Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting H2O with CRIRES+: WASP-20b

Infrared spectroscopy over a wide spectral range and at the highest resolving powers (R>70 000) has proved to be one of the leading technique to unveil the atmospheric composition of dozens of exoplanets. The recently upgraded spectrograph CRIRES instrument at the VLT (CRIRES+) was operative for a first Science Verification in September 2021 and its new capabilities in atmospheric characterisation were ready to be tested. We analysed transmission spectra of the Hot Saturn WASP-20b in the K-band (1981-2394 nm) acquired with CRIRES+, aiming at detecting the signature of H2O and CO. We used Principal Component Analysis to remove the dominant time-dependent contaminating sources such as telluric bands and the stellar spectrum and we extracted the planet spectrum by cross-correlating observations with 1D and 3D synthetic spectra, with no circulation included. We present the tentative detection of molecular absorption from water-vapour at S/N equal to 4.2 and 4.7 by using only-H2O 1D and 3D models, respectively. The peak of the CCF occurred at the same rest-frame velocity for both model types (Vrest=-1 $\pm$ 1 kms$^{-1}$), and at the same projected planet orbital velocity but with different error bands (1D model: KP=131$^{+18}_{-29}$ kms$^{-1}$; 3D: KP=131$^{+23}_{-39}$ kms$^{-1}$). Our results are in agreement with the one expected in literature (132.9 $\pm$ 2.7 kms$^{-1}$). Although sub-optimal observational conditions and issues with pipeline in calibrating and reducing our raw data set, we obtained the first tentative detection of water in the atmosphere of WASP-20b. We suggest a deeper analysis and additional observations to confirm our results and unveil the presence of CO.
Edge-oriented Implicit Neural Representation with Channel Tuning

Implicit neural representation, which expresses an image as a continuous function rather than a discrete grid form, is widely used for image processing. Despite its outperforming results, there are still remaining limitations on restoring clear shapes of a given signal such as the edges of an image. In this paper, we propose Gradient Magnitude Adjustment algorithm which calculates the gradient of an image for training the implicit representation. In addition, we propose Edge-oriented Representation Network (EoREN) that can reconstruct the image with clear edges by fitting gradient information (Edge-oriented module). Furthermore, we add Channel-tuning module to adjust the distribution of given signals so that it solves a chronic problem of fitting gradients. By separating backpropagation paths of the two modules, EoREN can learn true color of the image without hindering the role for gradients. We qualitatively show that our model can reconstruct complex signals and demonstrate general reconstruction ability of our model with quantitative results.
Normal functions and maximal order types

Transformations of well partial orders induce functions on the ordinals, via the notion of maximal order type. In most examples from the literature, these functions are not normal, in marked contrast with the central role that normal functions play in ordinal analysis and related work from computability theory. The present paper aims to explain this phenomenon. In order to do so, we investigate a rich class of order transformations that are known as $\textsf {WPO}$-dilators. According to a first main result of this paper, $\textsf {WPO}$-dilators induce normal functions when they satisfy a rather restrictive condition, which we call strong normality. Moreover, the reverse implication holds as well, for reasonably well-behaved $\textsf {WPO}$-dilators. Strong normality also allows us to explain another phenomenon: by previous work of Freund, Rathjen and Weiermann, a uniform Kruskal theorem for $\textsf {WPO}$-dilators is as strong as $\varPi ^1_1$-comprehension, while the corresponding result for normal dilators on linear orders is equivalent to the much weaker principle of $\varPi ^1_1$-induction. As our second main result, we show that $\varPi ^1_1$-induction is equivalent to the uniform Kruskal theorem for $\textsf {WPO}$-dilators that are strongly normal.
StyleTime: Style Transfer for Synthetic Time Series Generation

Neural style transfer is a powerful computer vision technique that can incorporate the artistic “style" of one image to the “content" of another. The underlying theory behind the approach relies on the assumption that the style of an image is represented by the Gram matrix of its features, which is typically extracted from pre-trained convolutional neural networks (e.g., VGG-19). This idea does not straightforwardly extend to time series stylization since notions of style for two-dimensional images are not analogous to notions of style for one-dimensional time series. In this work, a novel formulation of time series style transfer is proposed for the purpose of synthetic data generation and enhancement. We introduce the concept of stylized features for time series, which is directly related to the time series realism properties, and propose a novel stylization algorithm, called StyleTime, that uses explicit feature extraction techniques to combine the underlying content (trend) of one time series with the style (distributional properties) of another. Further, we discuss evaluation metrics, and compare our work to existing state-of-the-art time series generation and augmentation schemes. To validate the effectiveness of our methods, we use stylized synthetic data as a means for data augmentation to improve the performance of recurrent neural network models on several forecasting tasks.
Solving Robot Assembly Tasks by Combining Interactive Teaching and Self-Exploration

Many high precision (dis)assembly tasks are still being performed by humans, whereas this is an ideal opportunity for automation. This paper provides a framework which enables a non-expert human operator to teach a robotic arm to do complex precision tasks. The framework uses a variable Cartesian impedance controller to execute trajectories learned from kinesthetic human demonstrations. Feedback can be given to interactively reshape or speed up the original demonstration. Board localization is done through a visual estimation of the task board position and refined through haptic feedback. Our framework is tested on the Robothon benchmark disassembly challenge, where the robot has to perform complex precision tasks, such as a key insertion. The results show high success rates for each of the manipulation subtasks, including cases when the box is in novel poses. An ablation study is also performed to evaluate the components of the framework.
Rethinking Missing Data: Aleatoric Uncertainty-Aware Recommendation

