Conditional BRUNO: A neural process for exchangeable labelled data

We present a neural process that models exchangeable sequences of high-dimensional complex observations conditionally on a set of labels or tags. Our model combines the expressiveness of deep neural networks with the data-efficiency of Gaussian processes, resulting in a probabilistic model for which the posterior distribution is easy to evaluate and sample from, and the computational complexity scales linearly with the number of observations. The advantages of the proposed architecture are demonstrated on a challenging few-shot view reconstruction task which requires generalisation from short sequences of viewpoints

Analysis of Attention in Child–Robot Interaction Among Children Diagnosed with Cognitive Impairment

Interacting with social robots has been reported as potentially beneficial for children with social communication difficulties, with one of the promising applications being the practising of social skills, such as joint attention. We present the analysis of attention skills in children with cognitive impairments over a series of child-robot interaction sessions. Here, an interaction consists of five different modules. The first module introduces the child to the robot. The next three modules are the task modules during which children are expected to improve their attention skills during the completion of a series of social tasks. The final module is a free style interaction, where the duration of interaction between the child and robot was used as a proxy to indicate the attention of the child towards a robot. Our analysis showed that the majority of the children reduced their task completion time in modules two to four, indicating an improvement in attention. Moreover, most of the children showed positive engagement towards the robot and spent an average of 120 s during the free style interaction in module five. The positive response suggests that the robot, via child-robot interaction could be a useful and engaging tool to improve attention skills of the children with cognitive impairment

Bruno: A deep recurrent model for exchangeable data

We present a novel model architecture which leverages deep learning tools to perform exact Bayesian inference on sets of high dimensional, complex observations. Our model is provably exchangeable, meaning that the joint distribution over observations is invariant under permutation: this property lies at the heart of Bayesian inference. The model does not require variational approximations to train, and new samples can be generated conditional on previous samples, with cost linear in the size of the conditioning set. The advantages of our architecture are demonstrated on learning tasks that require generalisation from short observed sequences while modelling sequence variability, such as conditional image generation, few-shot learning, and anomaly detectio

A Modular Design for the 56 Variants of the Short Straight Section in the Arcs of the Large Hadron Collider (LHC)

The 360 Short Straight Sections (SSS) necessary for the eight arcs of the LHC machine have to fulfil different requirements. Their main function is to house the lattice two-in-one superconducting quadrupole and various correction magnets, all operating at 1.9 K in a superfluid helium bath. The magnetic and powering schemes of the arcs and the fact that the two proton beams alternate between the inner and outer magnet channels impose 24 different combinations of magnet assemblies, all housed in an identical helium enclosure. The cryogenic architecture of the LHC machine is based on cryogenic loops spanning over one half-cell (53 m) for the 4.6-20 K circuit, over a full cell (107 m) for the 1.9 K circuits, up to the full arc (about 2.3 km) for the shield cooling line. This cryogenic layout, when superimposed to the magnetic scheme, further complicated by the cryostat insulation vacuum sectorisation every 2 cells, creates additional assembly variants, up to a total number of 56. The required flexibility in the manufacture and assembly, as well as economic considerations, have led to a modular design for the different SSS components and sub-assemblies. This modularity allows to "specialise" the SSS at the latest possible assembly step of the "just in time" production line. This paper presents the conceptual design considerations to achieve this modularity, the SSS design retained for the series manufacture, and the assembly procedures recently validated on a prototype program at CERN

The New Superfluid Helium Cryostats for the Short Straight Sections of the CERN Large Hadron Collider (LHC)

The lattice of the CERN Large Hadron Collider (LHC) contains 364 Short Straight Section (SSS) units, one in every 53 m long half-cell. An SSS consists of three major assemblies: the standard cryostat section, the cryogenic service module, and the jumper connection. The standard cryostat section of an SSS contains the twin aperture high-gradient superconducting quadrupole and two pairs of superconducting corrector magnets, operating in pressurized helium II at 1.9 K. Components for isolating cryostat insulation vacuum, and the cryogenic supply lines, have to be foreseen. Special emphasis is given to the design changes of the SSS following adoption of an external cryogenic supply line (QRL). A jumper connection connects the SSS to the QRL, linking all the cryogenic tubes necessary for the local full-cell cooling loop [at every second SSS]. The jumper is connected to one end of the standard cryostat section via the cryogenic service module, which also houses beam diagnostics, current feedthroughs, and instrumentation capillaries. The conceptual design fulfilling the tight requirements of magnet alignment precision and cryogenic performance are described. Construction details, aimed at minimizing costs of series manufacturing and assembly, while ensuring the high quality of this complex accelerator component, are given

