Miracle-dz/UniFAR-FTUs huggingface dataset:
The Efficacy of GlOSS for the Text Database Discovery Problem

The popularity of information retrieval has led users to a new problem: finding which text databases (out of thousands of candidate choices) are the most relevant to a user. Answering a given query with a list of relevant databases is the text database discovery problem. The first part of this paper presents a practical method for attacking this problem based on estimating the result size of a query and a database. The method is termed GlOSS--Glossary of Servers Server. The second part of this paper evaluates GlOSS using four different semantics to answer a user''s queries. Real users'' queries were used in the experiments. We also describe several variations of GlOSS and compare their efficacy. In addition, we analyze the storage cost of our approach to the problem.
Omega-3 Fatty Acid Intake of Pregnant Women and Women of Childbearing Age in the United States: Potential for Deficiency?

Omega-3 fatty acids play critical roles during fetal growth and development with increased intakes associated with improved maternal-fetal outcomes. Omega-3 fatty acid intake in Western diets is low, and the impact of socioeconomic factors on omega-3 fatty acid intake in pregnant women and women of childbearing age has not been reported. We used the National Health and Nutrition Examination Survey (NHANES) cycles 2003–2012 to assess the relationship between omega-3 fatty acid intake and socioeconomic factors in women of childbearing age. Out of 7266 eligible participants, 6478 were women of childbearing age, while 788 were identified as pregnant at the time of the survey. Mean EPA+DHA intake of the population was 89.0 mg with no significant difference between pregnant and non-pregnant women. By univariate and multivariate analyses adjusting for confounders, omega-3 fatty acid intake was significantly associated with poverty-to-income ratio, race, and educational attainment. Our results demonstrate that omega-3 fatty acid intake is a concern in pregnant women and women of childbearing age in the United States, and that socioeconomically disadvantaged populations are more susceptible to potential deficiencies. Strategies to increase omega-3 fatty acid intake in these populations could have the potential to improve maternal and infant health outcomes.
Rescue of a severely bottlenecked wolf (Canis lupus) population by a single immigrant

The fragmentation of populations is an increasingly important problem in the conservation of endangered species. Under these conditions, rare migration events may have important effects for the rescue of small and inbred populations. However, the relevance of such migration events to genetically depauperate natural populations is not supported by empirical data. We show here that the genetic diversity of the severely bottlenecked and geographically isolated Scandinavian population of grey wolves (Canis lupus), founded by only two individuals, was recovered by the arrival of a single immigrant. Before the arrival of this immigrant, for several generations the population comprised only a single breeding pack, necessarily involving matings between close relatives and resulting in a subsequent decline in individual heterozygosity. With the arrival of just a single immigrant, there is evidence of increased heterozygosity, significant outbreeding (inbreeding avoidance), a rapid spread of new alleles and exponential population growth. Our results imply that even rare interpopulation migration can lead to the rescue and recovery of isolated and endangered natural populations.
Using Ant's Colony Algorithm for improved segmentation for number plate recognition

In this paper a number plate recognition system has been designed using the ant colony optimization technique. This system can be implemented in surveillance systems, detection of stolen vehicles and checking of vehicles at toll plazas, posts, barriers sand other entry points. In this paper, an ant colony based number plate extraction method is proposed. Ant colony optimization technique serves better results in edge detection while applying image segmentation, so using the concept in number plate recognition promises better accuracy. The Ant colony optimization (ACO) is an optimization algorithm inspired by the natural behavior of ant species that ants deposit pheromone on the ground for foraging. ACO is introduced to give a better image edge detection. The proposed ACO-based edge detection approach is able to establish a pheromone matrix that represents the edge information presented at each pixel position of the image, according to the movements of a number of ants which are dispatched to move on the image. Furthermore, the movements of these ants are driven by the local variation of the image's intensity values. Eventually, this gives the number plate area extracted from the image with improved accuracy. Finally a character recognition model is used to give out the final vehicle license number.
HIGHLY EFFICIENT SURFACE NORMAL INTEGRATION

The integration of surface normals for the computation of a surface in 3D space is a classic problem in computer vision. However, even nowadays it is still a challenging task to device a method that combines the flexibility to deal with non-trivial computational domains with high accuracy, robustness and computational efficiency. In this paper we propose to use for the first time in the literature Krylov subspace solvers as a main step in tackling the task. While these methods can be very efficient, they may only show their full potential when combined with a numerical preconditioning and even more importantly, a suitable initialization. To address the latter issue we propose to compute this initial state via a recently developed fast marching integrator. Numerical experiments prove the benefits of this novel combination.
Input Image : Smile Intensity Generated Responses : Input Question : Input

