Miracle-dz/UniFAR-FTUs huggingface dataset:
Delayed Graft Function in Kidney Transplants: Time Evolution, Role of Acute Rejection, Risk Factors, and Impact on Patient and Graft Outcome

Background. Although numerous risk factors for delayed graft function (DGF) have been identified, the role of ischemia-reperfusion injury and acute rejection episodes (ARE) occurring during the DGF period is ill-defined and DGF impact on patient and graft outcome remains controversial. Methods. From 1983 to 2014, 1784 kidney-only transplantations from deceased donors were studied. Classical risk factors for DGF along with two novel ones, recipient's perioperative saline loading and residual diuresis, were analyzed by logistic regression and receiver operating characteristic (ROC) curves. Results. Along with other risk factors, absence of perioperative saline loading increases acute rejection incidence (OR = 1.9 [1.2–2.9]). Moreover, we observed two novel risk factors for DGF: patient's residual diuresis ≤500 mL/d (OR = 2.3 [1.6–3.5]) and absence of perioperative saline loading (OR = 3.3 [2.0–5.4]). Area under the curve of the ROC curve (0.77 [0.74–0.81]) shows an excellent discriminant power of our model, irrespective of rejection. DGF does not influence patient survival (P = 0.54). However, graft survival is decreased only when rejection was associated with DGF (P < 0.001). Conclusions. Perioperative saline loading efficiently prevents ischemia-reperfusion injury, which is the predominant factor inducing DGF. DGF per se has no influence on patient and graft outcome. Its incidence is currently close to 5% in our centre.
Suspending vetoes: how the euro countries achieved unanimity in the fiscal compact

ABSTRACT At the beginning of 2012, the 17 countries of the eurozone and eight of the ten remaining countries of the EU reached an agreement on the Treaty on Stability, Co-ordination, and Governance in the Economic and Monetary Union (TSCG, known as the Fiscal Compact). The article traces six successive drafts of the agreement to discover how these countries reached agreement. We argue that there are three different procedures that can lead different actors with veto power over an agreement to suspend their veto – they increase, decrease or preserve the dimensionality of the underlying space. We call the three methods compensation, elimination and compromise respectively, and discover that in the Fiscal Compact the agreement was achieved mainly though elimination.
Error-Resilient Scheme for Wavelet Video Codec Using Automatic ROI Detection and Wyner-Ziv Coding Over Packet Erasure Channel

The error-resilient for video transmission over the Internet in which regarded as the packet erasure channel is always a tough task and has gained lots of attentions. The main contradictory problem lies between error-resilient and bandwidth usage. Additional redundant data has to be added to achieve robust transmission which leads to huge bandwidth usage. In this paper, an error-resilient scheme called Wyner-Ziv Error-Resilient (WZER) based on a receiver driven layered Wyner-Ziv (WZ) coding framework is proposed. The WZER purposely emphasizes on the protection of the Region of Interest (ROI) area in the frame thus to achieve the better tradeoff between the bandwidth usage and error-resilience. WZER is designed to work for the scenario of wavelet based video coding over packet erasure channel, where several techniques including automatic ROI detection, ROI mask generation, Rate distortion optimization (RDO) quantization, WZ coding with layer design, and packet level Low Density Parity Check (LDPC) code are used. The performances of the proposed WZER are simulated based on average PSNR of luminance, perceptual reconstruction and bandwidth usage and compared with normal Forward Error Correction (FEC) full protection scheme and no protection scheme. The results show the advantages of the proposed WZER over traditional FEC protection, especially in the aspects of the recovery of the subject area and bandwidth efficiency.
Learning Models: An Assessment of Progress, Challenges and New Developments

Learning models extend the traditional discrete choice framework by postulating that consumers have incomplete information about product attributes, and that they learn about these attributes over time. In this survey we describe the literature on learning models that has developed over the past 20 years, using the model of Erdem and Keane (1996) as a unifying framework. We described how subsequent work has extended their modeling framework, and applied learning models to a wide range of different products and markets. We argue that learning models have contributed greatly to our understanding of consumer behavior, in particular in enhancing our understanding of brand loyalty and long run advertising effects. We also discuss the limitations of existing learning models and discuss potential extensions. One key challenge is to disentangle learning as a source of dynamics from other key mechanisms that may generate choice dynamics (inventories, habit persistence, etc.). Another is to enhance identification of learning models by collecting and utilizing direct measures of signals, perceptions and expectations.
Verifiable Data Redundancy in the Cloud

