Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification of lung cancer stages on CT scan images using image processing

Lung cancer is the uncontrolled growth of abnormal cells that starts off in one or both images. People who smoke have the greatest risk of lung cancer. The overall 5 year survival rate for lung cancer combining all stages is roughly 15%. Early detection of lung cancer can increase the chance of survival among people. Lung cancer may be found by imaging tests such as chest computed tomography scan as it provides more detailed picture. To classify the stages of lung cancer, image processing technique is developed. In this work, new algorithm is developed using image processing technique to detect the cancer at early stage with more accuracy. Image processing involves the pre-processing, feature extraction and finally classification steps.
Non-linear Complex-Valued Extensions of Hebbian Learning : An Essay Simone

The Hebbian paradigm is perhaps the most known unsupervised learning theory in connectionism. It has inspired a wide research activity in the artificial neural network field because it embodies some interesting properties such as locality and the capability of being applicable to the basic weight-and-sum structure of neuron models. The plain Hebbian principle, however, also presents some inherent theoretical limitations that make it unpractical in most cases. Therefore, modifications of the basic Hebbian learning paradigm have been proposed over the last twenty years in order to design profitable signal/data processing algorithms. Such modifications led to the principal-component-analysis-type class of learning rules along with their non-linear extensions. The aim of this essay is primarily to present part of the existing fragmented material in the field of principal component learning within a unified view and contextually to motivate and present extensions of previous works on Hebbian learning to complex-weighted linear neural networks. This work benefits from previous studies on linear signal decomposition by artificial neural networks, non-quadratic component optimization and reconstruction error definition, neural parameters adaptation by constrained optimization of complex-valued learning criteria and orthonormality expression via the insertion of topological elements in the networks or by modifying the network learning criterion. In particular, the considered learning principles and their analysis concern complex-valued principal/minor component/subspace linear/non-linear rules for complex-weighted neural structures, both feedforward and laterally-connected.
Managerial cognition, emergency preparedness and firm's emergency response performance

Abstract Building on cognitive classification theory, schema theory and accident-causing theory, this article aims to examine the effect of firms’ managerial cognition on emergency preparedness behavior, and their emergency response performance. Data were collected through cross-sectoral survey method on 271 Chinese firms across manufacturing and mining industries. Results of structural equation modeling indicated that cognitive complexity was associated positively with multiple dimensions of emergency preparedness (information, resource and system preparedness). Cognitive focus was associated negatively with emergency preparedness. We also found information, resource and management system-wide preparedness can improve emergency response performance significantly. Based on the findings, this paper gives practical implications for firms to prevent and respond to emergencies. Integrating and articulating diverse information and knowledge sources, as well as direct and/or vicarious experience, reduce the likelihood of unexpected emergencies unplanned responses.
Perception of speaker age and speaker origin in a sound change in progress: The case of /s/-aspiration in Andalusian Spanish

This paper addresses the question whether listeners possess sociolinguistic knowledge of regional variation in a sound change affecting /s/ + voiceless stop sequences in Andalusian Spanish. We tested whether speakers from Seville and Granada were perceived as more Sevillian-sounding and as younger when a stimulus contained the novel phonetic variant, a post-aspirated stop. Word-medial syllable-final /s/ was manipulated in such a way that two stimuli of the same speaker differed only in whether they contained pre-aspiration ([eh.ˈtaŋ.ko]) or post-aspiration ([e.ˈthaŋ.ko]). Andalusian listeners rated the same speaker as younger and as more Sevillian-sounding when the stimulus contained a post-aspirated stop. The recognition of a speaker’s actual dialect was particularly high when the innovative variant was embedded in a Seville speaker’s speech, confirming earlier work that indexicality is context-specific. The results suggest that Andalusian listeners possess knowledge of the regional variation and the sound change and use this variation in social perception.
Physics Career Education Day: Design, Implementation, and Assessment

The nation faces critical shortages of Hispanic science, technology, engineering, and mathematics (STEM) college graduates—especially in physics. To address youth lack of awareness about physics careers, physics educators at the University of Texas Rio Grande Valley implemented a strategic intervention anchored in Modern Expectancy-Value Theory, Physics Career Education Day, in collaboration with two local school districts. Presurvey and postsurvey results have shown that this intervention significantly increased student awareness and interest in physics careers.
Global Tracking Control of one Degree of FreedomEuler-Lagrange Systems Without Velocity

