Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Instance-Level Parser Selection for Cross-Lingual Transfer of Dependency Parsers

Current methods of cross-lingual parser transfer focus on predicting the best parser for a low-resource target language globally, that is, “at treebank level”. In this work, we propose and argue for a novel cross-lingual transfer paradigm: instance-level parser selection (ILPS), and present a proof-of-concept study focused on instance-level selection in the framework of delexicalized parser transfer. Our work is motivated by an empirical observation that different source parsers are the best choice for different Universal POS-sequences (i.e., UPOS sentences) in the target language. We then propose to predict the best parser at the instance level. To this end, we train a supervised regression model, based on the Transformer architecture, to predict parser accuracies for individual POS-sequences. We compare ILPS against two strong single-best parser selection baselines (SBPS): (1) a model that compares POS n-gram distributions between the source and target languages (KL) and (2) a model that selects the source based on the similarity between manually created language vectors encoding syntactic properties of languages (L2V). The results from our extensive evaluation, coupling 42 source parsers and 20 diverse low-resource test languages, show that ILPS outperforms KL and L2V on 13/20 and 14/20 test languages, respectively. Further, we show that by predicting the best parser “at treebank level” (SBPS), using the aggregation of predictions from our instance-level model, we outperform the same baselines on 17/20 and 16/20 test languages.
Generalizability issues with deep learning models in medicine and their potential solutions: illustrated with cone-beam computed tomography (CBCT) to computed tomography (CT) image conversion

Generalizability is a concern when applying a deep learning (DL) model trained on one dataset to other datasets. It is challenging to demonstrate a DL model’s generalizability efficiently and sufficiently before implementing the model in clinical practice. Training a universal model that works anywhere, anytime, for anybody is unrealistic. In this work, we demonstrate the generalizability problem, then explore potential solutions based on transfer learning by using the cone-beam computed tomography (CBCT) to computed tomography (CT) image conversion task as the testbed. Previous works only studied on one or two anatomical sites and used images from the same vendor’s scanners. Here, we investigated how a model trained for one machine and one anatomical site works on other machines and other anatomical sites. We trained a model on CBCT images acquired from one vendor’s scanners for head and neck cancer patients and applied it to images from another vendor’s scanners and for prostate, pancreatic, and cervical cancer patients. We found that generalizability could be a significant problem for this particular application when applying a trained DL model to datasets from another vendor’s scanners. We then explored three practical solutions based on transfer learning to solve this generalization problem: the target model, which is trained on a target dataset from scratch; the combined model, which is trained on both source and target datasets from scratch; and the adapted model, which fine-tunes the trained source model to a target dataset. We found that when there are sufficient data in the target dataset, all three models can achieve good performance. When the target dataset is limited, the adapted model works the best, which indicates that using the fine-tuning strategy to adapt the trained model to an unseen target dataset is a viable and easy way to implement DL models in the clinic.
On the Jacobian group of a cone over a circulant graph

For any given graph G, consider the graph Ĝ which is a cone over G. We study two important invariants of such a cone, namely, the complexity (the number of spanning trees) and the Jacobian of the graph. We prove that complexity of graph Ĝ coincides with the number of rooted spanning forests in G and the Jacobian of Ĝ is isomorphic to the cokernel of the operator I + L(G), where L(G) is the Laplacian of G and I is the identity matrix. As a consequence, one can calculate the complexity of Ĝ as det(I + L(G)). As an application, we establish general structural theorems for the Jacobian of Ĝ in the case when G is a circulant graph or cobordism of two circulant graphs.
Design-Based Ratio Estimators and Central Limit Theorems for Clustered, Blocked RCTs

Abstract This article develops design-based ratio estimators for clustered, blocked randomized controlled trials (RCTs), with an application to a federally funded, school-based RCT testing the effects of behavioral health interventions. We consider finite population weighted least-square estimators for average treatment effects (ATEs), allowing for general weighting schemes and covariates. We consider models with block-by-treatment status interactions as well as restricted models with block indicators only. We prove new finite population central limit theorems for each block specification. We also discuss simple variance estimators that share features with commonly used cluster-robust standard error estimators. Simulations show that the design-based ATE estimator yields nominal rejection rates with standard errors near true ones, even with few clusters.
Astronomical masers and Dicke’s superradiance

