more recent developments in a coherent and readable manner. The text is centered on the concept of an agent as decision maker. Chapter 1 is a short introduction to the field of multiagent systems. Chapter 2 covers the basic theory of singleagent decision making under uncertainty. Chapter 3 is a brief introduction to game theory, explaining classical concepts like Nash equilibrium. Chapter 4 deals with the fundamental problem of coordinating a team of collaborative agents. Chapter 5 studies the problem of multiagent reasoning and decision making under partial observability. Chapter 6 focuses on the design of protocols that are stable against manipulations by self-interested agents. Chapter 7 provides a short introduction to the rapidly expanding field of multiagent reinforcement learning. The material can be used for teaching a half-semester course on multiagent systems covering, roughly, one chapter per lecture.
Listen to Your Footsteps: Wearable Device for Measuring Walking Quality

In this paper, we present a low-cost context-aware technique for determining a user's walking quality. This is achieved by filtering and analyzing the acoustic signal generated when users walk. To extract the acoustic values of footsteps, we implemented a simple wearable device attached on the user's ankle. To verify our approach, we conducted a preliminary test using several pattern classification algorithms. The results show that our system achieves an 89.6% average for three different walking styles (best, good, and bad) and 86.9% for four different real-world ground sets (carpet, asphalt, sand, and wood). We believe that our technique can be applied to existing context-aware techniques as well as various unexplored domains in wearable devices.
View planning and automated data acquisition for three‐dimensional modeling of complex sites

Constructing highly detailed three‐dimensional (3‐D) models of large complex sites using range scanners can be a time‐consuming manual process. One of the main drawbacks is determining where to place the scanner to obtain complete coverage of a site. We have developed a system for automatic view planning called VuePlan. When combined with our mobile robot, AVENUE, we have a system that is capable of modeling large‐scale environments with minimal human intervention throughout both the planning and acquisition phases. The system proceeds in two distinct stages. In the initial phase, the system is given a two‐dimensional site footprint with which it plans a minimal set of sufficient and properly constrained covering views. We then use a 3‐D laser scanner to take scans at each of these views. When this planning system is combined with our mobile robot it automatically computes and executes a tour of these viewing locations and acquires them with the robot's onboard laser scanner. These initial scans serve as an approximate 3‐D model of the site. The planning software then enters a second phase in which it updates this model by using a voxel‐based occupancy procedure to plan the next best view (NBV). This NBV is acquired, and further NBVs are sequentially computed and acquired until an accurate and complete 3‐D model is obtained. A simulator tool that we developed has allowed us to test our entire view planning algorithm on simulated sites. We have also successfully used our two‐phase system to construct precise 3‐D models of real‐world sites located in New York City: Uris Hall on the campus of Columbia University and Fort Jay on Governors Island. © 2009 Wiley Periodicals, Inc.
Concise representations for approximate association rules

The quality of association rule mining has drawn more and more attention recently. One problem with the quality of the discovered association rules is the huge size of the extracted rule set. Often for a dataset, a huge number of rules can be extracted, but many of them can be redundant to other rules and thus useless in practice. Mining non-redundant rules is a promising approach to solve this problem. In this paper, we firstly propose a definition for redundancy; then we propose a concise representation called reliable basis for representing non-redundant association rules for both exact rules and approximate rules. We prove that the redundancy elimination based on the reliable basis does not reduce the belief to the extracted rules. We also prove that all association rules can be deduced from the reliable basis. Therefore the reliable basis is a lossless representation of association rules. Experimental results show that the reliable basis significantly reduces the number of extracted rules.
Evolution of an Adaptive Middleware Exploiting Architectural Reflection

Nowadays information systems are required to adapt themselves dynamically to the ever changing environment and requirements. Architectural reflection represents a principled means to address adaptivity. It also represents an emerging approach to deal with the software evolution issues. In this paper we aim to point out how systems exploiting architectural reflection to achieve adaptivity evolve in an organized, linear manner controlling easier their growth and complexity than systems based on ad hoc solutions. To sustain this affirmation we present the possible evolution improvements we gain through our Adaptive and Reflective Middleware (ARM).
PSMA specific antibodies and their diagnostic and therapeutic use

