Miracle-dz/UniFAR-FTUs huggingface dataset:
A generative solution for ATM cash management

Optimizing cash in automatic teller machines (ATM) is challenging due unpredictability of withdrawals, but profitable because of the large number of tellers. Generally ATM cash management and optimization is performed manually, according to corporate policies and personnel experience. A non-optimal cash upload can lead to poor service when cash demand is underestimated and to unnecessary costs when demand is overestimated. Therefore, finding the best match between cash stock and demand becomes crucial to improve. Recently, some authors attempted to optimize the cash by modeling and forecasting the demand. However, the high variance and non-stationarity of the underlying stochastic process can affect reliability of such an approach. In this paper we suggest the application of genetic algorithms as means for searching and generating optimal upload strategies, able at the same time to minimize the daily amount of stocked money and to assure cash dispensing service. Experimentation led at Poste Italiane S.p.A. makes this promising and worth to be further investigated.
Comparing two approaches to nonlinear time-delayed 1st-order plants control

The paper introduces two new approaches to control of simple but frequently considered nonlinear plants with a time-delayed dominant first-order dynamics. To guarantee a consistent performance over a broader operating range, in dealing with nonlinear plants, typically different gain scheduling approaches may be used as e.g. the exact linearization, velocity linearization, flatness-based model-free PID control, control based on generalized transfer functions to nonlinear systems, etc. By considering example of a time delayed one-tank liquid level control in a hydraulic plant the paper introduces a new alternatives to these traditional approaches based on a generalization of the exact linearization by using two types of linearized plant models. They allow to consider unmodelled dynamics and a nonlinear predictive disturbance-observer (NPDO) based integral action. The resulting two-degree-of-freedom model-based control shows new possibilities in designing transients with required monotonicity properties and different degrees of optimization.
PORTFOLIO OPTIMIZATION IN A DEFAULT MODEL UNDER FULL/PARTIAL INFORMATION

In this paper, we consider a financial market with an asset exposed to a risk inducing a jump in the asset price, and which can still be traded after the default time. We use a default-intensity modeling approach, and address in this incomplete market context the problem of maximization of expected utility from terminal wealth for logarithmic, power and exponential utility functions. We study this problem as a stochastic control problem both under full and partial information. Our contribution consists of showing that the optimal strategy can be obtained by a direct approach for the logarithmic utility function, and the value function for the power (resp. exponential) utility function can be determined as the minimal (resp. maximal) solution of a backward stochastic differential equation. For the partial information case, we show how the problem can be divided into two problems: a filtering problem and an optimization problem. We also study the indifference pricing approach to evaluate the price of a contingent claim in an incomplete market and the information price for an agent with insider information.
Measurement problems in high-speed networks

Computer hardware and communications network speeds are rising at a steady rate. From Moore's law we know that the silicon computing power is doubled every 18 months, and especially with the onset of the Internet, the demand for increasing network bandwidth is also on the constant rise. CPU speeds have recently broken through the 1 GHz barrier, and 10 Gbit/sec network connections are becoming common. With the faster clock signal and shrinking silicon dimensions, digital transitions become faster, and presently sub-100 psec transition times are measured. To save power, but also as a need from the smaller silicon feature sizes, digital voltage swings are becoming smaller. The gigabit signaling techniques combined with dense printed-circuit-board wiring create on-board transmission lines where losses and dispersion must be taken into account. Finally, the low operating voltages and highly supply currents create a need for power-distribution impedances in the milliohm range. As a result, measuring and simulating the signals with several GHz bandwidth verifying trace and cable losses and transfer characteristics, measuring power-distribution network impedances creates new challenges. The paper gives an overview of the state-of-the-art measurement solutions for the above fields.
On Enhancing Revenue of Wireless Video Multicast