We consider the radiation properties and processes of a gas with a population inversion using the formalism based on the Maxwell-Bloch equations. We focus on the maser action and Dicke's superradiance to establish their relationship in the overall radiation process during the temporal evolution of the system as a function of position. We show that the maser action and superradiance are not competing phenomena but are rather complementary, and define two distinct limits for the intensity of radiation. Masers characterise the quasi-steady state limit, when the population inversion density and the polarisation amplitude vary on time-scales longer than those of non-coherent processes affecting their evolution (e.g., collisions), while superradiance defines the fast transient regime taking place when these conditions are reversed. We show how a transition from a maser regime to superradiance will take place whenever a critical threshold for the column density of the population inversion is reached, at which point a strong level of coherence is established in the system and a powerful burst of radiation can ensue during the transient regime. This critical level also determines the spatial region where a transition from the unsaturated to the saturated maser regimes will take place; superradiance can thus be seen as the intermediary between the two. We also quantify the gain in radiation intensity attained during the superradiance phase relative to the two maser regimes, and show how the strong coherence level during superradiance is well suited to explain observations that reveal intense and fast radiation flares in maser-hosting regions.
First Light And Reionization Epoch Simulations (FLARES) – I. Environmental dependence of high-redshift galaxy evolution

We introduce the First Light And Reionisation Epoch Simulations (FLARES), a suite of zoom simulations using the EAGLE model. We resimulate a range of overdensities during the Epoch of Reionization (EoR) in order to build composite distribution functions, as well as explore the environmental dependence of galaxy formation and evolution during this critical period of galaxy assembly. The regions are selected from a large $(3.2 \, \mathrm{cGpc})^{3}$ parent volume, based on their overdensity within a sphere of radius 14 h−1 cMpc. We then resimulate with full hydrodynamics, and employ a novel weighting scheme that allows the construction of composite distribution functions that are representative of the full parent volume. This significantly extends the dynamic range compared to smaller volume periodic simulations. We present an analysis of the galaxy stellar mass function (GSMF), the star formation rate distribution function (SFRF), and the star-forming sequence (SFS) predicted by FLARES, and compare to a number of observational and model constraints. We also analyse the environmental dependence over an unprecedented range of overdensity. Both the GSMF and the SFRF exhibit a clear double-Schechter form, up to the highest redshifts (z = 10). We also find no environmental dependence of the SFS normalization. The increased dynamic range probed by FLARES will allow us to make predictions for a number of large area surveys that will probe the EoR in coming years, carried out on new observatories such as Roman and Euclid.
Classification Representations Can be Reused for Downstream Generations

Contrary to the convention of using supervision for class-conditioned $\it{generative}$ $\it{modeling}$, this work explores and demonstrates the feasibility of a learned supervised representation space trained on a discriminative classifier for the $\it{downstream}$ task of sample generation. Unlike generative modeling approaches that aim to $\it{model}$ the manifold distribution, we directly $\it{represent}$ the given data manifold in the classification space and leverage properties of latent space representations to generate new representations that are guaranteed to be in the same class. Interestingly, such representations allow for controlled sample generations for any given class from existing samples and do not require enforcing prior distribution. We show that these latent space representations can be smartly manipulated (using convex combinations of $n$ samples, $n\geq2$) to yield meaningful sample generations. Experiments on image datasets of varying resolutions demonstrate that downstream generations have higher classification accuracy than existing conditional generative models while being competitive in terms of FID.
Investigation of background electron emission in the LUX detector

Dual-phase xenon detectors, as currently used in direct detection dark matter experiments, have observed elevated rates of signals from electron emission processes in the low energy region. While this background negatively impacts detector performance in various ways, its origins have only been partially studied. In this paper, we report a systematic investigation of the electron pathologies observed in the LUX dark matter experiment. We characterize different electron populations based on their emission intensities and their correlations with preceding energy depositions in the detector. By studying these electron backgrounds under different experimental conditions, we identified the possible mechanisms that may contribute to the electron emission. We report the observed electron event rate in LUX after background rejection, and discuss how such backgrounds can be further mitigated in ongoing and future xenon-based dark matter experiments.
A Game Theoretic Framework for Model Based Reinforcement Learning

Model-based reinforcement learning (MBRL) has recently gained immense interest due to its potential for sample efficiency and ability to incorporate off-policy data. However, designing stable and efficient MBRL algorithms using rich function approximators have remained challenging. To help expose the practical challenges in MBRL and simplify algorithm design from the lens of abstraction, we develop a new framework that casts MBRL as a game between: (1) a policy player, which attempts to maximize rewards under the learned model; (2) a model player, which attempts to fit the real-world data collected by the policy player. For algorithm development, we construct a Stackelberg game between the two players, and show that it can be solved with approximate bi-level optimization. This gives rise to two natural families of algorithms for MBRL based on which player is chosen as the leader in the Stackelberg game. Together, they encapsulate, unify, and generalize many previous MBRL algorithms. Furthermore, our framework is consistent with and provides a clear basis for heuristics known to be important in practice from prior works. Finally, through experiments we validate that our proposed algorithms are highly sample efficient, match the asymptotic performance of model-free policy gradient, and scale gracefully to high-dimensional tasks like dexterous hand manipulation.
Non-Autoregressive Machine Translation with Latent Alignments