The problem of global output feedback tracking control of robot manipulators has been open for several years. In this short note we propose a computed torque plus (nonlinear) PD like controller to solve the output feedback tracking control problem of one degree of freedom (dof) Euler-Lagrange (EL) systems. We prove in this case global asymptotic stability. Our approach is the extension of our previous semi-global result (Loria and Ortega, 1995). Even though we can not claim the same result for systems of more than one dof, as far as we know, none of the semi-global solutions present in the literature has been proved to be global even for 1 dof systems. 1. INTRODUCTION The problem of global output feedback 1 tracking control of robot manipulators has been open for several years and has attracted the interest of several researchers in this eld. Among the rst attempts, (Nicosia et al., 1990) proposed a high gain linear observer, while (Canudas de Wit and J.J. Slotine, 1991) used the sliding mode approach. However, it is well known that an observer that asymptotically reconstructs the estimated signal does not guarantee that a given stabilizing state-feedback controller will remain stable when using the estimated state instead of the true one.
ON THE NONLINEAR MAPPING OF A SURFACE GRAVITY WAVE SPECTRUM INTO A SAR IMAGE SPECTRUM AND FREAK WAVE DETECTION

It is shown that the nonlinear mapping relation between a surface gravity wave spectrum and its SAR image spectrum, as derived by Hasselmann and Hasselmann [1], is not complete. The reason for this is that the velocity bunching effect is so nonlinear that effects of skewness and kurtosis are as important as the nonlinear terms already retained in the Hasselmann and Hasselmann approach. An analysis of the azimuthal cutoff may therefore reveal information on the non-gaussian statistics of the sea surface, and may indicate the enhanced occurrence of freak waves.
A comparison of men’s and women’s perceptions of the female body using a multidimensional scaling analysis of naturalistic stimuli

Men and women worked with 25 naturalistic photos of females representing varied physiques. Similarity judgments of the photos were analyzed using multidimensional scaling analysis to produce composite maps for male and female participants. A comparison of the maps showed gender similarities and differences. Both genders used almost identical attributes in judging similarities and identified almost identical body types, but men were more inclusive in identifying ideal females; men included curvaceous females that were rejected by women. Women identified very thin females that were rejected by men. Men were affectively most positive toward female ideals; women were most positive to near-ideals.
Adapting Multidimensional Schemes to Data sources using Algebraic Operators

Designing a decisional system requires a methodology different from those commonly adopted for operational information systems. In our methodology data marts are constructed on the basis of user requirements specified using OLAP design patterns. Since these patterns are independent of any data source, the data mart design process should solve the problems due to differences between user OLAP requirements, from one hand, and data source from the other hand. This paper, first defines multidimensional models. Secondly, it defines a set of operators for the validation and the refinement of patterns, covering the addition and deletion of: hierarchy, dimension, dimension attribute, Non dimension attribute, measure and fact.
Biphasic current driver circuit A Light Amplitude Modulated Neural Stimulator Design with Photodiode for Visual Prostheses

Abstract The neural stimulators have been employed to the visual prostheses system based on the functional electrical stimulation (FES). Due to the size limitation of the implantable device, the smaller area of the stimulator IC is highly desired. This paper presents a neural stimulator with photodiode for visual prostheses. The proposed stimulator provides the light amplitude modulated stimulation current using photodiode. In conventional concept of visual prostheses, the image information is captured by external camera, and the converted electronic signal is sent to the implanted chip, therefore, the digital analog converter (DAC) is required in the implanted chip. The proposed stimulation scheme with onchip photodiodes is expected to be fully implantable and to have smaller size than previous external camera system
Participatory bluetooth sensing: A method for acquiring spatio-temporal data about participant mobility and interactions at large scale events

Acquisition of data to capture human mobility and interactions during large-scale events is a challenging task. In this paper we discuss a mobile sensing method for mapping the mobility of crowds at large scale events using a participatory Bluetooth sensing approach. This non-invasive technique for collecting spatio-temporal data about participant mobility and social interactions uses the capabilities of Bluetooth capable smartphones carried by participants. As a proof-of-concept we present a field study with deployment of the method in a large music festival with 130000 participants where a small subset of participants installed Bluetooth sensing apps on their personal smartphones. Our software module uses location and Bluetooth scans to utilize smartphones as provisional scanners that are present with higher frequency in regions with high density of participants. We discuss the initial results obtained and outline opportunities and challenges introduced by this methodology along with opportunities for future pervasive systems and applications.
A Novel Method for 3D Surface Mesh Segmentation

We propose a novel method for the problem of 3D surface mesh segmentation. This mesh segmentation method is based on differential geometry and geodesic distance information. In our algorithm, vertices of the same group of surface types are allowed to join a segment. The geodesic distance is estimated using shortest path between connected vertices, which can be computed from well known algorithms such as the Dijkstra algorithm. However, this is inefficient for large meshes. Therefore, in our algorithm, instead of applying the shortest path algorithm to the entire mesh, we locally apply it to mesh partitions so that we can avoid large computational costs. We tested our method with 3D surface meshes of synthetic objects and molecular structures.
Thresholded Basis Pursuit: Support Recovery for Sparse and Approximately Sparse Signals

