Miracle-dz/UniFAR-FTUs huggingface dataset:
Fitting Cox Model Using PROC PHREG and Beyond in SAS

Cox proportional hazard model has been widely used for survival analysis in many areas in investigating time-to-event data. PROC PHREG in SAS has been a powerful tool used for construction of a Cox model. In addition to the STATEMENTs and OPTIONs within PHREG that have already provided the most demanded output, a little more effort on data manipulation would accomplish the calculations and generate output for some new statistical approaches. This paper presents: 1) Using overall C-index as a measure of discrimination for model validation; 2) Calculating adjusted survival rate using corrected group prognosis method; 3) Presenting the effect of a continuous covariate on estimated survival. The core SAS program code and MACRO used for implementation are included.
Increasing packet delivery in Ad Hoc On-Demand Distance Vector (AODV) routing protocol

Broadcasting in the route discovery and the route maintenance of Ad hoc On-demand Distance Vector (AODV) routing protocol provokes a high number of unsuccessful packets deliveries from the source nodes to the destination nodes. Studies have been undertaken to optimize the rebroadcast focused on the route discovery of the AODV. In this study, lifetime ratio (LR) of the active route for the intermediate node is introduced to increase the number of unsuccessful packets delivery. Simulation results focused on the improvement of the packet delivery in the routing protocol compared to standard AODV. The performance metrics are measured by varying the number of nodes and the speeds. The OMNET++ is used to simulate the performance of the metrics.
M-parking: Vehicle parking guidance system using hierarchical Wireless Sensor Networks

With an expeditious increase in the vehicle transport in the major cities, it has become more difficult to find the free parking space to park the vehicles. Constant rise in the number of vehicles on the roads has led to increase in the unwanted traffic, drivers frustration, delay in the transportation and increase in the noise and air pollution. Due to lack of proper knowledge of parking space, it has become unable to utilize most of the nearby parking space. This work proposes an innovative approach to guide the vehicle drivers to the parking space using Hierarchical Wireless Sensor Networks. A three sensors per parking slot based technique is used to distinguish the type of parked vehicles and to find the free parking slots in the parking area. Free, partially-occupied and occupied parking slot states information is stored at the Parking Server in the form of parking location and the parking area layout with parking slot states marking on it. Parking Server makes the parking information available to the vehicle drivers by the mean of web and mobile applications for guiding vehicles for parking. The results show that the parking detection with multiple passive sensor nodes per parking slot system is more energy efficient and vehicle parking detection is as accurate as the single active sensor per parking slot system in the existing parking designs.
Compact oscillation neuron exploiting metal-insulator-transition for neuromorphic computing

The phenomenon of metal-insulator-transition (MIT) in strongly correlated oxides, such as NbO2, have shown the oscillation behavior in recent experiments. In this work, the MIT based two-terminal device is proposed as a compact oscillation neuron for the parallel read operation from the resistive synaptic array. The weighted sum is represented by the frequency of the oscillation neuron. Compared to the complex CMOS integrate-and-fire neuron with tens of transistors, the oscillation neuron achieves significant area reduction, thereby alleviating the column pitch matching problem of the peripheral circuitry in resistive memories. Firstly, the impact of MIT device characteristics on the weighted sum accuracy is investigated when the oscillation neuron is connected to a single resistive synaptic device. Secondly, the array-level performance is explored when the oscillation neurons are connected to the resistive synaptic array. To address the interference of oscillation between columns in simple cross-point arrays, a 2-transistor-1-resistor (2T1R) array architecture is proposed at negligible increase in array area. Finally, the circuit-level benchmark of the proposed oscillation neuron with the CMOS neuron is performed. At single neuron node level, oscillation neuron shows >12.5× reduction of area. At 128×128 array level, oscillation neuron shows a reduction of ∼4% total area, >30% latency, ∼5× energy and ∼40× leakage power, demonstrating its advantage of being integrated into the resistive synaptic array for neuro-inspired computing.
The phase noise analysis in Colpitts VCO implemented by CMOS technology

A Colpitts voltage controlled oscillator (VCO) is designed and fabricated by 0. 18μm CMOS technology. The linear time variant (LTV) model was used to analyze the phase noise. Under a supply voltage of 1.8 V, the frequency range of the VCO covers 340 ~ 400 MHz, the phase noise is −90dBc/Hz at 10 kHz offset, and the output power is −2.5dBm. The phase noise testing results are consistent with the theoretical calculating results. The chip size is 570μm×300μm.
Localization method of multiple targets by Crossing Direction Finding

