Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Beamforming for mm-Wave Massive MIMO Systems with Partially Connected RF Architecture.

To satisfy the capacity requirements of future mobile systems, under-utilized millimeter wave frequencies can be efficiently exploited by employing massive MIMO technology with highly directive beamforming. Hybrid analog-digital beamforming has been recognised as a promising approach for large-scale MIMO implementations with a reduced number of costly and power-hungry RF chains. In comparison to fully connected architecture, hybrid beamforming (HBF) with partially connected RF architecture is particularly appealing for the practical implementation due to less complex RF power division and combining networks. In this paper, we first formulate single- and multi-user rate maximization problems as weighted minimum mean square error (WMMSE) and derive solutions for hybrid beamformers using alternating optimization. The algorithms are designed for the full-array- and sub-array-based processing strategies of partially connected HBF architecture. In addition to the rate maximizing WMMSE solutions, we propose lower complexity sub-array-based zero-forcing algorithms. The performance of the proposed algorithms is evaluated in two different channel models, i.e., a simple geometric model and a realistic statistical millimeter wave model known as NYUSIM. The performance results of the WMMSE HBF algorithms are meant to reveal the potential of partially connected HBF and serve as upper bounds for lower complexity methods. Numerical results imply that properly designed partially connected HBF has the potential to provide an good compromise between hardware complexity and system performance in comparison to fully digital beamforming.
Metal‐Containing Liquid Crystals

This article has no abstract. Keywords: metallomesogens; coordination metal and organometallic complexes; molecular design; lanthanidomesogens; liquid crystalline buckyferrocene; mesomorphic metallodendrimers; polymetallic and polyoxometallate clusters; spin-transitions; luminescent
Trapping, transport and polarisation of ultracold lithium

The aim of our experiment is to explore two methods of creating an ultracold dipolar gas which can subsequently be used to simulate quantum phenomena. The first method is to sympathetically cool polar molecules. In this case, the molecules are overlapped with ultracold lithium atoms, thus allowing the two clouds to thermalise through elastic collisions. The second method is to electrically polarise ultracold lithium atoms using an electric field of approximately 1MV/cm. This involves placing the atoms between two high voltage electrodes. This thesis describes and characterises the setup used to produce, trap and transport a cloud of lithium-7 atoms. The setup consists of a lithium oven, Zeeman slower, magnetooptical trap (MOT) and magnetic trap. Up to 2.3 × 108 atoms are loaded into the MOT with an initial temperature of 1.3mK. By implementing a compressed MOT phase the temperature is reduced to 0.75mK. Before transport, 23% of the MOT atoms are transferred into the magnetic trap, which has a lifetime of 1.53 ± 0.01 s in the MOT chamber. Using a motorised translation stage to move the magnetic trapping coils, atomic transport over a distance of 44 cm from the MOT chamber to the science chamber has been demonstrated. The transport efficiency is 41%. In the science chamber the lifetime of the magnetic trap has been measured as 18.5± 0.7 s. Experiments to optimise the absorption imaging system have also been carried out, highlighting the fact that a time and position dependent magnetic field is present after the trapping coils switch off. The feasibility of producing a 1MV/cm electric field has been investigated. By using indium tin oxide coated glass electrodes in an adjustable electrode mount, an electric field of approximately 0.2MV/cm has been generated. These electrodes were subsequently replaced with superpolished stainless steel electrodes which generated a field of 0.38MV/cm.
Molecular Dynamics Study of Temperature Effects on Water Structure and Kinetics

For a system of 216 water molecules, molecular dynamics calculations have been carried out at two temperatures in addition to the one studied and reported previously. As before, the Ben‐Naim and Stillinger effective pair potential was used for these calculations. The results document the breakdown of hydrogen‐bond order and the rapid increase in the freedom of molecular motions that accompany temperature rise in real water. We find no evidence at any temperature to support those water models which partition molecules into two classes (bonded framework or cluster molecules vs unbonded molecules). Nevertheless the pair potential distribution function changes with temperature in such a way as to suggest a basic hydrogen‐bond rupture mechanism characterized by an excitation energy of about 2.5 kcal/mole. Some results indicate that the potential utilized is a bit too tetrahedrally directional to represent real water faithfully, so a possible modification is mentioned.
The effect of hydration of a protein preparation on the state of water in fine forcemeat sausages

