Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Silicon Racetrack Modulator Employing Coupling Modulation via Active MMI Coupler

In this paper, we propose and analyze the performance of a high-speed racetrack resonator (RTR)-based intensity modulator, which uses coupling coefficient modulation. The proposed modulator is realizable on the standard 220-nm silicon-on-insulator platform. The intensity modulation is achieved by modulating the coupling coefficient between the RTR and the access waveguide using a lateral p-i-n-diode-based active multimode interference (MMI) coupler. The coupling coefficient modulation has been achieved by creating a small perturbation in the refractive index profile inside the active MMI coupler. Theoretical analysis of the proposed modulator using the first-order perturbation theory and time-dependent dynamic model is presented. Analysis and simulation showed that the proposed modulator offers minimum 15.1 dB of static extinction ratio (ER) and <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>5.4 dB of dynamic ER at 32 Gb/s data rate. It has been shown that the modulator can be designed for both 0.8 and 1.6 nm free spectral range and the required driving voltage is <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>1.3 V. Both transient analysis and the small-signal model of the proposed modulator verify the maximum operating speed up to 32 Gb/s. Also, our analysis predicts that the power dissipation for the proposed modulator at 32 Gb/s is approximately 2.97 mW and thus, the energy dissipation per bit is <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>86 fJ.
Recognizing Visibility Graphs of Polygons with Holes and Internal-External Visibility Graphs of Polygons

Visibility graph of a polygon corresponds to its internal diagonals and boundary edges. For each vertex on the boundary of the polygon, we have a vertex in this graph and if two vertices of the polygon see each other there is an edge between their corresponding vertices in the graph. Two vertices of a polygon see each other if and only if their connecting line segment completely lies inside the polygon, and they are externally visible if and only if this line segment completely lies outside the polygon. Recognizing visibility graphs is the problem of deciding whether there is a simple polygon whose visibility graph is isomorphic to a given input graph. This problem is well-known and well-studied, but yet widely open in geometric graphs and computational geometry. Existential Theory of the Reals is the complexity class of problems that can be reduced to the problem of deciding whether there exists a solution to a quantifier-free formula F(X1,X2,.,Xn), involving equalities and inequalities of real polynomials with real variables. The complete problems for this complexity class are called Existential Theory of the Reals Complete. In this paper we show that recognizing visibility graphs of polygons with holes is Existential Theory of the Reals Complete. Moreover, we show that recognizing visibility graphs of simple polygons when we have the internal and external visibility graphs, is also Existential Theory of the Reals Complete.
Domain adaptation by manifold transfer for image classification

There are many progress towards transfer learning tasks where the distribution of two datasets is different. Most of them try to build a classifier from source dataset and then apply it to target dataset to obtain more correct labels for target samples by transferring knowledge between two domains. But they pay no attention to manifold transfer across datasets. In this paper, our goal is to inject the structure of source data to target part and learn a mapping matrix that maps both datasets to a new subspace in which the target manifold follows the properties of source manifold. In the next stage, we learn a metric such that distinct domains get close to each other by decreasing the difference between their means through marginal and conditional adjustment. Then, our algorithm uses marginalized denoising and low-rank property to increase the robustness of trained metric. We evaluate the validity of our algorithm by testing it on 12 cross-domain image datasets and show its ability against other domain adaptation approaches in classification tasks.
Inductive Graph Pattern Learning for Recommender Systems Based on a Graph Neural Network

Most modern successful recommender systems are based on matrix factorization techniques, i.e., learning a latent embedding for each user and each item from the given rating matrix and use the embeddings to complete the matrix. However, these learned latent embeddings are inherently transductive and are not designed to generalize to unseen users/items or new tasks. In this paper, we aim to learn an inductive model for recommender systems based on the local graph patterns around user-item pairs. The inductive model can generalize to unseen nodes/items, and potentially also transfer to other tasks. To learn such a model, we extract a local enclosing subgraph for each training (user, item) pair, and feed the subgraphs to a graph neural network (GNN) to train a rating prediction model. We show that our model achieves highly competitive performance with state-of-the-art transductive methods, and is more stable when the rating matrix is sparse. Furthermore, our transfer learning experiment validates that the learned model is transferrable to new tasks.
Secretome analysis of oral keratinocytes chronically exposed to shisha.

