Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification and validation of lateral driver models on experimentally induced driving behavior

This paper presents a real-world driving experiment with aim on controlled variation of steering and lane keeping behavior and investigates the ability of three common driver models to distinguish variations in driving performance. Nine drivers executed a lane keeping task with visual occlusion of the upper or lower field of view restraining them to near or far road scene information. Three common driver models are applied to replicate driving behavior. An autoregressive model with exogeneous input (ARX) is identified using vehicle lateral lane deviation as input and steering wheel angle as output. Two output error models are identified using vehicle heading deviation angles with respect to near and far preview points as respective inputs and steering wheel angle as output. The results show that the driving behaviors induced in the experiment are significantly different in terms of lane keeping performance. In simulations, the output error models exhibit advantages over the ARX model in capturing driving behavior. However, the model natural frequency and the model simulation error show weak performance in discerning this varying driving behavior and are largely determined by track effects.
A total variation-based reconstruction method for dynamic MRI

In recent years, total variation (TV) regularization has become a popular and powerful tool for image restoration and enhancement. In this work, we apply TV minimization to improve the quality of dynamic magnetic resonance images. Dynamic magnetic resonance imaging is an increasingly popular clinical technique used to monitor spatio-temporal changes in tissue structure. Fast data acquisition is necessary in order to capture the dynamic process. Most commonly, the requirement of high temporal resolution is fulfilled by sacrificing spatial resolution. Therefore, the numerical methods have to address the issue of images reconstruction from limited Fourier data. One of the most successful techniques for dynamic imaging applications is the reduced-encoded imaging by generalized-series reconstruction method of Liang and Lauterbur. However, even if this method utilizes a priori data for optimal image reconstruction, the produced dynamic images are degraded by truncation artifacts, most notably Gibbs ringing, due to the spatial low resolution of the data. We use a TV regularization strategy in order to reduce these truncation artifacts in the dynamic images. The resulting TV minimization problem is solved by the fixed point iteration method of Vogel and Oman. The results of test problems with simulated and real data are presented to illustrate the effectiveness of the proposed approach in reducing the truncation artifacts of the reconstructed images.
3D Trajectory Reconstruction of the Soccer Ball for Single Static Camera Systems

The interest on acquiring player and ball data during soccer games is increasing in several domains such as media or training. Consequently, tracking systems are becoming widely used for live data gathering. However, due to costs, stadium infrastructure, media rights etc. there is a trend for stand-alone mobile low-cost soccer tracking systems. The drawback of such systems is that generally only low-resolution images of the players are available. Thus, the problem of detection and tracking the soccer ball is also strongly exacerbated. This paper presents a novel approach for 3D reconstruction of the ball trajectory from monocular lowresolution soccer image sequences. The ball detection is done on accumulated motion-segmented binary images by extracting shape-validated ball candidates from these so-called motion history images. Then, robust ball tracklets are extracted from the motion history images and used to reconstruct the ball 3D trajectory based on physical characteristics and calibration information. The approach is tested on a Bundesliga data set: the tracklet extraction and the ball trajectory reconstruction are evaluated.
Limited assignments: a new cutoff strategy for incomplete depth-first search

In this paper, we propose an extension of three incomplete depth-first search techniques, namely depth-bounded backtrack search, credit search, and iterative broadening, towards producing incomplete solutions. We also propose a new cutoff strategy for incomplete depth-first search motivated by a human style of problem solving. This technique, called limited assignment number (LAN) search, is based on limiting the number of attempts tried to assign a value to the variable. A linear worst-case time complexity of LAN Search leads to promising stable time behavior in all accomplished experiments. The techniques are studied in the context of constraint satisfaction problems.
pluto: The CRDT-Driven IMAP Server

A typical cloud deployment of an IMAP service follows the service-statelessness principle, i.e. a load balancer distributes the requests to a mostly stateless backend that stores application state on a network file system like NFS. Within this work we suggest an alternative architecture allowing the backend to maintain its own replica of application state, leading to shorter response times and increased fault tolerance. To ensure convergence among replicas, we use CRDTs to model application state and evaluate the performance of our prototypical implementation against the de facto standard IMAP server Dovecot in multiple experimentation settings.
Convex Optimization and Feasible Circulant Matrix Embeddings in Synthesis of Stationary Gaussian Fields

Circulant matrix embedding is one of the most popular and efficient methods for the exact generation of Gaussian stationary univariate series. Although the idea of circulant matrix embedding has also been used for the generation of Gaussian stationary random fields, there are many practical covariance structures of random fields where classical embedding methods break down. In this work, we propose a novel methodology that adaptively constructs feasible circulant embeddings based on convex optimization with an objective function measuring the distance of the covariance embedding to the targeted covariance structure over the domain of interest. The optimal value of the objective function will be zero if and only if there exists a feasible embedding for the a priori chosen embedding size.
The present day mass function in the central region of the Arches cluster

