Miracle-dz/UniFAR-FTUs huggingface dataset:
Informative and Controllable Opinion Summarization

Opinion summarization is the task of automatically generating summaries for a set of reviews about a specific target (e.g., a movie or a product). Since the number of reviews for each target can be prohibitively large, neural network-based methods follow a two-stage approach where an extractive step first pre-selects a subset of salient opinions and an abstractive step creates the summary while conditioning on the extracted subset. However, the extractive model leads to loss of information which may be useful depending on user needs. In this paper we propose a summarization framework that eliminates the need to rely only on pre-selected content and waste possibly useful information, especially when customizing summaries. The framework enables the use of all input reviews by first condensing them into multiple dense vectors which serve as input to an abstractive model. We showcase an effective instantiation of our framework which produces more informative summaries and also allows to take user preferences into account using our zero-shot customization technique. Experimental results demonstrate that our model improves the state of the art on the Rotten Tomatoes dataset and generates customized summaries effectively.
Optical observations and cyclops post-shock region modelling of the polar V348 Pav

Post-shock regions (PSR) of polar cataclysmic variables (CVs) produce most of their luminosity and give rise to high circular polarization in optical wavelengths and strong variability on the white dwarf (WD) rotation period, which are distinctive features of these systems. To investigate the polar candidate V348 Pav, we obtained a comprehensive observational set including photometric, polarimetric, and spectroscopic data, which was used to constrain the post-shock properties of the system. The object presents high circular polarization (∼30 per cent) and high He ii 4686 Å to H β line ratio, confirming it is a polar. From both radial velocities and light curves, we determined an orbital period of 79.98 min, close to the orbital period minimum of CVs. The H β radial velocity curve has a semi-amplitude of 141.4 ± 1.5 km s−1. Doppler tomography showed that most of the spectral line emission in this system is originated in the region of the companion star facing the WD, possibly irradiated by the emission related to the PSR. We modelled the PSR using the cyclops code. The PSR density and temperature profiles, obtained by a proper solution of the hydrothermodynamic equations, were used in a 3D radiative transfer solution that takes into account the system geometry. We could reproduce the V348 Pav B, V, R, and I photometric and polarimetric data using a model with a WD magnetic field of ∼28 MG, a WD mass of ∼0.85 M$\odot$, and a low (∼25°) orbital inclination. These values for the WD mass and orbital inclination are consistent with the measured radial velocities.
Generalized Integrated Gradients: A practical method for explaining diverse ensembles

We introduce Generalized Integrated Gradients (GIG), a formal extension of the Integrated Gradients (IG) (Sundararajan et al., 2017) method for attributing credit to the input variables of a predictive model. GIG improves IG by explaining a broader variety of functions that arise from practical applications of ML in domains like financial services. GIG is constructed to overcome limitations of Shapley (1953) and Aumann-Shapley (1974), and has desirable properties when compared to other approaches. We prove GIG is the only correct method, under a small set of reasonable axioms, for providing explanations for mixed-type models or games. We describe the implementation, and present results of experiments on several datasets and systems of models.
Higher-order Comparisons of Sentence Encoder Representations

Representational Similarity Analysis (RSA) is a technique developed by neuroscientists for comparing activity patterns of different measurement modalities (e.g., fMRI, electrophysiology, behavior). As a framework, RSA has several advantages over existing approaches to interpretation of language encoders based on probing or diagnostic classification: namely, it does not require large training samples, is not prone to overfitting, and it enables a more transparent comparison between the representational geometries of different models and modalities. We demonstrate the utility of RSA by establishing a previously unknown correspondence between widely-employed pretrained language encoders and human processing difficulty via eye-tracking data, showcasing its potential in the interpretability toolbox for neural models.
Scattering of electronic waves in square and triangular lattice half-planes with monoatomic step