Computer-based conversational agents are becoming ubiquitous. However, for these systems to be engaging and valuable to the user, they must be able to express emotion, in addition to providing informative responses. Humans rely on much more than language during conversations; visual information is key to providing context. We present the first example of an image-grounded conversational agent using visual sentiment, facial expression and scene features. We show that key qualities of the generated dialogue can be manipulated by the features used for training the agent. We evaluate our model on a large and very challenging real-world dataset of conversations from social media (Twitter). The image-grounding leads to significantly more informative, emotional and specific responses, and the exact qualities can be tuned depending on the image features used. Furthermore, our model improves the objective quality of dialogue responses when evaluated on standard natural language metrics. ACM Classification
Longitudinal course of behavioral problems during Alzheimer's disease: linear versus curvilinear patterns of decline.

BACKGROUND Patients with Alzheimer's Disease (AD) are commonly assumed to experience a linear decline in behavioral functioning that parallels progressive cognitive decline. However, some researchers have suggested that specific behavioral problems either decline at different rates or improve in late dementia. METHODS The present analyses examined 150 AD patients at an initial assessment, 61 of whom were also evaluated annually on two additional occasions. Measures of cognitive impairment and behavioral problems were obtained. RESULTS Cross-sectional results indicated curvilinear associations between dementia severity and certain behavioral problems (forgetful behaviors, and emotional and impulsive behaviors). Longitudinal analyses further indicated trends for curvilinear rates of behavioral disturbance across time, with some problem areas showing improvement as AD progresses through the most severe stages. CONCLUSIONS Even though Alzheimer's disease is a progressive dementia characterized by increasing cognitive deterioration, it appears to be inaccurate to expect behavioral functioning to show the same linear decline across time.
Planning under uncertainty for extended reachability goals

Planning under uncertainty has being increasingly demanded for practical applications in several areas that require reliable solutions for complex goals and, therefore, some approaches for automatic plan synthesis based on formal methods have been proposed in AI Planning. Among these approaches, planning based on model checking seems to be a very attractive one, however, it is mainly based on ctl and deals only with simple reachability goals, as those in classical planning. In this paper, we introduce a more expressive class of planning goals ( extended reachability goals ) and show that, for this class of goals, the ctl ’s semantics is not adequate to formalize plan synthesis (or validation) algorithms. Then, as a way to overcome this limitation, we propose a new variant of ctl , based on which we implement plan synthesis (and validation) algorithms capable of solving problems with extended reachability goals, in totally observable nondeterministic environments
The sparsity and bias of the Lasso selection in high-dimensional linear regression

Meinshausen and Buhlmann [Ann. Statist. 34 (2006) 1436-1462] showed that, for neighborhood selection in Gaussian graphical models, under a neighborhood stability condition, the LASSO is consistent, even when the number of variables is of greater order than the sample size. Zhao and Yu [(2006) J. Machine Learning Research 7 2541-2567] formalized the neighborhood stability condition in the context of linear regression as a strong irrepresentable condition. That paper showed that under this condition, the LASSO selects exactly the set of nonzero regression coefficients, provided that these coefficients are bounded away from zero at a certain rate. In this paper, the regression coefficients outside an ideal model are assumed to be small, but not necessarily zero. Under a sparse Riesz condition on the correlation of design variables, we prove that the LASSO selects a model of the correct order of dimensionality, controls the bias of the selected model at a level determined by the contributions of small regression coefficients and threshold bias, and selects all coefficients of greater order than the bias of the selected model. Moreover, as a consequence of this rate consistency of the LASSO in model selection, it is proved that the sum of error squares for the mean response and the l α -loss for the regression coefficients converge at the best possible rates under the given conditions. An interesting aspect of our results is that the logarithm of the number of variables can be of the same order as the sample size for certain random dependent designs.
Identification of Personal Name Aliases on the Web

Extracting aliases of an entity is important for various tasks such as identiflcation of relations among entities, web search and entity disambiguation. To extract relations among entities properly, one must flrst identify those entities. We propose a novel approach to flnd aliases of a given name using automatically extracted lexical patterns. We exploit a set of known names and their aliases as training data and extract lexical patterns that convey information related to aliases of names from text snippets returned by a web search engine. The patterns are then used to flnd candidate aliases of a given name. We use anchor texts to design a word cooccurrence model and use it to deflne various ranking scores to measure the association between a name and a candidate alias. The ranking scores are integrated with page-countbased association measures using support vector machines to leverage a robust alias detection method. The proposed method outperforms numerous baselines and previous work on alias extraction on a dataset of personal names, achieving a statistically signiflcant mean reciprocal rank of 0:6718. Experiments carried out using a dataset of location names and Japanese personal names suggest the possibility of extending the proposed method to extract aliases for difierent types of named entities and for other languages. Moreover, the aliases extracted using the proposed method improve recall by 20% in a relation-detection task.
TIMED DISTRIBUTED SYSTEMS