In this paper, a Crossing Direction Finding (CDF) for localizations of multiple targets is proposed by using triangular theory. The location of a target is unique if the directions to other two determined points are known a priori. The two 2D direction-of-arrivals (DOAs) of target are obtained by using two cooperative radars respectively. The 2D DOAs are automatically matching by using L-shape array and conjugate-ESPRIT algorithm. The multiple targets'DOAs are automatically matching by using one reference sensor in multiple coordinate system. This method has less demand on radar system property, while hold a better resolution on localization than single radar detecting algorithm. Simulation results show that the CDF has a well performance on multiple targets localization.
A Topological Model for Grayscale Image Transformation

In this paper, we work on the topological properties such as the connectivity of regions, the boundaries and the adjacency for 2-D grayscale images. We present some original ideas for applying topology methods onto the graylevel image transformation which we call it pansystem topology. We use a Pansystem Parental model to develop an algorithm for grayscale image transformation. We also analyze the procedure of using pansystem clustering model to calculate connected components. The developed model can be well applied to biology image application such as image X-Ray image segmentation and cell counting.
The Demand for M4: A Sectoral Analysis. Part 2 the Corporate Sector (in 2 Parts)

This paper is the second part of a study on the determinants of the broad money aggregate M4, following a similar analysis of the personal sector developed in Working Paper No 61. It models the broad money holdings of both industrial and commercial companies (ICCs) and other financial institutions (OFIs) in the UK, and examines what role they play in the transmission mechanism. ICCs are shown to have both a transactions and a portfolio motive for holding money. A three-equation model of money, investment and the cost of capital is estimated, and the results suggest the existence of a corporate sector liquidity channel whereby firms' "excess" money balances have a negative influence on the cost of capital and a positive impact on investment spending. OFIs' money holdings are shown to depend upon wealth and relative rates of return, in line with standard portfolio models of money demand. But as OFIs are the chief counterparts to banks' liability management activity, their money holdings are also modelled jointly with deposit rates.
An event-based SDN architecture for network security analysis

Software-defined networking (SDN), which decouples the control plane from traditionally proprietary network devices, is highly flexible and suitable for flow management. However, if the policy depends on the results of network security analysis, the controller will perform complicated packet processing such as packet reassembly and protocol analysis. Such processing will easily overburden the controller, and passing raw packets to the controller will result in large communications overheads. In this work, we propose an event-based SDN architecture for network security analysis. This architecture comes with an event extractor on the network device that performs protocol analysis to extract policy neutral events from network traffic. The network device looks up an extracted event in an event table to see whether a policy exists for the event. If not, it will ask the controller about the policy corresponding to this event and configure the policy in the event table accordingly. Since the controller deals with only high-level event descriptions, it is free from low-level packet processing. We evaluate this architecture by emulating it with the Bro intrusion detection system for event extraction from real traffic. The experimental results show the communication overheads between the network devices and the controller can be effectively reduced.
Parallel branch-and-bound algorithm for constructing evolutionary trees from distance matrix

An ultrametric tree is an evolutionary tree in which the distances from the root to all leaves in the tree are equal. The Minimum Ultrametric Tree construction problem is the problem of constructing an ultrametric tree from distance matrices with minimum cost. It is shown that to construct a minimum cost ultrametric tree is NP-hard. In this paper, we present an efficient parallel branch and bound algorithm to construct a minimum ultrametric tree with less cost. The experimental results show that our proposed algorithm can discover optimal solutions for 38 species within reasonable time with 16 computing nodes
C 2011 Logan Niehaus Autonomous Learning of Action-word Semantics in a Humanoid Robot Table of Contents

For creation of an artificial agent that is capable of using language naturally, models that only manipulate symbols or classify speech are ineffective. The semantic information which language conveys must be grounded in the agent’s complete sensorimotor experience. Typically, patterns from visual, auditory, and proprioceptive data streams which share the same conceptual cause are fused together in an associative memory at the core of the language model. Coupling of motor and auditory modalities, which is crucial for a large part of semantic understanding, presents a particularly difficult challenge. Words and actions both need models capable of capturing spatial and temporal structure, and training algorithms that can learn in a selforganizing, incremental fashion. Presented is a method for online learning of word and action lexicons based on the hidden Markov model. The model is then evaluated through action-word learning experiments implemented on a humanoid robot.
Towards Bootstrap Learning for Object Discovery ∗