BACKGROUND Shisha smoking has been associated with multiple diseases including oral cancer. However, a mechanistic study to investigate alteration of secreted proteins in oral cells due to shisha smoking is lacking. OBJECTIVES Elucidation of differentially secreted proteins by immortalized human normal oral keratinocytes (OKF6/TERT1) upon chronic exposure to shisha. METHODS OKF6/TERT1 was chronically treated with 0.5% shisha extract for 8 months. Conditioned media from shisha treated (OKF6/TERT1-Shisha) and untreated (OKF6/TERT1-Parental) cells were subjected to TMT-based quantitative proteomic analysis. Bioinformatics analysis of differentially secreted proteins was carried out using SignalP, SecretomeP and TMHMM. Immunoblot validation of selected proteins was carried out to confirm the proteomics results. RESULTS Proteomic analysis of OKF6/TERT1-Parental and OKF6/TERT1-Shisha secretome resulted in the identification of 1,598 proteins, of which 218 proteins were found to be differentially secreted (⩾ 1.5-fold; p-value ⩽ 0.05) in shisha treated cells. Bioinformatics analysis using prediction tools showed secretory potential of differentially secreted proteins identified in OKF6/TERT1-Shisha. Western blotting validated the expression of AKR1C2, HSPH1 and MMP9 in OKF6/TERT1-Shisha secretome in agreement with proteomic data. CONCLUSION This study serves as a useful resource to understand the effect of chronic shisha smoking on the milieu of secreted proteins of oral cells. In vivo studies are warranted to supplement our in vitro data to elucidate the role of these proteins as early diagnostic biomarkers for oral carcinogenesis among shisha smokers.
Importance-Performance Analysis of Personal Health Records in Taiwan: A Web-Based Survey

Background Empowering personal health records (PHRs) provides basic human right, awareness, and intention for health promotion. As health care delivery changes toward patient-centered services, PHRs become an indispensable platform for consumers and providers. Recently, the government introduced “My health bank,” a Web-based electronic medical records (EMRs) repository for consumers. However, it is not yet a PHR. To date, we do not have a platform that can let patients manage their own PHR. Objective This study creates a vision of a value-added platform for personal health data analysis and manages their health record based on the contents of the "My health bank. " This study aimed to examine consumer expectation regarding PHR, using the importance-performance analysis. The purpose of this study was to explore consumer perception regarding this type of a platform: it would try to identify the key success factors and important aspects by using the importance-performance analysis, and give some suggestions for future development based on it. Methods This is a cross-sectional study conducted in Taiwan. Web-based invitation to participate in this study was distributed through Facebook. Respondents were asked to watch an introductory movie regarding PHR before filling in the questionnaire. The questionnaire was focused on 2 aspects, including (1) system functions, and (2) system design and security and privacy. The questionnaire would employ 12 and 7 questions respectively. The questionnaire was designed following 5-points Likert scale ranging from 1 (“disagree strongly”) to 5 (“Agree strongly”). Afterwards, the questionnaire data was sorted using IBM SPSS Statistics 21 for descriptive statistics and the importance-performance analysis. Results This research received 350 valid questionnaires. Most respondents were female (219 of 350 participants, 62.6%), 21-30 years old (238 of 350 participants, 68.0%), with a university degree (228 of 350 participants, 65.1%). They were still students (195 out of 350 participants, 56.6%), with a monthly income of less than NT $30,000 (230 of 350 participants, 65.7%), and living in the North Taiwan (236 of 350 participants, 67.4%), with a good self-identified health status (171 of 350 participants, 48.9%). After performing the importance-performance analysis, we found the following: (1) instead of complex functions, people just want to have a platform that can let them integrate and manage their medical visit, health examination, and life behavior records; (2) they do not care whether their PHR is shared with others; and (3) most of the participants think the system security design is not important, but they also do not feel satisfied with the current security design. Conclusions Overall, the issues receiving the most user attention were the system functions, circulation, integrity, ease of use, and continuity of the PHRs, data security, and privacy protection.
Experimental Investigation of Optimum and Allowable Range of Side Views for Teleoperated Digging and Release Works by Using Actual Construction Machinery

One of the most crucial problems associated with unmanned construction is low work efficiency due mainly to lack of visual information. External side views are necessary for efficient manipulation, but the optimum and allowable pan and tilt angles of side views have not been investigated. We thus conducted experiments using a scale model. However, experiments using actual construction machinery are necessary to confirm that these experimental results using a scale model can be applied to actual machines. Thus, the purpose of the present study is to investigate the optimum and allowable ranges of pan and tilt angles by using actual construction machinery. We focus on digging and releasing because these manipulation tasks require external views considering that precise operation is necessary. We conduct experiments based on a model task, including digging, which involves hook a target object, and releasing it in a designated area. The results suggest that the optimum pan angle for digging is 60°, and the allowable range is $45 ^{\circ} - 90 ^{\circ}$. Moreover, the results indicate that the optimum tilt angle is 60°, and the allowable range is $45 ^{\circ} - 90 ^{\circ}$.
Global hemostatic assay of different target procoagulant activities of factor VIII and factor IX