This project seeks models of computation, software techniques, and analytical models for distributed timed systems. Such systems coordinate components on a network, and timeliness matters. Applications include industrial automation, instrumentation systems, and networked embedded software systems. The introduction of network time synchronization such as IEEE 1588 makes possible time coherence that has not traditionally been part of the computational models. Given time synchronization with some known precision, we believe that distributed applications should be designed and developed differently, and that time synchronization can help with robust coordination of unreliable components. Existing methods such as real-time operating systems, time-triggered networks, and network time synchronization deal with parts of the problem. These technologies, however, are used with relatively conventional concurrency models (threads and processes). This project seeks to elevate timing and distribution to the level of the programmers model, so that applications are built by directly expressing timing and distribution properties. The objective is a framework for designing deployable timed distributed systems. Here we report on the outcomes during the first two years of the project.
Fair sampling across network flow measurements

Sampling is crucial for controlling resource consumption by internet traffic flow measurements. Routers use Packet Sampled NetFlow, and completed flow records are sampled in the measurement infrastructure. Recent research, motivated by the need of service providers to accurately measure both small and large traffic subpopulations, has focused on distributing a packet sampling budget amongst subpopulations. But long timescales of hardware development and lower bandwidth costs motivate post-measurement analysis of complete flow records at collectors instead. Sampling in collector databases then manages data volumes, yielding general purpose summaries that are rapidly queried to trigger drill-down analysis on a time limited window of full data. These are sufficiently small to be archived. This paper addresses the problem of distributing a sampling budget over subpopulations of flow records. Estimation accuracy goals are met by fairly sharing the budget. We establish a correspondence between the type of accuracy goal, and the flavor of fair sharing used. A streaming Max-Min Fair Sampling algorithm fairly shares the sampling budget across subpopulations, with sampling as a mechanism to deallocate budget. This provides timely samples and is robust against uncertainties in configuration and demand. We illustrate using flow records from an access router of a large ISP, where rates over interface traffic subpopulations vary over several orders of magnitude. We detail an implementation whose computational cost is no worse than subpopulation-oblivious sampling.
Enhanced Chondrogenic Differentiation of Human Umbilical Cord Wharton's Jelly Derived Mesenchymal Stem Cells by GSK-3 Inhibitors

Articular cartilage is an avascular, alymphatic, and aneural system with very low regeneration potential because of its limited capacity for self-repair. Mesenchymal stem cells (MSCs) are the preferred choice for cell-based therapies. Glycogen synthase kinase 3 (GSK-3) inhibitors are compounds that can induce the Wnt signaling pathway, which is involved in chondrogenesis and cartilage development. Here, we investigated the influence of lithium chloride (LiCl) and SB216763 synergistically with TGF-β3 on chondrogenic differentiation in human mesenchymal stem cells derived from Wharton’s jelly tissue (hWJ-MSCs). hWJ-MSCs were cultured and chondrogenic differentiation was induced in monolayer and pellet experiments using chondrogenic medium, chondrogenic medium supplemented with LiCl, or SB216763 for 4 weeks. After in vitro differentiation, cultured cells were examined for the expression of Sox9, ACAN, Col2a1, and β-catenin markers. Glycosaminoglycan (GAG) accumulation was also examined by Alcian blue staining. The results indicated that SB216763 was more effective than LiCl as evidenced by a higher up-regulation of the expression of cartilage-specific markers, including Sox9, ACAN, Col2a1 as well as GAG accumulation. Moreover, collagen type II expression was strongly observed in cells cultured in the chondrogenic medium + SB216763 as evidenced by western blot analysis. Both treatments appeared to mediate the Wnt signaling pathway by up-regulating β-catenin gene expression. Further analyses showed that all treatments suppressed the progression of chondrocyte hypertrophy, determined by decreased expression of Col10a1 and Runx2. These results indicate that LiCl and SB216763 are potential candidates for further in vivo therapeutic trials and would be of great importance for cartilage regeneration.
Packet combining in sensor networks