Optimized parameter search for large datasets of the regularization parameter and feature selection for ridge regression

In this paper we propose mathematical optimizations to select the optimal regularization parameter for ridge regression using cross-validation. The resulting algorithm is suited for large datasets and the computational cost does not depend on the size of the training set. We extend this algorithm to forward or backward feature selection in which the optimal regularization parameter is selected for each possible feature set. These feature selection algorithms yield solutions with a sparse weight matrix using a quadratic cost on the norm of the weights. A naive approach to optimizing the ridge regression parameter has a computational complexity of the order with the number of applied regularization parameters, the number of folds in the validation set, the number of input features and the number of data samples in the training set. Our implementation has a computational complexity of the order . This computational cost is smaller than that of regression without regularization for large datasets and is independent of the number of applied regularization parameters and the size of the training set. Combined with a feature selection algorithm the algorithm is of complexity and for forward and backward feature selection respectively, with the number of selected features and the number of removed features. This is an order faster than and for the naive implementation, with for large datasets. To show the performance and reduction in computational cost, we apply this technique to train recurrent neural networks using the reservoir computing approach, windowed ridge regression, least-squares support vector machines (LS-SVMs) in primal space using the fixed-size LS-SVM approximation and extreme learning machines

IMPLEMENTASI PEMBERIAN GANTI KERUGIAN LAYANAN PAKET DI PT TIKI JALUR NUGRAHA EKAKURIR (JNE) CABANG SURAKARTA DITINJAU DARI UNDANG-UNDANG NOMOR 38 TAHUN 2009 TENTANG POS

ABSTRAK Yusuf Bintang Syaifinuha, E0012414. 2016. IMPLEMENTASI PEMBERIAN GANTI KERUGIAN LAYANAN PAKET DI PT TIKI JALUR NUGRAHA EKAKURIR (JNE) CABANG SURAKARTA DITINJAU DARI UNDANG-UNDANG NOMOR 38 TAHUN 2009 TENTANG POS. Fakultas Hukum Universitas Sebelas Maret Surakarata. Penelitian ini bertujuan untuk mengetahui implementasi pemberian ganti kerugian di PT Tiki Jalur Nugraha Ekakurir (JNE) Cabang Surakarta ditinjau dari UU Pos dan untuk mengetahui cara penyelesaian sengketa yang timbul dari implementasi pemberian ganti kerugian di PT Tiki Jalur Nugraha Ekakurir (JNE) Cabang Surakarta. Metode penelitian yang digunakan dalam penelitian ini adalah deskriptif kualitatif. Jenis penelitian ini adalah penelitian empiris. Data yang digunakan terdiri dari dua data yaitu data primer dan data sekunder. Teknik pengumpulan data adalah dengan metode wawancara dan studi pustaka. Berdasarkan hasil analisis penelitian, dapat diambil kesimpulan bahwa implementasi pemberian ganti kerugian di JNE kurang sesuai dengan Pasal 28 UU Pos karena jenis ganti kerugian hanya untuk kehilangan kiriman dan kerusakan isi kiriman. Pengirim yang mengajukan klaim ganti kerugian harus memenuhi syarat administrasi yang telah ditetapkan oleh JNE. Nilai ganti kerugian yang diberikan JNE adalah 10 kali biaya pengiriman atau sesuai harga barang yang hilang dan/atau rusak jika menggunakan asuransi. JNE memilih upaya hukum diluar pengadilan (nonlitigasi) berupa negosiasi dalam menyelesaikan sengketa yang terjadi dalam implementasi pemberian ganti kerugian. Kata kunci :Perjanjian, Wanprestasi, Implementasi ganti kerugian

Avant-propos

Depuis quelque temps déjà, exception fait corps tant bien que mal avec française. Les chercheurs de l’Université Texas A&M ont donc décidé d’en faire en 2007 le sujet de la rencontre annuelle des Études françaises sur les xxe et xxie siècles, en collaboration avec l’équipe Écritures de la modernité, de la Sorbonne Nouvelle. De ces exposés et débats est déjà sorti, en deux livraisons de revue, French Exception. Voici le second ensemble, dont le titre diffère : il signifie un déplacement d’acce..