Ab s t ra c t . The study was undertaken to establish cha nges in water binding in fine forcemeat with some of the meat substituted by the protein pr epa ation CC 400. Three different variants of hydration of the preparation were tested: 1:5, 1:7. 5 and 1:10. At each level of hydration the meat was substituted in 5%, 10% and 15% of that in the i nitial state (reference sample). The content of total and free water was determined along with the spin-lattice and spin-spin relaxation times. The forcemeat was subjected to thermal treatment and th e amount of thermal drip was measured. The degree of hydration of the protein preparation was found to determine the mutual relations between the free and bound water in the forcemeat and the m olecular dynamics of water. The greatest amount of bound water and the smallest thermal drip were obtained for the protein preparation hydration at the ratio 1:7.5. Higher hydration can only be used at 5% meat substitution. The samples with 15% meat substitution should be hydrat e 1:5 ratio.
Serum C-Reactive Protein in Patients with Deficit Schizophrenia and the Relationship with Cognitive Function

Background Serum levels of C-reactive protein (CRP) were measured in patients with deficit schizophrenia (DS) to confirm the association between CRP level and cognitive performance and to determine whether CRP was a new biological indicator with the potential clinical applications in DS patients. Methods Three independent samples [41 DS and 50 non-deficit schizophrenia (NDS) and 30 sex- and age-matched healthy controls (HCs)] were recruited in our study. Serum CRP levels were measured by immunofluorescence. The Positive and Negative Syndrome Scale (PANSS) and alternative forms of the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) were also assessed. And the relationships between serum CRP levels and both PANSS and RBANS scores were finally analyzed. Results The results of serum CRP level were showed significantly different among the three groups and increased from the HCs to NDS patients to DS patients. There were also significant differences in the cognitive subdomain analyses among the three groups. Serum CRP levels were found positively correlated with total and negative PANSS scores, and showed negatively correlated with overall cognitive test scores in the DS samples. Conclusion Serum C-reactive protein levels and their association with cognitive performance were different between deficit schizophrenia and non-deficit schizophrenia samples, and higher serum CRP level was associated with worse cognitive performance in the DS patients. The results indicated that CRP could be a potential biomarker, and DS could be a distinct subset of schizophrenia.
Data-Efficient Methods for Dialogue Systems

Conversational User Interface (CUI) has become ubiquitous in everyday life, in consumer-focused products like Siri and Alexa or business-oriented solutions. Deep learning underlies many recent breakthroughs in dialogue systems but requires very large amounts of training data, often annotated by experts. Trained with smaller data, these methods end up severely lacking robustness (e.g. to disfluencies and out-of-domain input), and often just have too little generalisation power. In this thesis, we address the above issues by introducing a series of methods for training robust dialogue systems from minimal data. Firstly, we study two orthogonal approaches to dialogue: linguistically informed and machine learning-based - from the data efficiency perspective. We outline the steps to obtain data-efficient solutions with either approach. We then introduce two data-efficient models for dialogue response generation: the Dialogue Knowledge Transfer Network based on latent variable dialogue representations, and the hybrid Generative-Retrieval Transformer model (ranked first at the DSTC 8 Fast Domain Adaptation task). Next, we address the problem of robustness given minimal data. As such, propose a multitask LSTM-based model for domain-general disfluency detection. For the problem of out-of-domain input, we present Turn Dropout, a data augmentation technique for anomaly detection only using in-domain data, and introduce autoencoder-augmented models for efficient training with Turn Dropout. Finally, we focus on social dialogue and introduce a neural model for response ranking in social conversation used in Alana, the 3rd place winner in the Amazon Alexa Prize 2017 and 2018. We employ a novel technique of predicting the dialogue length as the main ranking objective and show that this approach improves upon the ratings-based counterpart in terms of data efficiency while matching it in performance.
Improving Auto-Encoders' self-supervised image classification using pseudo-labelling via data augmentation and the perceptual loss

In this paper, we introduce a novel method to pseudo-label unlabelled images and train an Auto-Encoder to classify them in a self-supervised manner that allows for a high accuracy and consistency across several datasets. The proposed method consists of first applying a randomly sampled set of data augmentation transformations to each training image. As a result, each initial image can be considered as a pseudo-label to its corresponding augmented ones. Then, an Auto-Encoder is used to learn the mapping between each set of the augmented images and its corresponding pseudo-label. Furthermore, the perceptual loss is employed to take into consideration the existing dependencies between the pixels in the same neighbourhood of an image. This combination encourages the encoder to output richer encodings that are highly informative of the input's class. Consequently, the Auto-Encoder's performance on unsupervised image classification is improved both in termes of stability and accuracy becoming more uniform and more consistent across all tested datasets. Previous state-of-the-art accuracy on the MNIST, CIFAR-10 and SVHN datasets is improved by 0.3\%, 3.11\% and 9.21\% respectively.
FPRAS Approximation of the Matrix Permanent in Practice