Video multicast exploits the wireless broadcast nature to transmit a video stream to multiple clients with a minimum bandwidth requirement. Assigning suitable transmission bit-rate to video stream is however a challenging problem because clients in a wireless network have heterogeneous channel quality and experience different packet loss probability. Existing video multicast researches have proposed several protocols in scalable video multicast and users can receive different numbers of video layers to match their channel conditions. However, most of them formulate the rate scheduling problem based on user prospect, e.g., Throughput or Peak Signal to Noise Ratio (PSNR) [1] maximization. In this paper, we focus on service provider prospect, e.g., Revenue from user payment, and define two possible payment rules for SVC multicast. Based on the payment rules, we propose respective protocols that consider both layer and inter-frame dependency and assign each block in SVC a proper bit rate to maximize the provider revenue. The simulation results show that, compared with other multicast schemes, our proposed scheme has the best performance on service provider revenue in most scenarios.
How reliable are the functional connectivity networks 1 of MEG in resting states ? 2 Running head : Reliability of FC networks in resting states

19 We investigated the reliability of nodal network metrics of functional connectivity (FC) 20 networks of MEG covering the whole brain at the sensor level in the eyes-closed (EC) and 21 eyes-open (EO) resting states. Mutual information (MI) was employed as a measure of FC 22 between sensors in theta, alpha, beta, and gamma frequency bands of MEG signals. MI matrices 23 were assessed with three nodal network metrics, i.e., nodal degree (Dnodal), nodal efficiency 24 (Enodal), and betweenness centrality (normBC). Intra-class correlation (ICC) values were 25 calculated as a measure of reliability. We observed that the test-retest reliabilities of the resting 26 states ranged from a poor to good level depending on the bands and metrics used for defining the 27 nodal centrality. The dominant alpha band FC network changes were the salient features of the 28 state-related FC changes. The FC networks in the EO resting state showed greater reliability 29 when assessed by Dnodal (maximum mean ICC=0.655) and Enodal (maximum mean 30 ICC=0.604) metrics. The gamma band FC network was less reliable than the theta, alpha, and 31 beta networks across the nodal network metrics. However, the sensor-wise ICC values for the 32 nodal centrality metrics were not uniformly distributed, that is, some sensors had high reliability. 33 This study provides a sense of how the nodal centralities of the human resting state MEG are 34 distributed at the sensor level and how reliable they are. It also provides a fundamental scientific 35 background for continued examination of the resting state of human MEG.
Optimal Precoder Design for Correlated MIMO Communication Systems Using Zero-Forcing Decision Feedback Equalization

We consider the design of the precoder for a multi-input multi-output (MIMO) communication system equipped with a decision feedback equalizer (DFE) receiver. For such design problems, perfect knowledge of the channel state information (CSI) at both the transmitter and the receiver is usually required. However, in the environment of wireless communications, it is often difficult to provide sufficiently timely and accurate feedback of CSI from the receiver to the transmitter for such designs to be practically viable. In this paper, we consider the optimum design of a precoder for a wireless communication link having M transmitter antennas and N receiver antennas (M < N), in which the channels are assumed to be flat fading and may be correlated. We assume that full knowledge of CSI is available at the receiver. At the transmitter, however, only the first- and second-order statistics of the channels are available. Our goal here is to come up with an efficient design of the optimal precoder for such a MIMO system by minimizing the average arithmetic mean-squared error (MSE) of zero-forcing (ZF) decision feedback detection subject to a constraint on the total transmitting power. Applying some of the properties of the matrix parameters, this nonconvex optimization problem can be transformed into a convex geometrical programming problem which can then be efficiently solved using an interior point method. The performance of the MIMO system equipped with this optimum precoder and a ZF-DFE has also been found to be comparable, and in some cases, superior to that of V-BLAST which necessitates optimally ordered successive interference cancellation based on the largest postdetection signal-to-noise ratio (SNR). In terms of tradeoff between performance and implementation simplicity, our new system is certainly an attractive alternative.
The response of a turbulent accretion disc to an imposedepicyclic shearing

