Miracle-dz/UniFAR-FTUs huggingface dataset:
Portfolio Choice With a Correlated Background Risk: Theory and Evidence

In this paper, we extend the static portfolio choice problem with a small background risk to the case of small partially correlated background risks. We show that respecting the theories under which risk substitution appears, except for the independence of background risk, it is perfectly rational for the individual to increase his optimal exposure to portfolio risk when risks are partially negatively correlated. Then, we test empirically the hypothesis of risk substitutability using French households data. We found that households respond by increasing their stockholdings in response to the increase in future earnings uncertainty. This conclusion is in contradiction with results obtained in other countries. So, in light of these results, our model provides an explanation to account for the lack of empirical consensus on cross-country tests of risk substitution theory that encompasses and criticises all of them.
Local Polynomial Regression and Its Applications in Environmental Statistics

Nonparametric regression estimates a conditional expectation of a response given a predictor variable without requiring parametric assumptions about this conditional expectation. There are many methods of nonparametric regression including kernel estimation, smoothing splines, regression splines, and orthogonal series. Local regression ts parametric models locally by using kernel weights. Local regression is proving to be a particularly simple and eeective method of nonparametric regression. This talk reviews recent work on local polynomial regression including estimation of derivatives, multivariate predictors, and bandwidth selection. Three applications to environmental science are discussed: 1. Estimation of the distribution of airborne mercury about an incinerator using biomonitoring data. 2. Estimation of airborne pollutants from LIDAR (LIght Detection And Ranging) data. Because of substantial heteroskedasticity, this example requires estimation of the conditional variance function as well as the conditional expectation function. 3. Estimation of gradients from elevation data. The estimated gradients are used in a model to predict soil movement during earthquakes. Data from Noshiro, Japan are used. The rst and third examples use two-dimensional spatial data. Though these problems could be analyzed by geostatistics, local polynomial regression has the advantage of modeling the heteroskedasticity in the rst and second examples and being able to estimate gradients in the third.
Identification of Vulnerable Groups and Coping Strategies in Korea

Using a 5-year household panel data from Korea, this paper identifies the vulnerable households and their coping strategies during 1994-1998. We find that (i) larger households tend to be more protected from shortfalls in consumption; (ii) households with young children tend to decrease consumption of other items to achieve higher spending on children’s education; (iii) female-headed households were not more vulnerable but these households appear to trade off children’s educational spending for food consumption; and (iv) urban households became more vulnerable during the 1997 currency crisis. We find that households reduce their consumption of luxurious and durable items to cope with negative shocks to their income. They tend to increase their debt to smooth expenditure on education and medical care while using private transfer income to smooth consumption of food and other non-durable. Private transfer income appears to act as both an ex-ante risk-managing device and an ex-post coping mechanism. We do not find public transfer income to be an effective coping device during the initial period of the crisis primarily because social safety net programs were not yet in place during the period our analysis covers.
Mixed calcium-magnesium pre-nucleation clusters enrich calcium

Abstract It is demonstrated that magnesium and carbonate ions can form pre-nucleation clusters in analogy to calcium carbonate. If a mixed calcium and magnesium solution is brought in contact with carbonate ions, mixed pre-nucleation clusters form. The equilibrium constants for their formation are reported revealing that over the entire range of possible cation mixing ratios, calcium gets enriched over magnesium in the pre-nucleation clusters. This can explain high magnesium contents in amorphous calcium carbonate. However, this enrichment alone is not sufficient to explain the magnesium content of less than 41 mol% which is found in magnesium calcite biominerals nucleated from seawater.
A novel abstraction-guided simulation approach using posterior probabilities for verification

This paper presents a novel abstraction-guided simulation approach for multiple target states which uses posterior probabilities of the states from the abstract model, instead of abstract distances used by former abstraction-guided approaches, as the guidance of simulation. The posterior probabilities carry more precise information of the abstract model, being able to offer more effective guidance as well as allow the simulation to deal with multiple target states at a time. Experimental results show that the simulation using posterior probabilities as guidance is much more efficient than that using the abstract distances, and the multiple target states simulation framework reduces the simulation cycles effectively.
Efﬁcient Communication in Participatory Environment

Participatory environments are a set of users that are within each other’s vicinity willing to share the device to perform a common task. The goal of this paper to make use of this environment to transmit data from source to destination. But there is no connected path between the two. The nodes that serve as the link between source and destination are chosen dynamically, based on some predetermined criteria. The model developed in this paper, selects the node eﬃ-ciently and its implementation is on an Android device. I have implemented 2 cases of the model stated in this paper. One where the source will select the intermediary node ef-ﬁciently and transmit the data. Other case will involve the destination, selecting the intermediary node to which the source will send its data and then transmit it to the destination. The focus of the project is to transmit messages eﬃciently from source to destination. The sections below will discuss the literature review, my implementation of this model and the results I collected. Since there are two cases of my model, we can compare the two and decide which one is most suitable for the user.
Hybrid trie based partitioning of TCAM based openflow switches