We show how a robot can autonomously learn an ontology of objects to explain aspects of its sensor input from an unknown dynamic world. Unsupervised learning about objects is an important conceptual step in developmental learning, whereby the agent clusters observations across space and time to construct stable perceptual representations of objects. Our proposed unsupervised learning method uses the properties of allocentric occupancy grids to classify individual sensor readings as static or dynamic. Dynamic readings are clustered and the clusters are tracked over time to identify objects, separating them both from the background of the environment and from the noise of unexplainable sensor readings. Once trackable clusters of sensor readings (i.e., objects) have been identified, we build shape models where they are stable and consistent properties of these objects. However, the representation can tolerate, represent, and track amorphous objects as well as those that have well-defined shape. In the end, the learned ontology makes it possible for the robot to describe a cluttered dynamic world with symbolic object descriptions along with a static environment model, both models grounded in sensory experience, and learned without external supervi-
Adaptive Brush Stroke Generation for Painterly Rendering

We propose adaptive brush stroke generation for source images, using reference data. Colors used are formed by actual palette colors from artists. To create the palette, we have referred mostly to colors used in Van Gogh’s works and determined the color of brush strokes by transferring it to the most similar one, through comparing colors used in source images and the palette colors. Also, by referring to the edge orientation of source images, we have applied a brush stroke orientation that surrounds the edges. The sizes were determined depending on the different sizes of the objects from wide to narrow brushes. Finally, we applied spline curve shapes. The brush strokes created in such method, were applied separately according to its segmented images, and composed after rendering
A Critique of Structural VARs Using Real Business Cycle Theory

The main substantive finding of the recent structural vector autoregression literature with a differenced specification of hours (DSVAR) is that technology shocks lead to a fall in hours. Researchers have used these results to argue that business cycle models in which technology shocks lead to a rise in hours should be discarded. We evaluate the DSVAR approach by asking, is the specification derived from this approach misspecified when the data are generated by the very model the literature is trying to discard? We find that it is misspecified. Moreover, this misspecification is so great that it leads to mistaken inferences that are quantitatively large. We show that the other popular specification that uses the level of hours (LSVAR) is also misspecified. We argue that alternative state space approaches, including the business cycle accounting approach, are more fruitful techniques for guiding the development of business cycle theory. > > Replaced by Staff Report No. 364
Sentence Rephrasing for Parsing Sentences with OOV Words

This paper addresses the problems of out-of-vocabulary (OOV) words, named entities in particular, in dependency parsing. The OOV words, whose word forms are unknown to the learning-based parser, in a sentence may decrease the parsing performance. To deal with this problem, we propose a sentence rephrasing approach to replace each OOV word in a sentence with a popular word of the same named entity type in the training set, so that the knowledge of the word forms can be used for parsing. The highest-frequency-based rephrasing strategy and the information-retrieval-based rephrasing strategy are explored to select the word to replace, and the Chinese Treebank 6.0 (CTB6) corpus is adopted to evaluate the feasibility of the proposed sentence rephrasing strategies. Experimental results show that rephrasing some specific types of OOV words such as Corporation, Organization, and Competition increases the parsing performances. This methodology can be applied to domain adaptation to deal with OOV problems.
UvA-DARE ( Digital Academic Repository ) Accelerated autoantibody clearance by intravenous immunoglobulin therapy : studies in experimental models to determine the magnitude and time course of the effect

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Motivating Ontology-Driven Information Extraction

Ontologies can provide Information Extraction Systems (IES) with much needed domain and task knowledge. Yet, it has to be analysed to what extent and in which form ontologies can be utilised to enhance the overall performance of an IES. Further, the use of ontologies requires an accurate management of the same. The most important issue with this respect is to keep the ontology up-to-date, because the domain it represents will change inevitably. In this paper we motivate the use of ontologies within IES, especially for automating the extraction-rule generation process, which currently is the main obstacle to portable and scalable IESs.
Normalizer circuits and a Gottesman-Knill theorem for infinite-dimensional systems