The matrix permanent belongs to the complexity class #P-Complete. It is generally believed to be computationally infeasible for large problem sizes, and significant research has been done on approximation algorithms for the matrix permanent. We present an implementation and detailed runtime analysis of one such Markov Chain Monte Carlo (MCMC) based Fully Polynomial Randomized Approximation Scheme (FPRAS) for the matrix permanent, which has previously only been described theoretically and with big-Oh runtime analysis. We demonstrate by analysis and experiment that the constant factors hidden by previous big-Oh analyses result in computational infeasibility.
Metathetic Selective Degradation of Polyisoprene: Low‐Molecular‐Weight Telechelic Oligomer Obtained from Both Synthetic and Natural Rubber

Degradation studies of cis-1,4-polyisoprene were carried out using first and second generation Grubbs catalysts to achieve end-functionalized acetoxy oligomers in both an organic solvent and a latex phase at room temperature. Well-defined acetoxy telechelic polyisoprene structures were obtained in a selective manner with arange of M n from 10 000 to 30 000, with a polydispersity index of around 2.5.
Adaptive Weighted Discriminator for Training Generative Adversarial Networks

Generative adversarial network (GAN) has become one of the most important neural network models for classical unsupervised machine learning. A variety of discriminator loss functions have been developed to train GAN’s discriminators and they all have a common structure: a sum of real and fake losses that only depends on the actual and generated data respectively. One challenge associated with an equally weighted sum of two losses is that the training may benefit one loss but harm the other, which we show causes instability and mode collapse. In this paper, we introduce a new family of discriminator loss functions that adopts a weighted sum of real and fake parts, which we call adaptive weighted loss functions or aw-loss functions. Using the gradients of the real and fake parts of the loss, we can adaptively choose weights to train a discriminator in the direction that benefits the GAN’s stability. Our method can be potentially applied to any discriminator model with a loss that is a sum of the real and fake parts. Experiments validated the effectiveness of our loss functions on unconditional and conditional image generation tasks, improving the baseline results by a significant margin on CIFAR-10, STL-10, and CIFAR-100 datasets in Inception Scores (IS) and Fréchet Inception Distance (FID) metrics.
Encapsulation of Naja-Naja oxiana snake venom into poly (lactide-co-glycolide) microspheres.

One small-scale double emulsion technique for incorporation of Naja- Naja oxiana venom into Poly (lactide-co-glycolide) (PLGA) microspheres were developed and optimized. The effects of high speed homogenization on the double emulsion stability, microsphere size, entrapment efficiency and In vitro release of venom were studied. A stable double emulsion was verified by homogenization method. Slow removal of the organic phase allowed measurement of the size of the emulsion droplets and subsequent predication of the size which resulting microspheres. Microspheres in the size range of 1-10µm were prepared using homogenization technique, but this technique was sensitive to changes in the operating time, speed and volume of outer aqueous phase. Snake venom was released in vitro in a triphasic manner. After immunization of guinea–pig with a single IM injection, the PLGA-venom microspheres elicited an antibody response very high as that elicited with conventional method. These results indicate that the antigenicity of venom was retained after incorporation into PLGA microspheres using homogenization technique.
NaturalCC: A Toolkit to Naturalize the Source Code Corpus

We present NaturalCC, an efficient and extensible toolkit to bridge the gap between natural language and programming language, and facilitate the research on big code analysis. Using NaturalCC, researchers both from natural language or programming language communities can quickly and easily reproduce the state-of-the-art baselines and implement their approach. NaturalCC is built upon Fairseq and PyTorch, providing (1) an efficient computation with multi-GPU and mixed-precision data processing for fast model training, (2) a modular and extensible framework that makes it easy to reproduce or implement an approach for big code analysis, and (3) a command line interface and a graphical user interface to demonstrate each model's performance. Currently, we have included several state-of-the-art baselines across different tasks (e.g., code completion, code comment generation, and code retrieval) for demonstration. The video of this demo is available at https://www.youtube. com/watch? v=q4W5VSI-u3E&t=25s.
Dialogue Discourse-Aware Graph Convolutional Networks for Abstractive Meeting Summarization

Sequence-to-sequence methods have achieved promising results for textual abstractive meeting summarization. Different from documents like news and scientific papers, a meeting is naturally full of dialogue-specific structural information. However, previous works model a meeting in a sequential manner, while ignoring the rich structural information. In this paper, we develop a Dialogue Discourse-Aware Graph Convolutional Networks (DDA-GCN) for meeting summarization by utilizing dialogue discourse, which is a dialogue-specific structure that can provide pre-defined semantic relationships between each utterance. We first transform the entire meeting text with dialogue discourse relations into a discourse graph and then use DDA-GCN to encode the semantic representation of the graph. Finally, we employ a Recurrent Neural Network to generate the summary. In addition, we utilize the question-answer discourse relation to construct a pseudo-summarization corpus, which can be used to pre-train our model. Experimental results on the AMI dataset show that our model outperforms various baselines and can achieve state-of-the-art performance.
RELAP5 Simulation of Advanced Test Reactor Scaled LOCA Experiments 7, 8, 12, and 14