Scattering of electronic waves in square and triangular lattice half-planes by a step on the surface is analyzed using the nearest-neighbour tight binding approximation. The changes in lattice spacing and the transfer integral between nearest-neighbor sites near the surface are ignored. A standard application of the discrete Wiener-Hopf method leads to an exact solution of the scattering problem associated with incidence from the `bulk'. A far-field approximation of electronic wavefunction, as well as its graphical comparison with a numerical solution, are also provided. Natural applications and possible extensions are based on the bulk Brillouin zone for two dimensional lattice planes as well as surface energy bands for fcc crystals.
A preliminary analysis of the Scotland-Chikwawa health initiative project on morbedity

The Scotland-Chikwawa Health Initiative (SCHI) is carrying out health interventions aimed at achieving measurable results in major causes of disease and death in four villages in Chikwawa, a southern district in Malawi. Four villages of Namila, Sekeni, Mwanayaya, and Mwalija were earmarked for the pilot study. A baseline survey was carried out in July 2006 in which, among other areas of interest, the role of environmental and socioeconomic factors in diarrhoea and malaria prevalence was investigated. 55% of the households reported malaria illness in the previous two months. 84% of households reported fever as a symptom for malaria and only 8% of them mentioned additional symptoms.3% of the households mentioned either drainage of standing water or indoor and out door spraying of insecticides as a main preventive measure used to combat malaria. Malaria prevalence varied across the villages (p=000) and across different maternal age groups (p=0.038). Malaria prevalence also depended on the size of and in the type of windows used to the household (p=0.079 and p=0.004 respectively). A multinomial logistic regression analysis showed Horan, Editors, The Handbook of Water and Wastewater significant variation in the choice of treatment for malaria ailments. There were significant variations across malaria ailments. There were significant variations across included access to media, accessibility of a health facility, and in the type of windows used. 34 and 63% of the children were reported to have diarrhoea illness in the last three and six months respectively. A bivariate regression analysis showed that children from households with no toilet facilities were more likely to have suffered from diarrhoea (odds ratio (OR)=1.701, p=0.006) than those that own such facilities. Children from households that use private taps (OR=0.172, p=0.000) and where each member uses own basin (OR=0.382, p=0.003) or running water from a tap (OR=0.117, p=0.007) for washing hands were less likely to have suffered from diarrhoea in the previous six months. 97% of the households reported to have access to improved water supply surpassing the Millennium Development Goals (MDGs) of 70% by 2015. In contrast, only 48% reported to have access to improved sanitation thus lagging behind the MDGs targets of 74% by 2015.
3D Bounding Box Estimation for Autonomous Vehicles by Cascaded Geometric Constraints and Depurated 2D Detections Using 3D Results

3D object detection is one of the most important tasks in 3D vision perceptual system of autonomous vehicles. In this paper, we propose a novel two stage 3D object detection method aimed at get the optimal solution of object location in 3D space based on regressing two additional 3D object properties by a deep convolutional neural network and combined with cascaded geometric constraints between the 2D and 3D boxes. First, we modify the existing 3D properties regressing network by adding two additional components, viewpoints classification and the center projection of the 3D bounding box s bottom face. Second, we use the predicted center projection combined with similar triangle constraint to acquire an initial 3D bounding box by a closed-form solution. Then, the location predicted by previous step is used as the initial value of the over-determined equations constructed by 2D and 3D boxes fitting constraint with the configuration determined with the classified viewpoint. Finally, we use the recovered physical world information by the 3D detections to filter out the false detection and false alarm in 2D detections. We compare our method with the state-of-the-arts on the KITTI dataset show that although conceptually simple, our method outperforms more complex and computational expensive methods not only by improving the overall precision of 3D detections, but also increasing the orientation estimation precision. Furthermore our method can deal with the truncated objects to some extent and remove the false alarm and false detections in both 2D and 3D detections.
Bose-Einstein condensation of triplons close to the quantum critical point in the quasi-one-dimensional spin-
12
 antiferromagnet 
NaVOPO4