We excite an epicyclic motion, whose amplitude depends on the vertical position, z, in a simulation of a turbulent accretion disc. An epicyclic motion of this kind can be caused by a warping of the disc. By studying how the epicyclic motion freely decays we can gain information on the interaction between the warp and the disc turbulence. A high amplitude epicyclic motion decays rst by exciting inertial waves through a parametric instability, but its subsequent exponential damping can be reproduced by a turbulent viscosity. We estimate the eeective viscosity parameter, v , pertaining to such a vertical shear. We also gain new information on the properties of the disc turbulence in general, and measure the usual viscosity parameter, h , pertaining to a horizontal (Keplerian) shear. We nd that, as is often assumed in theoretical studies, v is approximately equal to h and both are much less than unity, for the eld strengths achieved in our local box calculations of turbulence. In view of the smallness (0:01) of v and h we conclude that for = p gas =p mag 10 the timescale for diiusion or damping of a warp is much shorter than the usual viscous timescale. Finally, we review the astrophysical implications.
Characterization of 4×4 high gain microstrip array antenna for 3.3GHz WiMAX application

This paper presents the design of microstrip array antenna for 3.3GHz WiMAX (Worldwide Interoperability Mobile Access) application. The proposed antenna comprises of 16 rectangular patches with microstrip line feeding network established using a quarter-wavelength transformer impedance matching technique. To avoid grating lobes affected by the spacing between patches, the patch separation to the adjacent patches is set to be 0.6 of wavelength. Hence, in order to achieve high gain required by the WiMAX specifications, the array consisted of 16 patches is applied where the patches are structured in 4×4 (columns x rows) formation. The antenna structure is then deployed on an Arlon DiClad 527 substrate which the thickness, dielectric constant and loss tangent are 1.6mm, 2.5 and 0.0022, respectively. The characterization result of return loss, VSWR (Voltage Standing Wave Ratio), radiation pattern, and gain are presented consecutively. From the result, it shows that the proposed antenna has the overall gain of 16.02dB and the return loss of 29.85dB that corresponds to VSWR of 1.07 at center frequency of 3.35GHz, whilst the working bandwidth is 150MHz ranges from 3.25–3.40GHz.
Enhancement of the Digital Fortune Line System for Accelerating Mutual Examination of Learners ’ Conceptual Ecologies

We developed the Digital Fortune Line System for supporting the externalization and sharing of learners' multiple ideas and their commitment to those ideas. We used the Conceptual Ecology Model [4] as a theoretical base, and mobile and wireless network technology as a technological base. Based on the results of the evaluation research of the previous version of the system, this research newly added the name display function and the line display function in order to enhance the system, especially the counting function. These functions were added in order to support mutual examination of learners’ conceptual ecologies by enabling learners to instantly identify targets of questions and counterarguments, and by seamlessly coordinating the summary screen and the fortune line display screen. After implementing the new version system in the same science curriculum used in the evaluation research of the previous version system and analyzing the subjective evaluation of the system by 37 fourth-graders (10 or 11 years old), it has been revealed that the new version system will support the mutual examination more effectively than the previous version system.
Developing lightweight context-aware service mashup applications

As web services increase in popularity, the number of available services expands and they become more dynamic. Thus, it is increasingly important to allow the dynamic development of mashups on demand for a given context. To allow mobile users convenient access to the necessary services requires simple and lightweight context-aware methods to discover, select, and compose these services. In this paper, we introduce a model of a mashup page with a JavaScript wrapper that is generated automatically without user intervention. MyServices, the mashup construction system, allows service providers to register context-aware selection rules. Moreover, the JavaScript codes generated by MyServices can run in any browser environment and support navigation between services with minimal user interaction.
Stock Market Returns : A Temperature Anomaly ∗