This paper investigates two latent alignment models for non-autoregressive machine translation, namely CTC and Imputer. CTC generates outputs in a single step, makes strong conditional independence assumptions about output variables, and marginalizes out latent alignments using dynamic programming. Imputer generates outputs in a constant number of steps, and approximately marginalizes out possible generation orders and latent alignments for training. These models are simpler than existing non-autoregressive methods, since they do not require output length prediction as a pre-process. In addition, our architecture is simpler than typical encoder-decoder architectures, since input-output cross attention is not used. On the competitive WMT'14 En$\rightarrow$De task, our CTC model achieves 25.7 BLEU with a single generation step, while Imputer achieves 27.5 BLEU with 2 generation steps, and 28.0 BLEU with 4 generation steps. This compares favourably to the baseline autoregressive Transformer with 27.8 BLEU.
Predictive Whole-Body Control of Humanoid Robot Locomotion

Humanoid robots are machines built with an anthropomorphic shape. Despite decades of research into the subject, it is still challenging to tackle the robot locomotion problem from an algorithmic point of view. For example, these machines cannot achieve a constant forward body movement without exploiting contacts with the environment. The reactive forces resulting from the contacts are subject to strong limitations, complicating the design of control laws. As a consequence, the generation of humanoid motions requires to exploit fully the mathematical model of the robot in contact with the environment or to resort to approximations of it. This thesis investigates predictive and optimal control techniques for tackling humanoid robot motion tasks. They generate control input values from the system model and objectives, often transposed as cost function to minimize. In particular, this thesis tackles several aspects of the humanoid robot locomotion problem in a crescendo of complexity. First, we consider the single step push recovery problem. Namely, we aim at maintaining the upright posture with a single step after a strong external disturbance. Second, we generate and stabilize walking motions. In addition, we adopt predictive techniques to perform more dynamic motions, like large step-ups. The above-mentioned applications make use of different simplifications or assumptions to facilitate the tractability of the corresponding motion tasks. Moreover, they consider first the foot placements and only afterward how to maintain balance. We attempt to remove all these simplifications. [continued]
Centrifugally driven mass-loss and outbursts of massive stars

Rotation and mass-loss are crucially interlinked properties of massive stars, strongly affecting their evolution and ultimate fate. Massive stars rotating near their break-up limit shed mass centrifugally, creating Be stars with circumstellar discs and possibly driving outbursts. Using the mesa stellar evolution code, we examine the effects of efficient angular momentum transport on the main-sequence and post-main-sequence rotational evolution of massive stars. In rapid rotators, angular momentum transported from the contracting core to the expanding envelope can spin-up the surface layers past the break-up rate, particularly for stars near (or beyond) the end of the main-sequence and in low-metallicity environments. We also demonstrate that centrifugal instabilities could arise in rapidly rotating massive stars, potentially triggering the S Doradus outbursts observed in luminous blue variable stars. Prior mass accretion from a binary companion increases both the likelihood and the intensity of centrifugal mass-loss. We discuss implications for massive stellar evolution, Be stars, and luminous blue variables.
Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection

The ability to control for the kinds of information encoded in neural representation has a variety of use cases, especially in light of the challenge of interpreting these models. We present Iterative Null-space Projection (INLP), a novel method for removing information from neural representations. Our method is based on repeated training of linear classifiers that predict a certain property we aim to remove, followed by projection of the representations on their null-space. By doing so, the classifiers become oblivious to that target property, making it hard to linearly separate the data according to it. While applicable for multiple uses, we evaluate our method on bias and fairness use-cases, and show that our method is able to mitigate bias in word embeddings, as well as to increase fairness in a setting of multi-class classification.
Learning Structured Embeddings of Knowledge Graphs with Adversarial Learning Framework

Many large-scale knowledge graphs are now available and ready to provide semantically structured information that is regarded as an important resource for question answering and decision support tasks. However, they are built on rigid symbolic frameworks which makes them hard to be used in other intelligent systems. We present a learning method using generative adversarial architecture designed to embed the entities and relations of the knowledge graphs into a continuous vector space. A generative network (GN) takes two elements of a (subject, predicate, object) triple as input and generates the vector representation of the missing element. A discriminative network (DN) scores a triple to distinguish a positive triple from those generated by GN. The training goal for GN is to deceive DN to make wrong classification. When arriving at a convergence, GN recovers the training data and can be used for knowledge graph completion, while DN is trained to be a good triple classifier. Unlike few previous studies based on generative adversarial architectures, our GN is able to generate unseen instances while they just use GN to better choose negative samples (already existed) for DN. Experiments demonstrate our method can improve classical relational learning models (e.g.TransE) with a significant margin on both the link prediction and triple classification tasks.
Minimal quantum heat manager boosted by bath spectral filtering