Structural and magnetic properties of a quasi-one-dimensional spin-$1/2$ compound NaVOPO$_4$ are explored by x-ray diffraction, magnetic susceptibility, high-field magnetization, specific heat, electron spin resonance, and $^{31}$P nuclear magnetic resonance measurements, as well as complementary \textit{ab initio} calculations. Whereas magnetic susceptibility of NaVOPO$_4$ may be compatible with the gapless uniform spin chain model, detailed examination of the crystal structure reveals a weak alternation of the exchange couplings with the alternation ratio $\alpha\simeq 0.98$ and the ensuing zero-field spin gap $\Delta_{0}/k_{\rm B} \simeq 2.4$~K directly probed by field-dependent magnetization measurements. No long-range order is observed down to 50\,mK in zero field. However, applied fields above the critical field $H_{c1}\simeq 1.6$\,T give rise to a magnetic ordering transition with the phase boundary $T_{\rm N} \propto {(H - H_{\rm c1})^{\frac{1}{\phi}}}$, where $\phi \simeq 1.8$ is close to the value expected for Bose-Einstein condensation of triplons. With its weak alternation of the exchange couplings and small spin gap, NaVOPO$_4$ lies close to the quantum critical point.
Inverse Compton Scattering of the Central Source Photons as an X-Ray Emission Mechanism on Kiloparsec Scales in PKS 1127–145

The beamed inverse Compton/cosmic microwave background model has generally been used for the interpretation of X-ray radiation from kiloparsec-scale jets of core-dominated quasars. Recent Fermi-LAT and HST observations have brought this model into question. We examine the assumption that X-rays from the kiloparsec-scale jet of the quasar PKS 1127−145 are produced by inverse Compton scattering of the central source emission. In this context, we show that both similarity and distinction between the observed radio and X-ray spectral indices for some of the jet knots can be explained under a single power-law electron energy distribution. We derive that the viewing angle of the kiloparsec-scale jet is about 35° and the jet has a moderate relativistic speed of ≈0.8c. The predicted gamma-ray flux of the jet is found to be a few orders of magnitude lower than the minimum flux level measured by Fermi-LAT, further supporting our scenario.
A LOFAR census of non-recycled pulsars: extending to frequencies below 80 MHz

We present the results from the low-frequency (40–78 MHz) extension of the first pulsar census of non-recycled pulsars carried out with the LOw-Frequency ARray (LOFAR). We used the low-band antennas of the LOFAR core stations to observe 87 pulsars out of 158 that had been previously detected using high-band antennas. We present flux densities and flux-calibrated profiles for the 43 pulsars we detected. Of this sample, 17 have not, to our knowledge, previously been detected at such low frequencies. Here we recalculate the spectral indices using the new low-frequency flux density measurements from the LOFAR census and discuss the prospects of studying pulsars at very low frequencies using current and upcoming facilities, such as the New Extension in Nançay Upgrading LOFAR (NenuFAR).
High-Fidelity Extraction of Neural Network Models

—Model extraction allows an adversary to steal a copy of a remotely deployed machine learning model given access to its predictions. Adversaries are motivated to mount such attacks for a variety of reasons, ranging from reducing their computational costs, to eliminating the need to collect expensive training data, to obtaining a copy of a model in order to ﬁnd adversarial examples, perform membership inference, or model inversion attacks. In this paper, we taxonomize the space of model extraction attacks around two objectives: accuracy , i.e., performing well on the underlying learning task, and ﬁdelity , i.e., matching the predictions of the remote victim classiﬁer on any input. To extract a high-accuracy model, we develop a learning-based attack which exploits the victim to supervise the training of an extracted model. Through analytical and empirical arguments, we then explain the inherent limitations that prevent any learning-based strategy from extracting a truly high-ﬁdelity model—i.e., extracting a functionally-equivalent model whose predictions are identical to those of the victim model on all possible inputs. Addressing these limitations, we expand on prior work to develop the ﬁrst practical functionally-equivalent extraction attack for direct extraction (i.e., without training) of a model’s weights. We perform experiments both on academic datasets and a state-of-the-art image classiﬁer trained with 1 billion proprietary images. In addition to broadening the scope of model extraction research, our work demonstrates the practicality of model extraction attacks against production-grade systems.
A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding

Intent detection and slot filling are two main tasks for building a spoken language understanding (SLU) system. The two tasks are closely tied and the slots often highly depend on the intent. In this paper, we propose a novel framework for SLU to better incorporate the intent information, which further guiding the slot filling. In our framework, we adopt a joint model with Stack-Propagation which can directly use the intent information as input for slot filling, thus to capture the intent semantic knowledge. In addition, to further alleviate the error propagation, we perform the token-level intent detection for the Stack-Propagation framework. Experiments on two publicly datasets show that our model achieves the state-of-the-art performance and outperforms other previous methods by a large margin. Finally, we use the Bidirectional Encoder Representation from Transformer (BERT) model in our framework, which further boost our performance in SLU task.
Real Time Trajectory Prediction Using Deep Conditional Generative Models

Data driven methods for time series forecasting that quantify uncertainty open new important possibilities for robot tasks with hard real time constraints, allowing the robot system to make decisions that trade off between reaction time and accuracy in the predictions. Despite the recent advances in deep learning, it is still challenging to make long term accurate predictions with the low latency required by real time robotic systems. In this letter, we propose a deep conditional generative model for trajectory prediction that is learned from a data set of collected trajectories. Our method uses encoder and decoder deep networks that map complete or partial trajectories to a Gaussian distributed latent space and back, allowing for fast inference of the future values of a trajectory given previous observations. The encoder and decoder networks are trained using stochastic gradient variational Bayes. In the experiments, we show that our model provides more accurate long term predictions with a lower latency than popular models for trajectory forecasting like recurrent neural networks or physical models based on differential equations. Finally, we test our proposed approach in a robot table tennis scenario to evaluate the performance of the proposed method in a robotic task with hard real time constraints.
Robust Pricing and Hedging of Options on Multiple Assets and Its Numerics

We consider robust pricing and hedging for options written on multiple assets given market option prices for the individual assets. The resulting problem is called the multi-marginal martingale optimal transport problem. We propose two numerical methods to solve such problems: using discretisation and linear programming applied to the primal side and using penalisation and deep neural networks optimisation applied to the dual side. We prove convergence for our methods and compare their numerical performance. We show how adding further information about call option prices at additional maturities can be incorporated and narrows down the no-arbitrage pricing bounds. Finally, we obtain structural results for the case of the payoff given by a weighted sum of covariances between the assets.
Analysis and Optimization of Outage Probability in Multi-Intelligent Reflecting Surface-Assisted Systems

Intelligent reflecting surface (IRS) is envisioned to be a promising solution for designing spectral and energy efficient wireless systems. In this letter, we study a multi-IRS-assisted system under Rician fading where the phase shifts adapt to only the line of sight (LoS) components. First, we analyze and optimize the outage probability of the multi-IRS-assisted system in the slow fading scenario for the non-LoS (NLoS) components. We also show that the optimal outage probability decreases with the numbers of IRSs and elements of each IRS when the LoS components are stronger than the NLoS ones. Then, we characterize the asymptotically optimal outage probability in the high signal-to-noise ratio (SNR) regime, and show that it decreases with the powers of the LoS components. To the best of our knowledge, this is the first work that studies the outage probability in multi-IRS-assisted systems.
Time crystals in a shaken atom-cavity system

We demonstrate the emergence of a time crystal of atoms in a high-finesse optical cavity driven by a phase-modulated transverse pump field, resulting in a shaken lattice. This shaken system exhibits macroscopic oscillations in the number of cavity photons and order parameters at noninteger multiples of the driving period, which signals the appearance of an incommensurate time crystal. The subharmonic oscillatory motion corresponds to dynamical switching between symmetry-broken states, which are nonequilibrium bond ordered density wave states. Employing a semiclassical phase-space representation for the driven-dissipative quantum dynamics, we confirm the rigidity and persistence of the time crystalline phase. We identify experimentally relevant parameter regimes for which the time crystal phase is long lived, and map out the dynamical phase diagram. We compare and contrast the incommensurate time crystal with the commensurate Dicke time crystal in the amplitude-modulated case.
Postpartum plasma profile of glucose and total protein in Holstein Friesian cows with and without hormone therapy under tropical climate