Background Korean National Health Insurance reimburses factor VIII (FVIII) and factor IX (FIX) clotting factor concentrate (CFC) infusions to discrepant activity levels, allowing elevation of FVIII activity to 60 IU/dL and FIX to 40 IU/dL. We aimed to assess hemostatic response to these target levels using global hemostatic assays. Methods We enrolled 34 normal healthy men, 34 patients with hemophilia A, and 36 with hemophilia B, with residual factor activity of 3 IU/dL or less and without inhibitors. Patients with hemophilia A and B received injected CFCs according to reimbursement guidelines. Fifteen minutes after injection, we assessed hemostatic response with global hemostatic assays: thrombin generation assay (TGA), thromboelastography (TEG), and clot waveform analysis (CWA). Results Normal healthy men and patients with hemophilia A and B were 36.7, 37.2, and 35.1 years old, respectively. FVIII and recombinant FIX concentrate doses were 28.8 IU/kg and 43.6 IU/kg. Post-infusion FVIII activity rose from 0.5 IU/dL to 69.4 IU/dL, while FIX activity rose from 1.4 IU/dL to 46.8 IU/dL. Post-infusion peak thrombin concentrations in hemophilia A and B were 116.6 nM/L and 76.4 nM/L (P<0.001). Post-infusion endogenous thrombin potential (ETP) in hemophilia A and B was 1349.8 nM/min and 915.6 nM (P<0.001). TEG index of hemophilia A and B was 0.11 and −0.51 (P=0.006). Conclusion Current reimbursed doses for FIX concentrates are insufficient to achieve hemostatic responses comparable to those after reimbursed doses for FVIII concentrates in terms of peak thrombin concentration, ETP, and TEG index.
Energy-Efficient Mechanism for Smart Communication in Cellular Networks

Internet-of-things (IoT) is gaining popularity in recent times. Cellular network can be an appropriate solution for the IoT connectivity. With the exponential increase in the number of connected IoT devices, energy consumption is a bottleneck for wireless connectivity solutions. In this work, we proposed a scheme to decrease the power consumption of a base station of a cell. The maximum power consumption of a base station is associated with its data processing, routing, and transmission. Our scheme reduce the size of the transmission data which ultimately reduce the power consumption associated with it. Multiple data reduction techniques have been used for this purpose. System level simulations show the efficiency of our proposed system.
CDBV: A Driving Dataset With Chinese Characteristics From a Bike View

Public datasets are valuable for the development of the learning-based algorithms. In the field of autonomous driving, almost all the existing public datasets are collected by manually driving cars, and most of them are contributed by American and European researchers and represent the traffic scenarios typical for these countries. To diversify the public driving datasets and to provide data support for researches on the self-driving vehicles moving on bike lanes, such as wheelchairs, bikes, and some robots, we present the Chinese Driving from a Bike View (CDBV), which is a driving dataset collected by a bike in Beijing, China. The CDBV dataset contains annotated images, raw videos, and code tools. The annotations are for the object detection tasks and especially focus on the categories with Chinese characteristics, including the express tricycles, the motorbikes specializing in takeouts, the bicycle landmarks, the zebra crossings, and the traffic lights. We totally annotated 40 064 traffic elements in 13 427 images. The CDBV dataset is publicly available at http://www.sujingwang. name/CDBV.html.
Graph Planarity Testing with Hierarchical Embedding Constraints

Hierarchical embedding constraints define a set of allowed cyclic orders for the edges incident to the vertices of a graph. These constraints are expressed in terms of FPQ-trees. FPQ-trees are a variant of PQ-trees that includes F-nodes in addition to P- and to Q-nodes. An F-node represents a permutation that is fixed, i.e., it cannot be reversed. Let $G$ be a graph such that every vertex of $G$ is equipped with a set of FPQ-trees encoding hierarchical embedding constraints for its incident edges. We study the problem of testing whether $G$ admits a planar embedding such that, for each vertex $v$ of $G$, the cyclic order of the edges incident to $v$ is described by at least one of the FPQ-trees associated with $v$. We prove that the problem is NP-complete even when the number of FPQ-trees associated with each vertex is bounded by a constant. If however the branchwidth of $G$ is bounded, the problem can be solved in polynomial time. Besides being interesting on its own right, the study of planarity testing with hierarchical embedding constraints can be used to address other planarity testing problems which can be modeled by associating a set of FPQ-trees to the vertices of the input graph. As a proof of concept, we apply our techniques to the study of NodeTrix planarity testing of clustered graphs. We show that NodeTrix planarity testing with fixed sides is fixed-parameter tractable when parameterized by the size of the clusters and by the tree-width of the multi-graph obtained by collapsing the clusters to single vertices.
3D-SIC: 3D Semantic Instance Completion for RGB-D Scans