AbstractThe RELAP5 computer code is used to simulate four small-scale loss-of-coolant accident (LOCA) experiments conducted at Idaho National Engineering Laboratory (INEL). The purpose of the study is to help assess RELAP5 under conditions similar to those expected during a large-break LOCA at INEL’s Advanced Test Reactor (ATR). During an ATR large-break LOCA, it is expected that the primary system pressure will rapidly decrease from the initial operating pressure (∼2.55 MPa) to subatmospheric conditions governed by the primary coolant temperature. Flashing will occur in the high points of the system and air ingress from the break is possible. The RELAP5 code had not previously been assessed under these conditions. The results show that RELAP5 accurately predicted pressures, water levels, and air ingress behavior, thus providing confidence in the ability of the code to simulate an ATR large-break LOCA.
Pedestrian Behavior Prediction via Multitask Learning and Categorical Interaction Modeling

Pedestrian behavior prediction is one of the major challenges for intelligent driving systems. Pedestrians often exhibit complex behaviors influenced by various contextual elements. To address this problem, we propose a multitask learning framework that simultaneously predicts trajectories and actions of pedestrians by relying on multimodal data. Our method benefits from 1) a hybrid mechanism to encode different input modalities independently allowing them to develop their own representations, and jointly to produce a representation for all modalities using shared parameters; 2) a novel interaction modeling technique that relies on categorical semantic parsing of the scenes to capture interactions between target pedestrians and their surroundings; and 3) a dual prediction mechanism that uses both independent and shared decoding of multimodal representations. Using public pedestrian behavior benchmark datasets for driving, PIE and JAAD, we highlight the benefits of multitask learning for behavior prediction and show that our model achieves state-of-the-art performance and improves trajectory and action prediction by up to 22% and 6% respectively. We further investigate the contributions of the proposed processing and interaction modeling techniques via extensive ablation studies.
Source apportionment of the PM10 fraction of particulate matter collected in Kraków, Poland

Air pollution is one of many factors which may cause serious health problems [5, 8, 11, 13]. It can negatively influence respiratory and cardiovascular morbidity and mortality in humans. Air pollution is caused by gaseous (SO2, NOx) and particulate matter (soot, toxic metals) pollutants. The effects on human health are related to chemical and physical properties of particulate matter [4]. Particle size fractions of 1, 2.5 and 10 μm have different influence on the respiratory system [9]. The chemical content of particulate matter is very important; it has a significant effect on particulate matter (PM) toxicity [9]. Ambient PM originates from various sources, for example: industry, traffic and soil. For the most part, urban air is polluted by sources originating in industrial activities and from automotive traffic. Urban environments also tend to have the highest level of air pollution, which affects the people Source apportionment of the PM10 fraction of particulate matter collected in Krakow, Poland Lucyna Samek
Medium-resolution spectrum of the exoplanet HIP 65426 b

Medium-resolution integral-field spectrographs (IFS) coupled with adaptive-optics such as Keck/OSIRIS, VLT/MUSE, or SINFONI are appearing as a new avenue for enhancing the detection and characterization capabilities of young, gas giant exoplanets at large heliocentric distances (>5 au). We analyzed K-band VLT/SINFONI medium-resolution (Rλ ~5577) observations of the young giant exoplanet HIP 65426 b. Our dedicated IFS data analysis toolkit (TExTRIS) optimized the cube building, star registration, and allowed for the extraction of the planet spectrum. A Bayesian inference with the nested sampling algorithm coupled with the self-consistent forward atmospheric models BT-SETTL15 and Exo-REM using the ForMoSA tool yields Teff = 1560 ± 100 K, log(g) ≤ 4.40 dex, [M/H] = 0.05−0.22+0.24 dex, and an upper limit on the C/O (≤0.55). The object is also re-detected with the so-called “molecular mapping” technique. The technique yields consistent atmospheric parameters, but the loss of the planet pseudo-continuum in the process degrades or modifies the constraints on these parameters. The solar to sub-solar C/O ratio suggests an enrichment by solids at formation if the planet was formed beyond the water snowline (≥20 au) by core accretion (CA hereafter). However, a formation by gravitational instability (GI hereafter) cannot be ruled out. The metallicity is compatible with the bulk enrichment of massive Jovian planets from the Bern planet population models. Finally, we measure a radial velocity of 26 ± 15 km s−1 compatible with our revised measurement on the star. This is the fourth imaged exoplanet for which a radial velocity can be evaluated, illustrating the potential of such observations for assessing the coevolution of imaged systems belonging to star forming regions, such as HIP 65426.