This study investigates if stock market returns are related to the temperature. Psychological research has shown that temperature is one of the meteorological variables that significantly affect people’s mood. Mood changes in turn lead to behavioral changes. It is known that lower temperatures can lead to aggression, while higher tempratures can lead to aggression as well as apathy. Aggression could result in more risk-taking while apathy could impede risktaking. We therefore expect lower temperatures to be related to higher stock returns and higher temperatures to be related to higher or lower stock returns, depending on the trade-off between the two competing effects. We examine the potential linkage between temperatures and stock market returns for eight international stock exchanges. Our analysis reveals that lower temperatures are indeed related to higher stock returns, and higher temperatures are related to lower stock returns. The relationship is significant both statistically and economically when the temperature is low, and is robust to various altervative tests.
String kernels and similarity measures for information retrieval

Measuring a similarity between two strings is a fundamental step in many applications in areas such as text classification and information retrieval. Lately, kernel-based methods have been proposed for this task, both for text and biological sequences. Since kernels are inner products in a feature space, they naturally induce similarity measures. Information-theoretical approaches have also been subject of recent research. The goal is to classify finite sequences without explicit knowledge of their statistical nature: sequences are considered similar if they are likely to be generated by the same source. There is experimental evidence that relative entropy (albeit not being a true metric) yields high accuracy in several classification tasks. Compression-based techniques, such as variations of the Ziv-Lempel algorithm for text, or GenCompress for biological sequences, have been used to estimate the relative entropy. Algorithmic concepts based on the Kolmogorov complexity provide theoretic background for these approaches. This paper describes some string kernels and information theoretic methods. It evaluates the performance of both kinds of methods in text classification tasks, namely in the problems of authorship attribution, language detection, and cross-language document matching.
Regulation and function of P-Rex family Rac-GEFs

The P-Rex family are Dbl-type guanine-nucleotide exchange factors for Rac family small G proteins. They are distinguished from other Rac-GEFs through their synergistic mode of activation by the lipid second messenger phosphatidyl inositol (3,4,5) trisphosphate and the Gβγ subunits of heterotrimeric G proteins, thus acting as coincidence detectors for phosphoinositide 3-kinase and G protein coupled receptor signaling. Work in genetically-modified mice has shown that P-Rex1 has physiological importance in the inflammatory response and the migration of melanoblasts during development, whereas P-Rex2 controls the dendrite morphology of cerebellar Purkinje neurons as well as glucose homeostasis in liver and adipose tissue. Deregulation of P-Rex1 and P-Rex2 expression occurs in many types of cancer, and P-Rex2 is frequently mutated in melanoma. Both GEFs promote tumor growth or metastasis. This review critically evaluates the P-Rex literature and tools available and highlights exciting recent developments and open questions.
A new approach for identifying noisy input-output FIR models

This paper proposes an efficient algorithm for identifying FIR models when also the input is assumed as affected by additive noise. This procedure is more accurate than instrumental variables approaches and, differently from total least squares, does not require the a priori knowledge of the ratio between the input and output noise variances. The accuracy of the whole procedure has been tested by means of Monte Carlo simulations and compared with that of compensated and total least squares ones.
DIABETIC NEUROPATHY: DIAGNOSTIC METHODS —

Screening and diagnostic testing for neuropathy in patients with type 1 or type 2 diabetes is needed in order to prevent complications from diabetic neuropathy. As diabetic neuropathy frequently leads to foot ulcers and amputation— major causes of morbidity and disability in people with diabetes—the American Diabetes Association recommends an annual foot exam for people with diabetes in order to identify those with high-risk foot conditions. Yet, detection and diagnosis of diabetic neuropathy can be complex. The diagnosis of neuropathy is based on both clinical and objective measures. Medical and neurological history, physical and neurological examination (evaluating sensory, motor, reflex, and autonomic function), and measurement of peripheral nerve function by clinical testing have been used and then combined into a series of clinical assessment scores that screen for and quantify the severity of diabetic neuropathy. No fewer than 12 clinical assessment scoring systems are available. This article presents the tools and methods commonly used to screen and diagnose neuropathy in patients with diabetes and discusses issues surrounding their use. (Adv Stud Med. 2004;4(8A):S650-S661) D
14-3-3 proteins function in the initiation and elongation steps of DNA replication in Saccharomyces cerevisiae