In this paper we present a linear programming solution for support recovery. Support recovery involves the estimation of sign pattern of a sparse signal from a set of randomly projected noisy measurements. Our solution of the problem amounts to solving min ‖Z‖1 s.t. Y = GZ, and quantizing/thresholding the resulting solution Z. We show that this scheme is guaranteed to perfectly reconstruct a discrete signal or control the element-wise reconstruction error for a continuous signal for specific values of sparsity. We show that the sign pattern of X can be recovered with SNR = O(log n) and m = O(k log n/k) measurements, where k is the sparsity level and satisfies 0 < k ≤ αn, where, α is some non-zero constant. Our proof technique is based on perturbation of the noiseless l1 problem. Consequently, the maximum achievable sparsity level in the noisy problem is comparable to that of the noiseless problem. Our result offers a sharp characterization in that neither the SNR nor the sparsity ratio can be significantly improved. In contrast previous results based on LASSO and MAX-Correlation techniques either assume significantly larger SNR or sub-linear sparsity. Our results has implications for approximately sparse problems. We show that the k largest coefficients of a non-sparse signal X can be recovered from m = O(k log n/k) random projections for certain classes of signals.
Interactive Learning from Demonstration with a Multilevel Mechanism for Collision-Free Navigation in Dynamic Environments

While collision-free navigation could be accomplished using existing rule-based approaches, it would be more attractive to use learning from demonstration (LfD) approaches to ease the burden of tedious rule designing and parameter tuning procedures. To increase the performance interactively, the basic LfD method is further combined with the interactive learning approach in this paper. However, instead of retraining the control policy using all available data, we propose an interactive learning approach with a multilevel mechanism to learn the control policy interactively for accomplishing collision-free navigation in dynamic scenes. Compare with the approach in which all the available data are used for retraining, not only the collision rate decreases, the training time also decreases since the top level policy is not retrained and the training time for the additional level is much less than retraining the whole policy. The results show that collision-free navigation in simulated dynamic environments is learnable from interactive demonstrations.
Context-based Word Acquisition for Situated Dialogue in a Virtual World

To tackle the vocabulary problem in conversational systems, previous work has applied unsupervised learning approaches on co-occurring speech and eye gaze during interaction to automatically acquire new words. Although these approaches have shown promise, several issues related to human language behavior and human-machine conversation have not been addressed. First, psycholinguistic studies have shown certain temporal regularities between human eye movement and language production. While these regularities can potentially guide the acquisition process, they have not been incorporated in the previous unsupervised approaches. Second, conversational systems generally have an existing knowledge base about the domain and vocabulary. While the existing knowledge can potentially help bootstrap and constrain the acquired new words, it has not been incorporated in the previous models. Third, eye gaze could serve different functions in human-machine conversation. Some gaze streams may not be closely coupled with speech stream, and thus are potentially detrimental to word acquisition. Automated recognition of closely-coupled speech-gaze streams based on conversation context is important. To address these issues, we developed new approaches that incorporate user language behavior, domain knowledge, and conversation context in word acquisition. We evaluated these approaches in the context of situated dialogue in a virtual world. Our experimental results have shown that incorporating the above three types of contextual information significantly improves word acquisition performance.
Adapting Spoken Dialog Systems Towards Domains and Users

Spoken dialog systems have been widely used, such as the voice applications or agents in smart phone or smart car environments. However, speech systems are built using the developers’ understanding of the application domain and of potential users in the field, driven by observations collected by sampling the population at a given time. Therefore, the deployed models may not perfectly fit the real-life usage or may no longer be valid with the changing dynamics of the domain/users over time. A system which automatically adapts to the domain and users is naturally desired. In this thesis, we focus on three realistic problems in language-based communication between human and machine. First, current speech systems with fixed vocabulary have difficulty understanding out-of-vocabulary words (OOVs), leading to misunderstanding or even task failures. Our approaches can learn new words during the conversation or even before the conversation by detecting the presence of OOVs or anticipating the new words ahead of time. Our experiments show that OOV-related recognition and understanding errors can be therefore prevented. Second, cloudbased automatic speech recognition (cloud-ASR) is widely used by current dialog applications. The problem though is that it lacks the flexibility to adapt to domains or users. Our method, which combines hypotheses from 1) a local and adaptive ASR and 2) the cloud-ASR, can provide better recognition accuracy. Third, when interacting with a dialog system, users’ intention may go beyond individual domains hosted by the system. However, current multi-domain dialog systems do not have the awareness of the user’s high-level intentions, resulting in lost opportunities to assist the user in a timely manner or personalize the interaction experience. We built models to recognize the complex user intentions and enable the system to communicate with the user at the task level, in addition to the individual domain level. We believe that adaptation in these three levels can contribute to the quality of human-machine interactions.
A modified differential evolution for autonomous deployment and localization of sensor nodes