This paper presents the Simple Packet Combining (SPaC) error-correction scheme for wireless sensor networks. Nodes buffer corrupt packets, and when two or more corrupt versions of a packet have been received, a packet combining procedure attempts to recover the original packet from the corrupt copies. Packet combining exploits the broadcast medium and spatial diversity of a multi-hop wireless network by using packets overheard at any node, in addition to the next-hop destination of the packet itself. Unlike point-to-point forward error correction (FEC), packet combining therefore helps multi-node interactions such as multi-hop routing or broadcasting as well as to hop-by-hop communication. Also, SPaC does not transmit redundant overhead on good links and does not require costly probes to estimate channel conditions.We have implemented SPaC as a link-layer extension on sensor nodes; it is transparent to upper layer protocols and has low memory and CPU footprints. We evaluate performance through a combination of analysis, trace-driven simulation, indoor and outdoor testbed micro-benchmarks, and deployment on a live network. The results show significant performance gains, even when accounting for the energy cost of CPU processing. We also present detailed bit-level link measurements and the design and evaluation of a new preamble detection scheme motivated by these measurements.
An EEG-Based BCI Platform to Improve Arm Reaching Ability of Chronic Stroke Patients by Means of an Operant Learning Training with a Contingent Force Feedback

The Brain Computer Interface platform described in this paper was implemented to enhance neuroplasticity of a stroke-damaged brain in order to promote recovery of motor functions like reaching, fundamentally important in a healthy daily life. To this scope a closed-loop between the stroke patients' brain and a robotic arm is established by means of a real-time identification of the cerebral activity related to the movement and its transformation in a force feedback delivered by the robot. In particular, an operant-learning strategy is employed: while patients are performing the motor task they receive a feedback of their neural activity. If the latter agrees with the expected neurophysiological hypothesis, they are helped by the robotic arm in completing the task. The method trains patients to control the modulation of sensorimotor rhythms of their perilesional area and, at the same time, it should induce them to associate that modulation to the reaching movement. In this way, the modification of the neural activity becomes an alternative tool for controlling the impaired reaching ability bypassing the damaged brain area. Preliminary encouraging results were found in both the two first patients recruited in the program.
Information flow in entangled quantum systems

All information in quantum systems is, notwithstanding Bell's theorem, localized. Measuring or otherwise interacting with a quantum system S has no effect on distant systems from which S is dynamically isolated, even if they are entangled with S. Using the Heisenberg picture to analyse quantum information processing makes this locality explicit, and reveals that under some circumstances (in particular, in Einstein–Podolsky–Rosen experiments and in quantum teleportation), quantum information is transmitted through ‘classical’ (i.e. decoherent) information channels.
Microprocessor Modeling and Simulation with SystemC

Complexity of advanced chip designs is driving the progress of ESL methodology. SystemC, with its mature C++ language environment and the availability of public tools, is quickly becoming the de facto ESL language. In this paper, we demonstrate the methodology by microprocessor modeling with systemC. Various abstraction levels and the corresponding purposes are addressed. The advantage of ESL methodology is shown by the experimental results of simulation speed. With a 18 times to over 500 times simulation speed-up, the methodology has proved useful in modeling, verification, and software development.
Two-year observation of artificial intervertebral disc replacement: results after supplemental ultra-high strength bioresorbable spinal stabilization.

OBJECT This 2-year experimental study was conducted to investigate the efficacy of a bioactive three-dimensional (3D) fabric disc for lumbar intervertebral disc replacement. The authors used a bioresorbable spinal fixation rod consisting of a forged composite of particulate unsintered hydroxyapatite/poly-L-lactide acid (HA/PLLA) for stability augmentation. The biomechanical and histological alterations as well as possible device-related loosening were examined at 2 years postoperatively. METHODS Two lumbar intervertebral discs (L2-3 and L4-5) were replaced with the 3D fabric discs, which were augmented by two titanium screws and a spanning bioresorbable rod (HA/PLLA). The segmental biomechanics and interface bone ingrowth were investigated at 6, 15, and 24 months postoperatively, and results were compared with the other two surgical groups (3D fabric disc alone; 3D fabric disc with additional anterior instrumentation stabilization). The 3D fabric disc and HA/PLLA-spinal segments demonstrated segmental mobility at 15 and 24 months; however, the range of motion (ROM) in flexion-extension decreased to 49 and 40%, respectively, despite statistically equivalent preserved torsional ROM. Histologically there was excellent osseous fusion at the 3D fabric disc surface-vertebral body interface. At 2 years posttreatment, no adverse tissue reaction nor aseptic loosening of the device was observed. CONCLUSIONS Intervertebral disc replacement with the 3D fabric disc was viable and when used in conjunction with the bioresorbable HA/PLLA spinal augmentation. Further refinements of device design to create a stand-alone type are necessary to obviate the need for additional spinal stabilization.