We study the evolution of the mass function in young and dense star clusters by means of direct N-body simulations. Our main aim is to explain the recent observations of the relatively flat mass function observed near the centre of the Arches star cluster. In this region, the power-law index of the mass function for stars more massive than about 5–6 M⊙ is larger than the Salpeter value by about unity, whereas further out, and for the lower mass stars, the mass function resembles the Salpeter distribution. We show that the peculiarities in the Arches mass function can be explained satisfactorily without primordial mass segregation. We draw two conclusions from our simulations: (i) the Arches initial mass function is consistent with a Salpeter slope down to ∼1 M⊙, and (ii) the cluster is about half-way towards core collapse. The cores of other star clusters with characteristics similar to those of the Arches are expected to show similar flattening in the mass functions for the high-mass (≳5 M⊙) stars.
Role of endogenous nitric oxide in airway microvascular leakage induced by inflammatory mediators.

This study examines the role of endogenous nitric oxide (NO) in airway microvascular leakage induced inflammatory mediators, which play an important role in asthmatic airways. Guinea-pigs were anesthetized and mechanically-ventilated with monitoring of arterial blood pressure, and airway microvascular leakage induced by intravenous injection of substance P (SP), leukotriene D4 (LTD4) and histamine was evaluated using Evans blue dye and Monastral blue dye in the presence and absence of the NO synthase inhibitors, L-NG-nitroarginine methyl ester (L-NAME) and L-NG-monomethyl arginine (L-NMMA). The effect of a soluble guanylate cyclase inhibitor, LY83583, on SP-induced dye leakage was also examined. Intravenous injection of SP (1 microgram.kg-1), LTD4 (1 microgram.kg-1) and histamine (100 micrograms.kg-1) significantly increased dye extravasation at all airway levels. Pretreatment with L-NAME (10 mg.kg-1 i.v.) and L-NMMA (100 mg.kg-1 i.v.) significantly inhibited SP-induced extravasation, and L-arginine (100 mg.kg-1 i.v.) reversed L-NAME-induced inhibition. L-NAME (10 mg.kg-1 i.v.) also significantly inhibited LTD4-induced dye extravasation only in central airways, and this inhibitory effect was abolished by a neurokinin-1 (NK1) antagonist, FK888 (10 mg.kg-1 i.v.) pretreatment. Histamine-induced dye extravasation was not affected by L-NAME. LY83583 (2.5 and 7.5 mg.kg-1 i.v.) partially but significantly reduced SP-induced dye leakage. These results suggest that endogenous nitric oxide plays a role in neurokinin-1 receptor-mediated airway microvascular leakage, and presumably involves the guanylate cyclase pathway.
Sum rate maximizing multigroup multicast beamforming under per-antenna power constraints

A multi-antenna transmitter that conveys independent sets of common data to distinct groups of users is herein considered, a model known as physical layer multicasting to multiple co-channel groups. In the recently proposed context of per-antenna power constrained multigroup multicasting, the present work focuses on a novel system design that aims at maximizing the total achievable throughput. Towards increasing the system sum rate, the available power resources need to be allocated to well conditioned groups of users. A detailed solution to tackle the elaborate sum rate maximizing, multigroup multicast problem, under per-antenna power constraints is therefore derived. Numerical results are presented to quantify the gains of the proposed algorithm over heuristic solutions. The solution is applied to rayleigh as well as Vandermonde channels. The latter case is typically realised in uniform linear array transmitters operating in the far field, where line-of-sight conditions are realized. In this setting, a sensitivity analysis with respect to the angular separation of co-group users is included. Finally, a simple scenario providing important intuitions for the sum rate maximizing multigroup multicast solutions is elaborated.
Structural Breaks and Unit Root Tests for Short Panels

In this paper we suggest panel data unit root tests which allow for a potential structural break in the individual e¤ects and/or the trends of each series of the panel, assuming that the time-dimension of the panel, T , is . xed. The proposed test statistics consider for the case that the break point is known and for the case that it is unknown. Monte Carlo evidence suggests that they have size which is very close to the nominal . ve percent level and power which is analogues to that of Harris and Tzavalis (1999) test statistics, which do not allow for a structural break. JEL classi.cation : C22, C23
A Hybrid Rule/Model-Based Finite-State Framework for Normalizing SMS Messages