Prostate-specific membrane antigen (PSMA) is a membrane-bound glycoprotein highly restricted to prostatic epithelial cells. PSMA expression is increased in association with prostatic cancer, particularly in hormone refractory disease. Given its membrane-bound character, PSMA is an ideal sentinel molecule for use in targeting prostatic cancer cells. Monoclonal antibodies specific for PSMA are available, beginning with the antibody 7E11.C5 which originally defined PSMA and which has been developed for use in cancer detection via immunoscintiscanning in the ProstaScint™ test. Newer second generation antibodies specific for both linear amino acid sequence epitopes and protein conformational epitopes on the extracellular domain of PSMA have been reported. Although most of these are murine antibodies, both humanised and fully human examples have been developed. These antibodies are beginning to work their way into clinical applications for potential improved diagnostic and therapeutic uses. Results to date suggest that antibodies specific for extracellular epitopes are significantly better for clinical uses in vivo than the 7E11.C5 antibody that is specific for an intracellular epitope. Current knowledge relating to PSMA-specific antibodies and their clinical uses and potential is described and evaluated.
Exploiting Change Blindness for Image Compression

Change blindness refers to the inability of our visual system to memorize details in pictures. We suggest that, under certain conditions, regions containing such details can be identified and altered in a way that benefits image compression. We define change blindness maps and a compliant texture synthesis method to identify, remove and subsequently recover pixels which are subject to change blindness. Unlike comparable methods, ours is particularly intended for images with complex textures. We demonstrate via a subjective experiment that up to 15% of such an image can be altered in a way that is considered as natural and acceptable.
Discovery of Gene Function by Expression Profiling of the Malaria Parasite Life Cycle

The completion of the genome sequence for Plasmodium falciparum, the species responsible for most malaria human deaths, has the potential to reveal hundreds of new drug targets and proteins involved in pathogenesis. However, only ∼35% of the genes code for proteins with an identifiable function. The absence of routine genetic tools for studying Plasmodium parasites suggests that this number is unlikely to change quickly if conventional serial methods are used to characterize encoded proteins. Here, we use a high-density oligonucleotide array to generate expression profiles of human and mosquito stages of the malaria parasite's life cycle. Genes with highly correlated levels and temporal patterns of expression were often involved in similar functions or cellular processes.
Social-optimized win-win resource allocation for Self-organizing Cloud

With the increasing scale of applications and the number of users, we design a Self-organizing Cloud (SoC) which aims to make use of the distributed volunteer computers or dedicated machines to provide powerful computing ability. These resources are provisioned elastically according to user's specific demand, by leveraging virtual machine (VM) resource isolation technology. Based on such a framework, we propose a social-optimized auction-based resource allocation scheme, which mainly tackles two issues: (1) how to make full use of the widely dispersed multi-attribute idle resources to construct a win-win situation, such that each task schedule could let both sides (resource providers and consumers) be satisfied with their final payoffs. (2) The total resource utility welfare should also be optimized to guarantee the overall performance around the global system. The key challenge of getting the win-win effect with social optimization is its provable NP-completeness. Finally, we validate that our approach can effectively improve the resource contributor's payoffs up to about five times as the level without our method via simulation work. Meanwhile, our approach can also keep a high level of the processing rate for the task scheduling.
Mining the blogosphere to generate local cuisine hotspots for mobile map service

On-the-go consumers require dynamic information, particularly "word of mouth, " to make better purchase decisions. A popular genre of mobile map services is travel/cuisine, which is a popular topic for bloggers as well. This study attempts to generate local cuisine hotspot maps through blog content mining. The main obstacle in doing this involves recognizing and extracting restaurants and essential restaurant information (i.e., restaurant dishes) in unstructured content. In contrast to traditional Named Entity Recognition (NER) targets, dish name is a promising target that received little attention in previous studies. This study develops methods for recognizing and extracting restaurant names and dish names from Chinese blog posts and achieves satisfactory performance. The extraction results are arranged into hotspots and presented in map views. The extracted information can be fed back to POI (Point of Interest) databases, and thus a brand-new POI database comprising information extracted from User Generated Content (UGC) can be realized.
Effortful Control as a Moderator of Reactive Temperamental Risk for Anxiety and Depression: A Short-Term Prospective Study