The performance of a wireless sensor network (WSN) is largely influenced by the optimal deployment and accurate localization of sensor nodes. This article considers real-time autonomous deployment of sensor nodes from an unmanned aerial vehicle (UAV). This kind of deployment has importance, particularly in ad hoc WSNs, for emergency applications, such as disaster monitoring and battlefield surveillance. The objective is to deploy the nodes only in the terrains of interest, which are identified by segmentation of the images captured by a camera on board the UAV. In this article we propose an improved variant of an important evolutionary algorithm Differential Evolution for image segmentation and for distributed localization of the deployed nodes. Simulation results show that the proposed algorithm ADE_pBX performs image segmentation faster than both types of algorithm for optimal thresholds. Moreover in case of localization it gives more accurate results than the compared algorithms.
Collisional and molecular spectroscopy in an ultracold Bose–Bose mixture

The route towards a Bose–Einstein condensate (BEC) of dipolar molecules requires the ability to efficiently associate dimers of different chemical species and transfer them to the stable rovibrational ground state. Here, we report on recent spectroscopic measurements of two weakly bound molecular levels and newly observed narrow d-wave Feshbach resonances. The data are used to improve the collisional model for the Bose–Bose mixture 41K87Rb, one of the most promising candidates to create a molecular dipolar BEC.
Link repair in managed multi‐domain connections with end‐to‐end quality guarantees

The Internet is a platform providing connection channels for various services. Whereas for services like email the best‐effort nature of the Internet can be considered sufficient, other services strongly depend on service‐specific connection quality parameters. This quality dependence has led to dedicated content distribution networks as a workaround solution for services like YouTube. Such workarounds are applicable to a small number of services only. With the global application of the Internet, the impact of quality of service varies from annoyance due to jitter in VoIP communication to endangering human lives in telemedicine applications. Thus network connections with end‐to‐end quality guarantees are indispensable for various existing and evolving services. In this paper we consider point‐to‐point multi‐domain network connections for which the end‐to‐end quality has to be assured. Our contribution includes the classification of fault cases in general and countermeasures against end‐to‐end performance degradation. By correlating events and reasonable countermeasures, this work provides the foundation for quality assurance during the operation phase of end‐to‐end connections. We put our contribution in the context of a vision of global‐goal‐aware self‐adaptation in computer networks and outline further research areas that require a similar classification to the work provided here. Copyright © 2012 John Wiley & Sons, Ltd.
Implementation of M-CHAT Screening for Autism in Primary Care in Saudi Arabia

Background. Integration of autism screening into primary care practice in Saudi Arabia is not well established. Objectives. To evaluate the feasibility and effectiveness of implementing the Arabic Modified Checklist for Autism in Toddlers (M-CHAT) in a primary care practice at John Hopkins Aramco Healthcare Center in Saudi Arabia. Method. The Arabic version of M-CHAT was distributed to caregivers of 1207 toddlers (16-32 months) from January to December 2014. Feasibility was assessed by measuring the proportion of visits with M-CHAT completed, and reports of workflow challenges and provider satisfaction. The effectiveness of screening was evaluated based on the number of referrals for autism evaluation and autism identification rates. Results. Total M-CHAT completion rate was 89% (1078 out of 1207 child-specific visits). Those identified as low risk (n = 951; 88%) were reassured and followed routinely. Those screening positive (n = 127; 12%) were referred for diagnostic assessment. Twelve (1% of toddlers screened) were diagnosed with autism at a mean age of 24 months. In addition, positive M-CHAT detected speech delay and social anxiety. Providers acknowledged their satisfaction with the M-CHAT implementation process; the main challenge was communicating to families the importance of screening. Referrals for diagnostic evaluations increased from 23 to 43 cases in the first year, and 35 in the second year. Conclusion. Implementation of the autism screening using the Arabic M-CHAT is feasible and effective in a primary care setting in Saudi Arabia. Sustaining the implementation of developmental screening in practice requires staff engagement and systematic monitoring of the impact of change.
ITP-SB-98-72 ANL-HEP-PR-98-140 hep-th/yymmddd Degeneration of ALF Dn Metrics

Beginning with the Legendre transform construction of hyperkähler metrics, we analyze the ALF version of the D n metrics. We determine the constraint equation obtained from extremizing the w coordinate of the generating function F (z, ¯ z, u, ¯ u, w) and study its behavior as we send two of the mass parameters of the D n metric to zero. We find that the constraint equation enforces the limit that the metric becomes that of multi-Taub-NUT.