We reveal the potentially important role of a general mechanism that has hitherto not been invoked in quantum heat management schemes, namely, spectral filtering of the coupling between the heat baths in the setup and the quantum system that controls the heat flow. Such filtering is enabled by interfaces between the system and the baths by means of harmonic-oscillator modes whose resonant frequencies and coupling strengths are used as control parameters of the system-bath coupling spectra. We show that this uniquely quantum-electrodynamic mechanism, here dubbed bath spectral filtering, boosts the performance of a minimal quantum heat manager comprised of two interacting qubits or an analogous optomechanical system, allowing this device to attain either perfect heat diode action or strongly enhanced heat transistor action.
Harmonic Based Model Predictive Control for Set-Point Tracking

This article presents a novel model predictive control (MPC) formulation for set-point tracking. Stabilizing predictive controllers based on terminal ingredients may exhibit stability and feasibility issues in the event of a reference change for small to moderate prediction horizons. In the MPC for tracking formulation, these issues are solved by the addition of an artificial equilibrium point as a new decision variable, providing a significantly enlarged domain of attraction and guaranteeing recursive feasibility for any reference change. However, it may suffer from performance issues if the prediction horizon is not large enough. This article presents an extension of this formulation where a harmonic artificial reference is used in place of the equilibrium point. The proposed formulation achieves even greater domains of attraction and can significantly outperform other MPC formulations when the prediction horizon is small. We prove the asymptotic stability and recursive feasibility of the proposed controller, as well as provide guidelines for the design of its main ingredients. Finally, we highlight its advantages with a case study of a ball and plate system.
Next Generation Hemostatic Materials Based on NHS-Ester Functionalized Poly(2-oxazoline)s

In order to prevent hemorrhage during surgical procedures, a wide range of hemostatic agents have been developed. However, their efficacy is variable; hemostatic devices that use bioactive components to accelerate coagulation are dependent on natural sources, which limits reproducibility. Hybrid devices in which chain-end reactive poly(ethylene glycol) is employed as active component sometimes suffer from irregular cross-linking and dissolution of the polar PEG when blood flow is substantial. Herein, we describe a synthetic, nonbioactive hemostatic product by coating N-hydroxysuccinimide ester (NHS)-functional poly(2-oxazoline)s (POx-NHS) onto gelatin patches, which acts by formation of covalent cross-links between polymer, host blood proteins, gelatin and tissue to seal the wound site and prevent hemorrhage during surgery. We studied different process parameters (including polymer, carrier, and coating technique) in direct comparison with clinical products (Hemopatch and Tachosil) to obtain deeper understanding of this class of hemostatic products. In this work, we successfully prove the hemostatic efficacy of POx-NHS as polymer powders and coated patches both in vitro and in vivo against Hemopatch and Tachosil, demonstrating that POx-NHS are excellent candidate polymers for the development of next generation hemostatic patches.
Importance of specific rehabilitation methods for patients with intracerebral hemorrage

Abstract Intracerebral hemorrhage (ICH) is a severe type of stroke which causes bleeding in the brain parenchyma, accounting for 10-15% of all strokes. Prognosis of patients with ICH remains very poor despite new advances in management strategies, and has not improved in the last decades. Disability remains important in ICH survivors, only a small proportion of patients live an independent life after ICH. Development of new strategies in rehabilitation care is expected to improve survival and outcome after intracerebral hemorrhage. The main neurological sequellae after ICH are similar to those seen after ischemic stroke. Given the clinical and pathological differences between the two types of stroke, clinical recovery is expected to be different. Comparisons between rehabilitation results in hemorrhagic versus ischemic stroke have offered mixed results, but all of them agreed that rehabilitation is highly effective after hemorrhagic strokes. There are no standard recommendations regarding the specific procedures used in the rehabilitation program of ICH patients. In the absence of clinical data to guide specific practice, the rehabilitation of ICH patients is largely based on general principles learned from ischemic stroke recovery. Physical therapy, task-specific training, sensory stimulation, use of assistive devices for upper limb or gait functions, speech and language therapy, physiotherapy and balnear therapy are methods used in rehabilitation of patients with hemorrhagic stroke, with improvement in patients functional outcome. There is a need for further development of specific rehabilitation techniques to enhance recovery in ICH patients.