Data redundancy is critical for the assurance of service continuity in fault tolerant systems. Researchers have proposed several efficient data replication strategies to secure users' sensitive information from a single point of failure. Although the cloud service providers (CSP) assure users regarding data safety by following some of those replication strategies, the opaque operational model of the cloud does not allow the users to verify the replications. This, in turn, introduces a lack of trust in clouds. The challenge of verifiable data redundancy is two-fold: to verify that the server indeed possesses multiple copies, and to verify that those copies are not located on the same physical system. Current research mainly focuses on verification by access time of multiple requests which is highly system dependent. Hence, we propose a novel storage-as-a-service protocol that provides verifiable data redundancy in the semi or untrusted server. Our proposed model is completely oblivious of the physical system and the allocation protocol of the cloud. It does not include the server in the verification process which eliminates the risk of manipulation by a dishonest service provider. Our model uses distinct copies to store in the server as replicas and provides deterministic verification of having data redundancies in the server. For generating distinct copies of data, we use different random numbers in the ElGamal encryption system. After proposing the storage, access, and verification processes, we implemented a prototype system to analyze the performance of our proposed model. Our experimental results show that our system can successfully verify the redundancy of the data in the remote server and detect any service level agreement anomalies.
Formal semantics for perceptual classification

A formal semantics for low-level perceptual aspects of meaning is presented, tying these together with the logical-inferential aspects of meaning traditionally studied in formal semantics. The key idea is to model perceptual meanings as classifiers of perceptual input. Furthermore, we show how perceptual aspects of meaning can be updated as a result of observing language use in interaction, thereby enabling fine-grained semantic plasticity and semantic coordination. This requires a framework where intensions are (i) represented independently of extensions, and (ii) structured objects which can be modified as a result of learning. We use Type Theory with Records (TTR), a formal semantics framework that starts from the idea that information and meaning is founded on our ability to perceive and classify the world, i.e. to perceive objects and situations as being of types. As an example of our approach, we show how a simple classifier of spatial information based on the Perceptron can be
Phase transitions of orientifold gauge theories at large N in finite volume

In this paper we consider the phase structure of ``orientifold'' gauge theories — obtained from unitary supersymmetric gauge theories by replacing adjoint Majorana fermions by Dirac fermions in the symmetric or anti-symmetric representations—in finite volume S3 × S1. If the radius of the S3 is small the calculations can be performed at weak coupling for any value of the S1 radius. We demonstrate that there is a confinement/de-confining type of phase transition even when the fermions have periodic (non-thermal) boundary conditions around S1. At small radius of S1, the theory is in a phase where charge conjugation and large non-periodic gauge transformation are spontaneously broken. But for large radius of S1 the phase preseves these symmetries just as in the related supersymmetric theory.
Structural Domains and Matrix Attachment Regions along Colinear Chromosomal Segments of Maize and Sorghum

Although a gene’s location can greatly influence its expression, genome sequencing has shown that orthologous genes may exist in very different environments in the genomes of closely related species. Four genes in the maize alcohol dehydrogenase (adh1) region represent solitary genes dispersed among large repetitive blocks, whereas the orthologous genes in sorghum are located in a different setting surrounded by low-copy-number DNAs. A specific class of DNA sequences, matrix attachment regions (MARs), was found to be in comparable positions in the two species, often flanking individual genes. If these MARs define structural domains, then the orthologous genes in maize and sorghum should experience similar chromatin environments. In addition, MARs were divided into two groups, based on the competitive affinity of their association with the matrix. The “durable” MARs retained matrix associations at the highest concentrations of competitor DNA. Most of the durable MARs mapped outside genes, defining the borders of putative chromatin loops. The “unstable” MARs lost their association with the matrix under similar competitor conditions and mapped mainly within introns. These results suggest that MARs possess both domain-defining and regulatory roles. Miniature inverted repeat transposable elements (MITEs) often were found on the same fragments as the MARs. Our studies showed that many MITEs can bind to isolated nuclear matrices, suggesting that MITEs may function as MARs in vivo.
Joint Estimation of Default and Recovery Risk: A Simulation Study †

A joint modeling and estimation of recovery risk, default intensity risk, and interest rate risk has so far been a neglected issue in the empirical credit risk literature. This paper presents a framework in which all three types of risk can be modeled. It is argued that under the assumption of recovery of face value it is theoretically possible to jointly estimate both recovery and default intensity risk. Given simulated data, a model containing all three types of risk is estimated; the results show that default and recovery risk can in fact be separated.
Phenotypic Plasticity of the Introduced New Zealand Mud Snail, Potamopyrgus antipodarum, Compared to Sympatric Native Snails

Phenotypic plasticity is likely to be important in determining the invasive potential of a species, especially if invasive species show greater plasticity or tolerance compared to sympatric native species. Here in two separate experiments we compare reaction norms in response to two environmental variables of two clones of the New Zealand mud snail, Potamopyrgus antipodarum, isolated from the United States, (one invasive and one not yet invasive) with those of two species of native snails that are sympatric with the invader, Fossaria bulimoides group and Physella gyrina group. We placed juvenile snails in environments with high and low conductivity (300 and 800 mS) in one experiment, and raised them at two different temperatures (16°C and 22°C) in a second experiment. Growth rate and mortality were measured over the course of 8 weeks. Mortality rates were higher in the native snails compared to P. antipodarum across all treatments, and variation in conductivity influenced mortality. In both experiments, reaction norms did not vary significantly between species. There was little evidence that the success of the introduced species is a result of greater phenotypic plasticity to these variables compared to the sympatric native species.
Reliability Modelling of Disk Subsystems with Probabilistic Model Checking