The packet forwarding mechanism in the open flow network switch is based on the IP lookup in the forwarding table, which consist of the destination IP address of the incoming packet. In general, these forwarding tables are Content Addressable Memory (CAM), where the desire key will be searched in the table to know out going port. Ternary content addressable memory (TCAM) is one of the efficient mechanism to store the forwarding table, where values are stored in sorted order. Search of any key is done by using longest prefix matching technique. It uses 0, 1 and x (don't care) to represent the data, rather using 0 and 1 in case of CAM. The major drawback of TCAM is that it is a power hungry circuit. Today's high-density TCAMs consumes 12 to 15 Watts of power per chip, when the entire memory is enabled. This paper proposes an efficient representation of data in the forwarding tables and search algorithm which takes the algorithmic time complexity is O(loglog n). This paper also proposes an efficient way to reduce the index TCAM size by using y-Fast trie-partitioning algorithm.
Modelling and simulation of controlled bi-directional power electronic converters in a DC energy distribution line with AC grid- and motor-side active filtering

The paper deals with the modelling and simulation of the bi-directional power flow through different type of power electronic converters connected to a DC-energy distribution line, which is supplied from the AC grid by means a square-wave rectifier with active current filtering at unity power factor. The DC line is supplying more vector controlled AC drives, like PWM voltage-source inverter-fed field- oriented medium power induction motor and PM synchronous motor drives. There is connected also a high power induction motor drive fed by a tandem hybrid frequency converter containing of a high power current-source inverter for energy transfer and a low power voltage-source one for active filtering of the motor currents. The two DC-links of the component inverters are coupled by means of a two quadrant DC chopper. The modeling of the power electronic converters is based on circuit techniques, according to a similar method like the quadripole theory and not on circuit-oriented simulation concept. The simulation structures are containing blocks with two inputs and two outputs, one for the current and one for the voltage on each part of the quadripole. Each converter is consisting of a current-model- and a voltage-model-based block, which are coupled together by means of the commutation- or PWM-logic signals. The PWM converters (inverter, rectifier and active filter) are controlled with current feedback modulation at constant switching frequency. The square-wave rectifier and the DC-link chopper have "reverse" model versus the PWM converters, concerning the inputs and outputs with respect to the energy flow. The simulations were made in MATLAB-Simulink environment. The simulation structures built in MATLAB/Simulink are suitable for easy implementation of the motor and converter control structures due to the connection between the simulation environment and the hardware platform by means of dSPACE controller board.
A biphasic temporal pattern in pupil size around perceptual switches in binocular rivalry

In binocular rivalry, two incompatible inputs are presented separately to the two eyes, causing subjects to alternate between two perceptual interpretations. These percept switches engage a large frontoparietal network, accompanied by large pupil dilations. It is unclear whether these measured functional MRI (fMRI) and pupil responses relate to neural processing causing the switches, or constitute the brain’s response to the switches. The temporal dynamics with which subjects switch between the two interpretations are determined by catecholaminergic neuromodulatory systems and these dynamics should be reflected in pupil size. We simultaneously recorded pupil dilation and fMRI during binocular rivalry, and show a biphasic pupil response around the time of a percept switch. The strength of a transient pupil dilation after the behavioral response reflects the temporal predictability of the switch. We also find a marked pupil constriction before the behavioral response. Surprisingly, the amplitude of this constriction predicts the duration of the subsequent percept several seconds in advance, implying that the pupil can be used as a proxy for two different neural mechanisms underpinning perceptual dynamics in ambiguous perception. Together with concurrent fMRI recordings, these results allow us to disambiguate between competing computational accounts of the ongoing inference process that shapes the dynamics of bistable perception.
Cepstral Statistics Compensation Using Online Pseudo Stereo Codebooks for Robust Speech Recognition in Additive Noise Environments