In recent years, research in natural language processing has increasingly focused on normalizing SMS messages. Different well-defined approaches have been proposed, but the problem remains far from being solved: best systems achieve a 11% Word Error Rate. This paper presents a method that shares similarities with both spell checking and machine translation approaches. The normalization part of the system is entirely based on models trained from a corpus. Evaluated in French by 10-fold-cross validation, the system achieves a 9.3% Word Error Rate and a 0.83 BLEU score.
Recommending academic papers via users' reading purposes

The past decades have witnessed the rapid development of academic research, which results in a growing number of scholarly papers. As a result, paper recommender systems have been proposed to help researchers find their interested papers. Most previous studies in paper recommendations mainly concentrate on paper-paper or user-paper similarities without taking users' reading purposes into account. It is common that different users may prefer to different aspects of a paper, e.g., the focused problem/task or the proposed solution. In this paper, we propose to satisfy user-specific reading purposes by recommending the most problem-related papers or solution-related papers to users separately. For a target paper, we use the paper citation graph to generate a set of potential relevant papers. Once getting the candidate set, we calculate the problem-based similarities and solution-based similarities between candidates and the target paper through a concept based topic model, respectively. We evaluate our models on a real academic paper dataset and our experiments show that our approach outperforms a traditional similarity based model and can provide highly relevant paper recommendations according to different reading purposes for researchers.
Iterative Multi-Branch Lattice Reduction-Aided Successive Interference Cancellation for Multiuser MIMO Systems

In this paper, we propose an iterative detection and decoding (IDD) scheme for the uplink of coded multiuser multiple-input multiple-output (MU-MIMO) systems. The structure of the proposed soft-input soft-output (SISO) iterative (turbo) scheme is based on a multi-branch lattice reduction successive interference cancellation (MB-LR-SIC) strategy that computes in the first iteration a reliable extrinsic information and then feeds it to the decoder. After the first iteration, the soft cancellation minimum mean square error (SC-MMSE) strategy is used to subtract the residual interference. Simulations show that the proposed scheme has an excellent performance as compared to prior art and approach the single user (SU) scenario with only one transmit antenna which is free from interference. Keywords—Multiuser MIMO systems; Iterative detection and decoding receiver; Multi-branch lattice reduction sequence interference cancellation detection.
Morphological Features and Markedness in the Old French Noun Declension

In the evolution of morphological studies, morphological features and markedness have come to play increasingly important rôles. Feature notation in morphology allows the development of the notion of natural morphological class, renders explicit much of the internal structure of paradigms and permits the exploitation, suitably adapted, of many of the results obtained in phonology. Markedness considerations, particularly when linked to general theories of morphology, again highlight morphological structure, constrain the variety of permitted analyses, and indicate, at least in part, the expected direction of historical change. In the sections to follow, I will present an analysis of Old French nominal and adjectival inflection which makes key use of morphological features and the differences between the marked and unmarked values of these features. This study will be particularly concerned with constraining the morphological analysis of Old French and conversely, with using Old French data to investigate more general properties of morphological systems.
BINOMIAL-POISSON ENTROPIC INEQUALITIES AND THE M/M/∞ QUEUE

This article provides entropic inequalities for binomial-Poisson distributions, derived from the two point space. They appear as local inequalities of the M/M/∞ queue. They describe in particular the exponential dissipation of Φ -entropies along this process. This simple queueing process appears as a model of “constant curvature”, and plays for the simple Poisson process the role played by the Ornstein-Uhlenbeck process for Brownian Motion. Some of the inequalities are recovered by semi-group interpolation. Additionally, we explore the behaviour of these entropic inequalities under a particular scaling, which sees the Ornstein-Uhlenbeck process as a fluid limit of M/M/∞ queues. Proofs are elementary and rely essentially on the development of a “Φ -calculus”.
Bilinear decomposition for blended expressions representation

This paper proposes a new method for the analysis of blended expressions with varying intensity. The method is based on an asymmetric bilinear model learned on a small amount of expressions. In the resulting expression space, a blended unknown expression has a signature, that can be interpreted as a mixture of the basic expressions used in the creation of the space. Three methods are compared: a traditional method based on active appearance vectors, the asymmetric bilinear model on person-independent appearance vectors and the asymmetric bilinear model on person-specific appearance vectors. Experimental results on the recognition of 14 blended unknown expressions show the relevance of the bilinear models compared to appearance-based methods and the robustness of the person-specific models according to the types of parameters (shape and/or texture).
Differences in approach to and output of innovation: study of "established" and "new entrant" small software businesses in India