We discuss how probabilistic model checking, a variant of model checking, can be useful in modelling multiple failures in disk subsystems, as it allows specification of probabilities or rates of transitions also. Probabilistic model checking can be used to not only check the correctness of formulae that are augmented with a probability operator, but also to compute, as a side effect, probabilities and Markov rewards in response to queries expressed in an expressive logic. With exponential distributions for failures, it is possible to accurately compute the reliability of many non-trivial disk systems. Exploiting the technique of symmetry reduction, it is possible to handle even larger fully symmetric systems. For non-exponential distributions, such as Weibull models of disk reliability, we can analyze reasonable sized systems (such as RAID4/5/6) if good approximations based on exponomials are possible. We report our experiences and compare our results with those from simulation.
Stability of Finite Difference Schemes for Hyperbolic Initial Boundary Value Problems

We study the stability of finite difference schemes for hyperbolic initial boundary value problems in one space dimension. Assuming stability for the dicretization of the hyperbolic operator as well as a geometric regularity condition, we show that the uniform Kreiss-Lopatinskii condition yields strong stability for the discretized initial boundary value problem. The present work extends results of Gustafsson, Kreiss, Sundstrom and a former work of ours to the widest possible class of finite difference schemes by dropping some technical assumptions. We give some new examples of numerical schemes for which our results apply.
Deep spatiotemporal LSTM network with temporal pattern feature for 3D human action recognition

With the rapid development of RGB‐D cameras and pose estimation techniques, action recognition based on three‐dimensional skeleton data has gained significant attention in the artificial intelligence community. In this paper, we incorporate temporal pattern descriptors of joint positions with the currently popular long short‐term memory (LSTM)–based learning scheme to obtain accurate and robust action recognition. Considering that actions are essentially formed by small subactions, we first utilize a two‐dimensional wavelet transform to extract temporal pattern descriptors in the frequency domain for each subaction. Afterward, we design a novel LSTM structure to extract deep features, which model a long‐term spatiotemporal correlation between body parts. Since temporal pattern descriptors and LSTM deep features can be regarded as multimodal representations for actions, we fuse them with an autoencoder network to achieve a more effective feature descriptor for action recognition. Experimental results on three challenging data sets with several comparative methods demonstrate the effectiveness of the proposed method for three‐dimensional action recognition.
MPPT controller for a photovoltaic power system based on increment conductance approach

This paper deals with the power optimization problem in photovoltaic generators. Indeed the nonlinear characteristic of photovoltaic generators [1], leads to search for a solution to track the maximum power provided by the PV array in order to exploit well the energy. A maximum power point tracking algorithm based on an approach called increment conductance control of photovoltaic systems is well detailed in this paper. It provides good performance against the different variation of climatic conditions and load. Simulation results prove that this algorithm can effectively attend the maximum photovoltaic power's production.
A method for obstacle avoidance in role reassignment of robot formation control

In the paper, an algorithm for the obstacle avoidance and a procedure for the role reassignment of robot formation control based on this algorithm are proposed. This algorithm contains an edge detector that is used to determine the position of the vertical edges of the obstacle in the environment. Meanwhile, a motion controller is also designed in this research based on the dynamic equation for robots. The developed algorithm is applied to the formation control of omni-directional driven robots. Simulation and experiment are performed to verify the proposed algorithm and the results show that the performance of the proposed algorithm for obstacle avoidance and procedure for role reassignment are efficient for robot formation control.
Multiple Graph-Kernel Learning

Kernels for structures, including graphs, generally suffer of the diagonally dominant gram matrix issue, the effect by which the number of sub-structures, or features, shared between instances are very few with respect to those shared by an instance with itself. A parametric rule is typically used to reduce the weights of largest (more complex) sub-structures. The particular rule which is adopted is in fact a strong external bias that may strongly affect the resulting predictive performance. Thus, in principle, the applied rule should be validated in addition to the other hyper-parameters of the kernel. Nevertheless, for the majority of graph kernels proposed in literature, the parameters of the weighting rule are fixed a priori. The contribution of this paper is two-fold. Firstly, we propose a Multiple Kernel Learning (MKL) approach to learn different weights of different bunches of features which are grouped by complexity. Secondly, we define a notion of kernel complexity, namely Kernel Spectral Complexity, and we show how this complexity relates to the well-known Empirical Rademacher Complexity for a natural class of functions which include SVM. The proposed approach is applied to a recently defined graph kernel and evaluated on several real-world datasets. The obtained results show that our approach outperforms the original kernel on all the considered tasks.
A study of minimum classification error training for segmental switching linear Gaussian hidden Markov models