Recently calved HF cows (24) of University farm were monitored through clinical diagnosis and weekly plasma pranle of glucose and total protein from the day of calving till 21 st \veek postpartum. It included GnRH (reeeptal) and PGF, alpha (lutalysc) treatment at day 48--49 postpartum in anoestrus and suboestrus cows (6 each), respectively. The mean plasma glucose level was higher in suboestrus cows (PGF,a group) than the anoestrus cows (GnRH group) from sixth week postpartum with significant difference between the overall mean (61(44;1:0.83 vs 63.99±0.84 mg/dl). Moreover, the trend of higher glucose levels observed in GnRH control than the treatment group was reversed between PGF2a control and treatment, groups. The glucose levels varied insignificantly between weeks postpartum in GnRH treatment, control and pooled groups. The glucose levels were lower in PGF,a control than its treatment group throughout the postpartum period, and varied significantly at 3rd, 9th, 13th, 16th- to 18th and 21st week postpartum, including the overall mean (62.03±0.97 vs 74.69±1.08 mg/dl). The weekly means did not vary significantly in conceived group, but in non-conceived group it showed significant variation with lowest value of57.97±3.55 mg/dl at third week and highest value of 74.65±3.69 mg/dl at 14th week postpartum, though it did not vary between conceived and non-conceived groups at any of tile intervals postpartum. The total protein concentrations of anoestrus (GnRH) and suboestrus (PGF,a) groups did not differ significantly between weeks of even between groups at any of the intervals postpartum, including the pooled means (6.51±0.09 vs 6.64±0.08 g/dl). The mean levels in both GnRH and PGF,a treatment, control and their pooled groups also did not vary between weeks, except in GnRH control group where it ~as significantly low at first week (5.61 ±0.25 g/dl) and high at 12th (7AO±OJ7 g/dl) and 18th (7.25±0.30 g/dl) week postpartum. Moreover, there was no significant difference between PGFp. treatment and control groups at any of the intervals postpartum. The weekly variation in plasma total protein levels of conceived, non-conceived and pooled groups was not significant and so also between the groups, except at calving (6.l0±O.25 vs 6.85±0.25 g/d! ) and at 20th week postpartum (5.32±O.29 vs 6.51±0.26 g/d! ).
Schur null preserving maps

We provide a characterisation of Schur multiplicative maps on both finite and infinite dimensional matrix spaces, and show that every surjective Schur multiplicative contraction is automatically an isometry. We also generalise this result and provide a characterisation of Schur null preserving maps on $m\times n$ matrices and on Schur multipliers.
Magnetic field, activity, and companions of V410 Tau

We report the analysis, conducted as part of the MaTYSSE programme, of a spectropolarimetric monitoring of the ∼0.8 Myr, ∼1.4 M⊙ disc-less weak-line T Tauri star V410 Tau with the ESPaDOnS instrument at the Canada–France–Hawaii Telescope and NARVAL at the Télescope Bernard Lyot, between 2008 and 2016. With Zeeman-Doppler Imaging, we reconstruct the surface brightness and magnetic field of V410 Tau, and show that the star is heavily spotted and possesses a ∼550 G relatively toroidal magnetic field. We find that V410 Tau features a weak level of surface differential rotation between the equator and pole ∼5 times weaker than the solar differential rotation. The spectropolarimetric data exhibit intrinsic variability, beyond differential rotation, which points towards a dynamo-generated field rather than a fossil field. Long-term variations in the photometric data suggest that spots appear at increasing latitudes over the span of our data set, implying that, if V410 Tau has a magnetic cycle, it would have a period of more than 8 yr. Having derived raw radial velocities (RVs) from our spectra, we filter out the stellar activity jitter, modelled either from our Doppler maps or using Gaussian process regression. Thus filtered, our RVs exclude the presence of a hot Jupiter-mass companion below ∼0.1 au, which is suggestive that hot Jupiter formation may be inhibited by the early depletion of the circumstellar disc, which for V410 Tau may have been caused by the close (few tens of au) M dwarf stellar companion.