$\textit{Normalizer circuits}$ [1,2] are generalized Clifford circuits that act on arbitrary finite-dimensional systems $\mathcal{H}_{d_1}\otimes . \otimes \mathcal{H}_{d_n}$ with a standard basis labeled by the elements of a finite Abelian group $G=\mathbb{Z}_{d_1}\times. \times \mathbb{Z}_{d_n}$. Normalizer gates implement operations associated with the group $G$ and can be of three types: quantum Fourier transforms, group automorphism gates and quadratic phase gates. In this work, we extend the normalizer formalism [1,2] to infinite dimensions, by allowing normalizer gates to act on systems of the form $\mathcal{H}_\mathbb{Z}^{\otimes a}$: each factor $\mathcal{H}_\mathbb{Z}$ has a standard basis labeled by $\textit{integers}$ $\mathbb{Z}$, and a Fourier basis labeled by $\textit{angles}$, elements of the circle group $\mathbb{T}$. Normalizer circuits become hybrid quantum circuits acting both on continuous- and discrete-variable systems. We show that infinite-dimensional normalizer circuits can be efficiently simulated classically with a generalized $\textit{stabilizer formalism}$ for Hilbert spaces associated with groups of the form $\mathbb{Z}^a\times \mathbb{T}^b \times \mathbb{Z}_{d_1}\times. \times \mathbb{Z}_{d_n}$. We develop new techniques to track stabilizer-groups based on normal forms for group automorphisms and quadratic functions. We use our normal forms to reduce the problem of simulating normalizer circuits to that of finding general solutions of systems of mixed real-integer linear equations [3] and exploit this fact to devise a robust simulation algorithm: the latter remains efficient even in pathological cases where stabilizer groups become infinite, uncountable and non-compact. The techniques developed in this paper might find applications in the study of fault-tolerant quantum computation with superconducting qubits [4,5].
Characterization of Cloud Obscured Free Space Optical Channels

For ground-to-air and air-to-air communications, clouds form a main element of the communication channel. In wireless optical communications, clouds induce temporal and spatial dispersion, causing power loss and bandwidth constraints; critical parameters for reliable communication system design. In this paper, we focus on the temporal dispersion induced by the clouds, and provide a statistical estimate of the available bandwidth for cloud obscured channels based on a two year observation period for the continental United States.
Data aggregation in resource-limited wireless communication environments — Differences between theory and praxis

Multimodal Wireless Sensor Networks generate huge amount of heterogeneous data, which has to be transmitted in a dynamically changing network infrastructure. Especially in the domain of wireless low-power applications, the energy-efficiency and the prioritisation of communication tasks is critical. Several research areas deal with these issues. They are optimising the respective hardware components as well as the protocols within the PHY, MAC or network layer. But for an optimised media transport in the topology also the data management and the task scheduling on the application layer are essential. Here, the key challenge is to minimise the data amount without decreasing the information quality. Related research work in the field of data aggregation and data fusion offer interesting techniques for an efficient data handling. In this paper, we discuss usual ways for data aggregation, including the adapted communication process. We critically analyse the benefits in theory and compare these conceptual advantages with measured real-world results. The evaluation was done in two steps. The first one is based on simulation scenarios of typical WSN/SANET applications. In a second step, we implement a demonstrator platform for a respective real-world environment. The test bed configuration is similar to the simulation scenario and provides comparable data. Based on the results and the respective analysis, we propose feasible methods for optimising data aggregation techniques. We clarify, that these improvements are essential for an efficient usage in resource-limited, multimodal sensor network environments.
Liberalism and Postmaterialism in China: The Role of Social Class and Inequality

Abstract The postmaterialist thesis holds that postmaterialist and liberal values tend to be strongest in affluent locations and among people in higher socioeconomic positions. We demonstrate the degree to which China fails to conform to these expectations and seek to account for Chinese exceptionalism. We use multilevel models fitted to 2006 Chinese General Social Survey data to test the postmaterialist thesis. In general, we find expected associations for postmaterialism but not for liberalism. Indicators of individual-level status, including household income, middle/upper class status, urban residence, and majority ethnic group status are not associated with liberalism. Provincial-level affluence is not positively associated with either postmaterialism or liberalism, while income inequality is positively associated with liberalism. We conclude that in highly collectivist cultures like China's, economic development can have unexpected effects on value change. Growing inequality, which people in lower-status positions perceive as a threat, can promote liberalism, while people who benefit most from rising affluence and growing inequality may be more inclined to support traditional than liberal values.
Robust Data Association

We present a novel method for performing data association that handles complex motion models and also increases the robustness of tracking. Instead of using a motion model defined recursively, we prefer to robustly fit the motion model over multiple frames simultaneously. This allows us to elegantly handle arbitrarily complex motion models, track initiation, occlusion and false alarms. This also improves the robustness to motion model weakness and to frequent false-negatives and false-positives, and the autonomy of the tracker. Our algorithm is easy to implement and we show its capabilities on two real examples of complex motion tracking.