The purpose of this study was to examine prospectively a model of risk for anxiety and depression based on reactive and effortful aspects of temperament. Negative affectivity (NA), positive affectivity (PA), attentional control (AC), and anxious and depressive symptoms of 210 participants were monitored at 3 points across an 8-week period. Specifically, Time 1 (T1) AC was predicted to significantly moderate the association of T1 NA with Time 3 (T3) anxious and depressive symptoms when controlling for such symptoms at T1. Additionally T1 AC was predicted to significantly moderate the association of PA with T3 depressive symptoms when controlling for such symptoms at T1. Contrary to predictions, an NA X AC interaction was not found for anxious symptoms. T1 NA was a significant predictor of T3 anxiety regardless of level of AC. Results for depression were more consistent with expectations. However, rather than NA X AC and PA X AC interactions, results revealed a significant NA X PA X AC interaction. Examination of this interaction revealed that AC only significantly moderated the association between NA and depression when PA was high. At lower levels of PA, AC did not moderate the risk for depression associated with heightened NA. Limitations and directions for future research are discussed.
Dysarthria and Quality of Life in neurologically healthy elderly and patients with Parkinson's disease.

PURPOSE To compare the speech and voice of Parkinson's disease (PD) patients and neurologically healthy elderly adults (control group, CG), to find out whether these features are related to the disease or the normal aging process, and investigate the impact that dysarthria has on the Quality of Life (QoL) of these individuals. METHODS This is a cross-sectional study involving 25 individuals, 13 patients with PD and 12 CG. All the participants underwent vocal assessment, perceptual and acoustic analysis, based on "Dysarthria Assessment Protocol" and analysis of QoL using a questionnaire, "Living with Dysarthria". The data underwent statistical analysis to compare the groups in each parameter. RESULTS In the assessment of dysarthria, patients with PD showed differences in prosody parameter (p=0.012), at the habitual frequency for females (p=0.025) and males (p=0.028), and the extent of intensity (p=0.039) when compared to CG. In QoL questionnaire, it was observed that patients with PD showed more negative impact on the QoL compared to CG, as indicated by the total score (p=0.005) with various aspects influencing this result. CONCLUSION The degree of modification of speech and voice of patients with PD resembles those seen in normal aging process, with the exception of prosody and the habitual frequency, which are related to the greatest negative impact on the QoL of patients with PD.
Shortcut-JFS: A write efficient journaling file system for phase change memory

Journaling file systems are widely used in modern computer systems as it provides high reliability with reasonable performance. However, existing journaling file systems are not efficient for emerging PCM (Phase Change Memory) storage. Specifically, a large amount of write operations performed by journaling incur serious performance degradation of PCM storage as it has long write latency. In this paper, we present a new journaling file system for PCM, called Shortcut-JFS, that reduces write amount of journaling by more than a half exploiting the byte-accessibility of PCM. Specifically, Shortcut-JFS performs two novel schemes, 1) differential logging that performs journaling only for modified bytes and 2) in-place checkpointing that removes unnecessary block copy overhead. We implemented Shortcut-JFS on Linux 2.6, and measured the performance of Shortcut-JFS and legacy journaling schemes used in ext 3. The results show that the performance improvement of Shortcut-JFS against ext 3 is 40% on average.
UniMelb_NLP-CORE: Integrating predictions from multiple domains and feature sets for estimating semantic textual similarity

In this paper we present our systems for calculating the degree of semantic similarity between two texts that we submitted to the Semantic Textual Similarity task at SemEval2013. Our systems predict similarity using a regression over features based on the following sources of information: string similarity, topic distributions of the texts based on latent Dirichlet allocation, and similarity between the documents returned by an information retrieval engine when the target texts are used as queries. We also explore methods for integrating predictions using different training datasets and feature sets. Our best system was ranked 17th out of 89 participating systems. In our post-task analysis, we identify simple changes to our system that further improve our results.