In our previous works, a Switching Linear Gaussian Hidden Markov Model (SLGHMM) and its segmental derivative, SSLGHMM, were proposed to cast the problem of modelling a noisy speech utter-ance by a well-designed dynamic Bayesian network. We presented parameter learning procedures for both models with maximum likelihood (ML) criterion. The effectiveness of such models was con ﬁ rmed by evaluation experiments on Aurora2 database. In this paper, we present a study of minimum classi ﬁ cation error (MCE) training for SSLGHMM and discuss its relation to our earlier proposals based on stochastic vector mapping. An important implementation issue of SSLGHMM, namely the speci ﬁ cation of switching states for a given utterance, is also studied. New evaluation results on Aurora3 database show that MCE-trained SSLGH-MMs achieve a relative error reduction of 21% over a baseline sys-tem based on ML-trained continuous density HMMs (CDHMMs).
University of Birmingham Dynamic Modelling of Tactics Impact on the Stability of Self-aware Cloud Architectures

—Given the elasticity, on-demand nature, and run-time dynamics of the cloud, a stable self-adaptive architecture should keep the fulﬁlment of Quality of Service objectives stable, while performing stable adaptations that converge towards these objectives. The dynamic management and selection of architectural tactics, as adaptation mechanisms, shall be in the heart of the adaptation process, as being essential for effective and stable adaptations. This calls for measuring the impact of tactics on the stability of inter-related quality attributes during run-time. In this paper, we introduce a Markovian-based analytical model for dynamically assessing the impact of tactics on the stability behaviour of self-adaptive cloud architectures. The model also employs self-awareness capabilities for better-informing the selection of optimal tactics conﬁgurations leading to stability. Experimental evaluations have shown the accuracy and efﬁciency of the model in measuring and predicting the impact of tactics on stabilising the Quality of Service provision and the adaptation process.
Long-Distance Axon Regeneration in the Mature Optic Nerve: Contributions of Oncomodulin, cAMP, and pten Gene Deletion

The inability of retinal ganglion cells (RGCs) to regenerate damaged axons through the optic nerve has dire consequences for victims of traumatic nerve injury and certain neurodegenerative diseases. Several strategies have been shown to induce appreciable regeneration in vivo, but the regrowth of axons through the entire optic nerve and on into the brain remains a major challenge. We show here that the induction of a controlled inflammatory response in the eye, when combined with elevation of intracellular cAMP and deletion of the gene encoding pten (phosphatase and tensin homolog), enables RGCs to regenerate axons the full length of the optic nerve in mature mice; approximately half of these axons cross the chiasm, and a rare subset (∼1%) manages to enter the thalamus. Consistent with our previous findings, the axon-promoting effects of inflammation were shown to require the macrophage-derived growth factor Oncomodulin (Ocm). Elevation of cAMP increased the ability of Ocm to bind to its receptors in the inner retina and augmented inflammation-induced regeneration twofold. Inflammation combined with elevated cAMP and PTEN deletion increased activation of the phosphatidylinositol 3-kinase and mitogen-activated protein kinase signaling pathways and augmented regeneration ∼10-fold over the level induced by either pten deletion or Zymosan alone. Thus, treatments that synergistically alter the intrinsic growth state of RGCs produce unprecedented levels of axon regeneration in the optic nerve, a CNS pathway long believed to be incapable of supporting such growth.
Parallel Corpora Segmentation Using Anchor Words

A new technique for monotone segmentation of parallel corpora is introduced. This segmentation is based on a set of anchor words which are defined manually. The parallel segments are computed using a dynamic programming algorithm. To assess this technique, finite-state transducers are inferred from both non-segmented and segmented corpora. Experiments have been carried out with Spanish-English and Italian-English translation tasks. This technique has proven useful in improving the results with respect to those obtained with unsegmented corpora.
An adaptive network intrusion detection approach for the cloud environment

As Internet attacks grow rapidly, firewalls or network intrusion systems are indispensable. Existing approaches usually use attack signatures, machine learning or data mining algorithms to detect and stop anomaly or malicious flow. Machine learning algorithms need a set of labeled data to train the detection model, while the labeled data set is not always available. In this paper, we proposed an anomaly detection approach that is adaptive to the ever-changing network environment. The approach constructs a decision tree-based detection model for intrusion detection from unlabeled data by using an unsupervised learning algorithm called spectral clustering. And the system can easily be deployed on the cloud environment. In the experiments with the DARPA 2000 data set and the KDD Cup 1999 data set, our system shows notable improvement on the detection performance after the adaptation procedure.