This paper examines whether and how established or incumbent firms and new entrants differ in their approach to innovation and innovation output. Analyzing three streams of theory we argue that established firms base their approach to innovation strategy on their resources, capabilities, technologies, and existing markets, while new entrants approach innovation from emerging customer needs and new markets. We also view that established companies are more likely to produce innovation related to new technology, products, and processes, while new entrants are more likely to perform marketing or business model innovations. In-depth qualitative interviews with CEOs and CEO- level officials in a sample of small software businesses in India produce results that support the conclusions from theory. These results have implications for industry and policy makers and open up avenues for further research.
Everything old is new again: quoted domain-specific languages

We describe a new approach to implementing Domain-Specific Languages(DSLs), called Quoted DSLs (QDSLs), that is inspired by two old ideas:quasi-quotation, from McCarthy's Lisp of 1960, and the subformula principle of normal proofs, from Gentzen's natural deduction of 1935. QDSLs reuse facilities provided for the host language, since host and quoted terms share the same syntax, type system, and normalisation rules. QDSL terms are normalised to a canonical form, inspired by the subformula principle, which guarantees that one can use higher-order types in the source while guaranteeing first-order types in the target, and enables using types to guide fusion. We test our ideas by re-implementing Feldspar, which was originally implemented as an Embedded DSL (EDSL), as a QDSL; and we compare the QDSL and EDSL variants. The two variants produce identical code.
A Linear Logic Speci cation of

Chimera 11] is an active, deductive, and object-oriented database system which incorporates many of the most advanced technologies in the eld. Our work is aimed at proposing a formalization of its computational contents by employing the language E hhf 14], a rich linear logic programming and speciication language which is a subset of Forumm34] (a presentation of linear logic in terms of an abstract logic programming language). We will show that the resulting specii-cation captures the most important operational aspects of Chimera at the right level of abstraction, leaving room both for experiments and changes, and for possible implementation optimizations.
Science Literacy or Value Predisposition? A Meta-Analysis of Factors Predicting Public Perceptions of Benefits, Risks, and Acceptance of Nuclear Energy

ABSTRACT Nuclear energy is widely regarded as a controversial technology that polarizes public opinion. Guided by the scientific literacy and cognitive miser models, this study systematically identified and examined the magnitude of the effects of 19 predictors on public perceptions of benefits, risks, and acceptance of nuclear energy. We meta-analysed 34 empirical studies, representing a total sample of 32,938 participants and 129 independent correlations. The findings demonstrated that trust substantially affected public perception of benefits regarding nuclear energy. Sex, education, public perception of benefits regarding nuclear energy, trust, and public deliberation substantially influenced public perception of risks regarding nuclear energy. Moreover, sex, education, public perceptions of benefits, risks and costs regarding nuclear energy, knowledge, and trust substantially affected public acceptance of nuclear energy. Country of sample and time period of data collection moderated public perceptions of benefits, risks, and acceptance of nuclear energy. Implications for future research are discussed.
Excretion of Host DNA in Feces Is Associated with Risk of Clostridium difficile Infection

Clostridium difficile infection (CDI) is intricately linked to the health of the gastrointestinal tract and its indigenous microbiota. In this study, we assessed whether fecal excretion of host DNA is associated with CDI development. Assuming that shedding of epithelial cell increases in the inflamed intestine, we used human DNA excretion as a marker of intestinal insult. Whole-genome shotgun sequencing was employed to quantify host DNA excretion and evaluate bacterial content in fecal samples collected from patients with incipient CDI, hospitalized controls, and healthy subjects. Human DNA excretion was significantly increased in patients admitted to the hospital for a gastrointestinal ailment, as well as prior to an episode of CDI. In multivariable analyses, human read abundance was independently associated with CDI development. Host DNA proportions were negatively correlated with intestinal microbiota diversity. Enterococcus and Escherichia were enriched in patients excreting high quantities of human DNA, while Ruminococcus and Odoribacter were depleted. These findings suggest that intestinal inflammation can occur prior to CDI development and may influence patient susceptibility to CDI. The quantification of human DNA in feces could serve as a simple and noninvasive approach to assess bowel inflammation and identify patients at risk of CDI.
Explaining Social Relationships

Our work is aimed at explaining relationships between entities, and in particular, between persons. We note that there is an increasing amount of social network data on the web. This data may be leveraged to provide information about relationships. In the mobile computing ecosystem, with its many mobile devices and software applications, explanations of relationships between potential communicating entities may be critical to managing interconnections. The SocialXplain project was initiated to investigate methods of generating explanations of relationships leveraging distributed social networks. We explore social network navigation aimed at finding and describing links between a source and a target person. Once relationships are determined, SocialXplain also needs to define and implement strategies for generating abstract relationship explanations.