This paper focuses on the task of semantic instance completion: from an incomplete, RGB-D scan of a scene, we aim to detect the individual object instances comprising the scene and jointly infer their complete object geometry. This enables a semantically meaningful decomposition of a scanned scene into individual, complete 3D objects. This semantic instance completion of a 3D scene opens up many new possibilities in enabling meaningful interactions with a scene, for instance for virtual or robotic agents. Rather than considering 3D semantic instance segmentation and scan completion separately, we propose 3D-SIC, a new end-to-end 3D convolutional neural network which jointly learns to detect object instances and predict their complete geometry -- achieving significantly better performance than treating these tasks independently. 3D-SIC leverages joint color-geometry feature learning and a fully-convolutional 3D network to effectively infer semantic instance completion for 3D scans at scale. Our method runs at interactive rates, taking several seconds inference time on scenes of $30$m $\times$ $25$m spatial extent. For the task of semantic instance completion, we additionally introduce a new semantic instance completion benchmark on real scan data, where we outperform alternative approaches by over 15 in mAP@0.5.
Face Memorization System Using the Mathematical AIM Model for Mobile Robot

In this paper, we present a bio-inspired method to help make efficient utilization of computing resource and battery of a robot system, and at the same time, help a robot to draw people’s attention and leave a friendly impression. To achieve the goals described above, firstly, the mathematical Activation-Input-Modulation (AIM) model is applied to the robot, which can emulate the human states of consciousness (e.g., waking, sleeping). By using it, the robot will be able to switch state according to the external environment. When there is nobody around the robot, it turns the state from waking to sleeping, cutting down a part of information processing to economize the utilization of computing resource and battery. Secondly, we propose to develop a face memorization system which enables the robot to recognize the human face by using deep/machine learning methods. When the robot meets a person whose face was memorized, it greets that person by calling his/her name to draw people’s attention and leave a friendly impression on them. Besides, in order to make effective use of the sleeping time, when the robot is in the sleeping state, we propose to assign works to the robot that are difficult or unnecessary to be processed when in the waking state, such as image pre-processing and classifier training. These works are also necessary to implement a face recognition function that can achieve high accuracy. Finally, experiments using the mathematical AIM model and the face memorization system together were conducted to demonstrate the effectiveness of the proposed method on the utilization of computing resource and battery.
Design and Development of Integrated Deep Convolution Neural Network Approach for Handling Heterogeneous Medical Data

In recent years, Neural Network (NN) is developed as an optimal technique for the prediction of tasks which include image classification, speech recognition and also useful in biomedical analysis. Biomedical data consists of diverse modalities like X-ray, CT, MRI, PET, EEG and ECG signals. There are several NNs techniques such as Artificial Neural Network (ANN), Convolutional Neural Network (CNN) and Deep Neural Network (DNN) that are used for various prediction applications in handling multimodal heterogeneous data. However, learning and prediction of such multimodal data limits the scope of existing neural techniques. One of the limitations of ANN observes that addition of layer cause back propagation stuck in local minima and reduction of learning speed. Where, DNN causes higher computational complexity in training the features which are based on contrastive divergence. In CNN there is loses of spatial information due to the weight factors variation. To overcome these issues, this paper proposes a novel learning technique in which the weight factor of DNN is integrated with CNN for handling multimodal heterogeneous data. The simulation results prove that the integrated learning technique (IDCNN) obtains better learning performance than ANN, CNN and DNN models in terms of Root Mean Square Error (RMSE) and efficiency in terms of cross entropy.
On the Difference between Physics and Biology: Logical Branching and Biomolecules

Physical emergence - crystals, rocks, sandpiles, turbulent eddies, planets, stars - is fundamentally different from biological emergence - amoeba, cells, mice, humans - even though the latter is based in the former. This paper points out that an essential difference is that as well as involving physical causation, causation in biological systems has a logical nature at each level of the hierarchy of emergence, from the biomolecular level up. The key link between physics and life enabling this to happen is provided by biomolecules, such as voltage gated ion channels, which enable branching logic to emerge from the underlying physics and hence enable logically based cell processes to take place in general, and in neurons in particular. These molecules can only have come into being via the contextually dependent processes of natural selection, which selects them for their biological function. A further major difference is between life in general and intelligent life. We characterise intelligent organisms as being engaged in deductive causation, which enables them to transcend the physical limitations of their bodies through the power of abstract thought, prediction, and planning. Ultimately this is enabled by the biomolecules that underlie the propagation of action potentials in neuronal axons in the brain.