In this paper, we propose the cepstral statistics compensation (CSC) algorithm, which alleviates the effect of additive noise on the cepstral features for speech recognition. It is a simple but quite efficient noise reduction technique that makes use of online constructed pseudo stereo codebooks. The statistics, such as mean and variance, for the cepstral features in both clean and noisy environments are evaluated using pseudo stereo codebooks. Then a transform is obtained for the noise-corrupted cepstra so that the statistics of the transformed ones are close to those of clean cepstra. Experimental results show that CSC provided a 13% reduction in word error rate when compared to the results obtained using cepstral mean and variance normalization (CMVN), and a 34% reduction in error rate compared to baseline processing in the noise range of 0-20 dB in experiments conducted on Aurora-2 test set A noisy digits database. In addition, we also provide some other noise robustness approaches based on pseudo stereo codebooks and show their effectiveness in noisy speech recognition
Capacity of Cellular CDMA Systems with Integrated Voice and Data Services

In this paper we analyze the capacity of a CDMA system supporting integrated voice and data services. The bit energy-to-interference ratios for voice and data services are each modeled using lognormal distributions, and an integrated capacity formula based on a lognormal approximation is proposed. The capacity formula defines an admission scheme to guarantee with a predefined probability the total interference is less than the maximum acceptable interference. Under the assumption of Poisson arrivals for voice and data services, Kaufman's method is applied to calculate the blocking probabilities for voice and data services. Numerical results are provided to illustrate the proposed procedure.
A catalogue of dust clouds in the Galaxy

In this study 21 catalogues of dust clouds in the Galaxy were cross-identied by taking into account available properties such as position, angular dimensions, opacity class and velocity. An initial list of6500 entries was condensed into a cross-identied all-sky catalogue containing 5004 dust clouds. In particular, the transition zone between high and low Galactic latitude studies was also cross-identied. The unied catalogue contains 525 high-latitude clouds. The catalogue deals primarily with optical dark nebulae and globules, but it includes as well substantial information from their molecular counterparts. Some previously uncatalogued clouds were detected on optical images and FIR maps. Finally, we address recent results and prospective work based on NIR imaging, especially for clouds detected in the 2MASS Ks Atlas.
Dynamical model of long-term synaptic plasticity

Long-term synaptic plasticity leading to enhancement in synaptic efficacy (long-term potentiation, LTP) or decrease in synaptic efficacy (long-term depression, LTD) is widely regarded as underlying learning and memory in nervous systems. LTP and LTD at excitatory neuronal synapses are observed to be induced by precise timing of pre- and postsynaptic events. Modification of synaptic transmission in long-term plasticity is a complex process involving many pathways; for example, it is also known that both forms of synaptic plasticity can be induced by various time courses of Ca2+ introduction into the postsynaptic cell. We present a phenomenological description of a two-component process for synaptic plasticity. Our dynamical model reproduces the spike time-dependent plasticity of excitatory synapses as a function of relative timing between pre- and postsynaptic events, as observed in recent experiments. The model accounts for LTP and LTD when the postsynaptic cell is voltage clamped and depolarized (LTP) or hyperpolarized (LTD) and no postsynaptic action potentials are evoked. We are also able to connect our model with the Bienenstock, Cooper, and Munro rule. We give model predictions for changes in synaptic strength when periodic spike trains of varying frequency and Poisson distributed spike trains with varying average frequency are presented pre- and postsynaptically. When the frequency of spike presentation exceeds ≈30–40 Hz, only LTP is induced.
Automatic Extraction and Prediction of Word Order Errors from Language Learning SNS

Recent research into writing tools to support foreign language learners of English has focused on prevalent errors in learner writing, while other errors, such as word order errors, have received little attention. As the word order of some languages are similar, there are also large differences between languages which can affect foreign language learning. In this paper, we automatically extract corrected sentences that contain word order errors samples from a language learning SNS to create a word order error corpus for machine learning. This corpus is then analyzed to train and evaluate the effectiveness of SVM classifiers to automatically classify word order errors in learner writing.
Query Driven Simulation Using SIMODULA

This paper describes the design and early implementation phases of a simulation modeling environment that integrates a process-oriented simulation language and a database system with object-oriented extensions. User may interact with the system simply by formulating SQL-like queries to retrieve information stored from previous simula tions, or indeed cause information to be automatically generated.
Evaluation of exon-skipping strategies for Duchenne muscular dystrophy utilizing dystrophin-deficient zebrafish

Duchenne muscular dystophy (DMD) is a severe muscle wasting disease caused by mutations in the dystrophin gene. By utilizing antisense oligonucleotides, splicing of the dystrophin transcript can be altered so that exons harbouring a mutation are excluded from the mature mRNA. Although this approach has been shown to be effective to restore partially functional dystrophin protein, the level of dystrophin protein that is necessary to rescue a severe muscle pathology has not been addressed. As zebrafish dystrophin mutants (dmd) resemble the severe muscle pathology of human patients, we have utilized this model to evaluate exon skipping. Novel dmd mutations were identified to enable the design of phenotype rescue studies via morpholino administration. Correlation of induced exon‐skipping efficiency and the level of phenotype rescue suggest that relatively robust levels of exon skipping are required to achieve significant therapeutic ameliorations and that pre‐screening analysis of exon‐skipping drugs in zebrafish may help to more accurately predict clinical trials for therapies of DMD.
Autonomous agent based intrusion detection in virtual computing environment