14-3-3s are highly conserved abundant eukaryotic proteins essential for viability, at least in lower eukaryotes. We previously showed that they associate with mammalian and yeast replication origins in a cell-cycle-dependent manner, and are involved in the initiation of DNA replication. Here, we present evidence that 14-3-3 proteins are novel regulators of the initiation and elongation steps of DNA replication in Saccharomyces cerevisiae. The results show that the Bmh2 protein, one of the two 14-3-3 homologues in S. cerevisiae, interacts with Mcm2 and Orc2 proteins, binds to ARS1 maximally at the G1 phase, is essential for plasmid stability, and is required for normal S-phase entry and progression. Furthermore, during G1 phase, the Bmh2 protein is required for the association of MCM proteins with chromatin and their maintenance at replication origins. The results reveal that 14-3-3 proteins function as essential factors for the assembly and maintenance of the pre-replication complex during G1 phase.
Secure Data Exchange: A Marketplace in the Cloud

A vast amount of data belonging to companies and individuals is currently stored in the cloud in encrypted form by trustworthy service providers such as Microsoft, Amazon, and Google. Unfortunately, the only way for the cloud to use the data in computations is to first decrypt it, then compute on it, and finally re-encrypt it, resulting in a problematic trade-off between value/utility and security. At a high level, our goal in this paper is to present a general and practical cryptographic solution to this dilemma. More precisely, we describe a scenario that we call Secure Data Exchange (SDE), where several data owners are storing private encrypted data in a semi-honest non-colluding cloud, and an evaluator (a third party) wishes to engage in a secure function evaluation on the data belonging to some subset of the data owners. We require that none of the parties involved learns anything beyond what they already know and what is revealed by the function, even when the parties (except the cloud) are active malicious. We also recognize the ubiquity of scenarios where the lack of an efficient SDE protocol prevents for example business transactions, research collaborations, or mutually beneficial computations on aggregated private data from taking place, and discuss several such scenarios in detail. Our main result is an efficient and practical protocol for enabling SDE using Secure Multi-Party Computation (MPC) in a novel adaptation of the server-aided setting. We also present the details of an implementation along with performance numbers.
Myelin instability and oligodendrocyte metabolism in myelin-deficient mutant mice

During the active phase of myelination in myelin-deficient mutant mice (mld), myelin basic protein (MBP) synthesis is defective and the myelin lamellae are uncompacted. In these mutants, we found a fast metabolism of the myelin-associated glycoprotein (MAG) and of sulfatides, and the presence of cholesterol esters and a degradation product of MAG, dMAG, indicating that mld myelin was unstable. The increased synthesis of MAG and Wolfgram protein, two proteins present in uncompacted myelin sheath and paranodal loops, was demonstrated by high levels of messengers. Simultaneously, we found an accumulation of inclusion bodies, vacuoles, and rough endoplasmic reticulum in mld oligodendrocytes. This material was heavily immunostained for MAG. Furthermore, the developmental change between the two molecular forms of MAG (p72MAG/p67MAG) was delayed in mld mice. In 85-d-old mld mice, the MBP content increased and myelin lamellae became better compacted. In these mutants, dMAG was absent and MAG mRNAs were found in normal amounts. Furthermore, the fine structure of mld oligodendrocytes was normal and the MAG immunostaining was similar to age-matched controls. These results support a functional role for MBP in maintaining the metabolic stability and the compact structure of myelin. Furthermore, in the absence of MBP and myelin compaction, the regulation of the synthesis of at least two membrane proteins related to myelin cannot proceed.