One of the motivations for virtualization technology is the desire to develop new services to enhance system security without trusting both the applications and the operating systems. An intrusion detection system is an example of such service that can help to isolate users from malicious attacks. In this paper, we propose hybrid-based intrusion detection architecture in virtual computing environment to detect and isolate harmful behaviors by real-time monitoring and alarming. In contrast to monolithic intrusion detection system, we introduce autonomous agents, acting independently of each other, to monitor the system. The agents are deployed in virtual machines to analyze actions occurring on the network and inside the hosts to determine whether they are potential security violations or not. Our architecture is implemented based on Xen, and the detection management center is deployed in a secure virtual machine.
Change-Resilient Design and Dataflow Optimization for Distributed XML Stream Processors

We propose a new stream-processing framework based on a virtual assembly line (val) model. We instantiate the val framework obtaining ∆-XML, an approach for designing and optimizing distributed XML processing pipelines. val/∆-XML greatly simplifies the design of change-resilient dataflow pipelines: XML processors (called actors) can be inserted, deleted, and their “scope of work” (the parts of the stream they can read from and write to) changed freely, without compromising the overall process pipeline design. Unlike conventional approaches that rely on adapters to “glue” together processing components, our actors employ flexible configurations that select only relevant portions of the input stream. ∆-XML pipelines are not only more flexible and change resilient than current approaches, but can also be optimized by compiling them into dataflow process networks that minimize shipping cost in distributed settings: Using a static type inference approach based on regular expression types for XML, we show how to perform a dataflow analysis to determine XML stream fragments that are relevant to an actor, allowing irrelevant fragments to be bypassed (“shipped”) to downstream actors. We also show that our approach is optimal for distributed XML pipelines, given the type information available in ∆-XML.
High performance control on air-condition compressor drive system

Aiming to air-condition compressor drive system, this paper proposes a compound control strategy based on sliding-mode observer and high frequency voltage injection method. In high speed region, an excellent performance control on compressor is conducted by combining two-stage filter and angle compensation with phase-locked loop technology. For the low speed region, the reason of speed fluctuation for compressor is firstly analyzed. Then, the real-time load condition is estimated through look-up table on the basis of estimated rotor position, and torque current feed-forward compensation is performed automatically according to estimated load torque. Thus, the speed fluctuation for compressor is attenuated greatly and a good performance control of compressor is achieved within a low speed region. Simultaneously, in order to reduce the power loss of compressor and improve the running efficiency of compressor, MTPA control is employed under base speed. Finally, these proposed strategies are verified by experiments.
The Conjugacy Problem in Amalgamated Products I: Regular Elements and Black Holes

We discuss the time complexity of the word and conjugacy problems for free products of two groups with amalgamation over a subgroup. We stratify the set of elements of the fre product with respect to the complexity of the word and conjugacy problems and show that for the generic stratum the conjugacy search problem is decidable under some reasonable assumptions about the groups groups involved. Moreover, the decision algorithm is fast on the generic stratum.
dJay: enabling high-density multi-tenancy for cloud gaming servers with dynamic cost-benefit GPU load balancing

In cloud gaming, servers perform remote rendering on behalf of thin clients. Such a server must deliver sufficient frame rate (at least 30fps) to each of its clients. At the same time, each client desires an immersive experience, and therefore the server should also provide the best graphics quality possible to each client. Statically provisioning time slices of the server GPU for each client suffers from severe underutilization because clients can come and go, and scenes that the clients need rendered can vary greatly in terms of GPU resource usage over time. In this work, we present dJay, a utility-maximizing cloud gaming server that dynamically tunes client GPU rendering workloads in order to 1) ensure all clients get satisfactory frame rate, and 2) provide the best possible graphics quality across clients. To accomplish this, we develop three main components. First, we build an online profiler that collects key cost and benefit data, and distills the data into a reusable regression model. Second, we build an online utility optimizer that uses the regression model to tune GPU workloads for better graphics quality. The optimizer solves the Multiple Choice Knapsack problem. We demonstrate dJay on two high quality commercial games, Doom 3 and Fable 3. Our results show that when compared to a static configuration, we can respond much better to peaks and troughs, achieving up to four times the multi-tenant density on a single server while offering clients the best possible graphics quality.