Historical interactions are the default choice for recommender model training, which typically exhibit high sparsity, i.e., most user-item pairs are unobserved missing data. A standard choice is treating the missing data as negative training samples and estimating interaction likelihood between user-item pairs along with the observed interactions. In this way, some potential interactions are inevitably mislabeled during training, which will hurt the model fidelity, hindering the model to recall the mislabeled items, especially the long-tail ones. In this work, we investigate the mislabeling issue from a new perspective of aleatoric uncertainty, which describes the inherent randomness of missing data. The randomness pushes us to go beyond merely the interaction likelihood and embrace aleatoric uncertainty modeling. Towards this end, we propose a new Aleatoric Uncertainty-aware Recommendation (AUR) framework that consists of a new uncertainty estimator along with a normal recommender model. According to the theory of aleatoric uncertainty, we derive a new recommendation objective to learn the estimator. As the chance of mislabeling reflects the potential of a pair, AUR makes recommendations according to the uncertainty, which is demonstrated to improve the recommendation performance of less popular items without sacrificing the overall performance. We instantiate AUR on three representative recommender models: Matrix Factorization (MF), LightGCN, and VAE from mainstream model architectures. Extensive results on four real-world datasets validate the effectiveness of AUR w.r. t. better recommendation results, especially on long-tail items.
On Efficient Reinforcement Learning for Full-length Game of StarCraft II

StarCraft II (SC2) poses a grand challenge for reinforcement learning (RL), of which the main difficulties include huge state space, varying action space, and a long time horizon. In this work, we investigate a set of RL techniques for the full-length game of StarCraft II. We investigate a hierarchical RL approach, where the hierarchy involves two. One is the extracted macro-actions from experts’ demonstration trajectories to reduce the action space in an order of magnitude. The other is a hierarchical architecture of neural networks, which is modular and facilitates scale. We investigate a curriculum transfer training procedure that trains the agent from the simplest level to the hardest level. We train the agent on a single machine with 4 GPUs and 48 CPU threads. On a 64x64 map and using restrictive units, we achieve a win rate of 99% against the difficulty level-1 built-in AI. Through the curriculum transfer learning algorithm and a mixture of combat models, we achieve a 93% win rate against the most difficult non-cheating level built-in AI (level-7). In this extended version of the paper, we improve our architecture to train the agent against the most difficult cheating level AIs (level-8, level-9, and level-10). We also test our method on different maps to evaluate the extensibility of our approach. By a final 3-layer hierarchical architecture and applying significant tricks to train SC2 agents, we increase the win rate against the level-8, level-9, and level-10 to 96%, 97%, and 94%, respectively. Our codes and models are all open-sourced now at https://github.com/liuruoze/HierNet-SC2. To provide a baseline referring the AlphaStar for our work as well as the research and open-source community, we reproduce a scaled-down version of it, mini-AlphaStar (mAS). The latest version of mAS is 1.07, which can be trained using supervised learning and reinforcement learning on the raw action space which has 564 actions. It is designed to run training on a single common machine, by making the hyper-parameters adjustable and some settings simplified. We then can compare our work with mAS using the same computing resources and training time. By experiment results, we show that our method is more effective when using limited resources. The inference and training codes of mini-AlphaStar are all open-sourced at https://github.com/liuruoze/mini-AlphaStar. We hope our study could shed some light on the future research of efficient reinforcement learning on SC2 and other large-scale games.
Towards Auditing the Sensitive Information Leakage During Data Trading and Data Computing

Data markets enable users to own their data and trade their data for profits. Existing data market designs allow these data owners to directly sell or provide computation services on their private data in a secure and privacy-preserving way. However, an important problem that is neglected in previous work is that the transferred data or computation results over the data can leak information about other sensitive features that are not involved in the data market. To tackle this problem, we propose an auditing system based on smart contracts and zero-knowledge proof to measure and manage the sensitive information leakage risk in Web3 data markets. Primary results on the information leakage estimation show that even a small percent of information leakage can lead to significant predictability by simple machine learning models.
Determinants Of Migration: Linear regression Analysis in Indian Context

: Environmental degradation, global pandemic and severing natural resource related problems cater to increase demand resulting from migration is nightmare for all of us. Huge flocks of people are rushing towards to earn, to live and to lead a better life. This they do for their own development often ignoring the environmental cost. With existing model, this paper looks at out migration (interstate) within India focusing on the various proximate and fundamental causes relating to migration. The author deploys OLS to see those fundamental causes. Obviously, these are not exhaustive cause, but definitely plays a role in migration decision of individual. Finally, this paper advocates for some policy prescription to cope with this problem.
Web‐spline‐based finite element approximation of some quasi‐newtonian flows: Existence‐uniqueness and error bound

This article deals with the web‐spline‐based finite element approximation of quasi‐Newtonian flows. First, we consider the scalar elliptic p‐Laplace problem. Then, we consider quasi‐Newtonian flows where viscosity obeys power law or Carreau law. We prove well‐posedness at the continuous as well as the discrete level. We give some error bounds for the solution of quasi‐Newtonian flow problem based on the web‐spline method. Finally, we provide the numerical results for the p‐Laplace problem. © 2014 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq31: 54–77, 2015
Deep Learning for Multi-User Proactive Beam Handoff: A 6G Application

This paper demonstrates the use of deep learning and time series data generated from user equipment (UE) beam measurements and positions collected by the base station (BS) to enable handoffs between beams that belong to the same or different BSs. We propose the use of long short-term memory (LSTM) recurrent neural networks with three different approaches and vary the number of lookbacks of the beam measurements to study the performance of the prediction used for the proactive beam handoff. Simulations show that while UE positions can improve the prediction performance, it is only up to a certain point. At a sufficiently large number of lookbacks, the UE positions become irrelevant to the prediction accuracy since the LSTMs are able to learn the optimal beam based on implicitly defined positions from the time-defined trajectories.
Terahertz Scanless Hypertemporal Imaging

Since its first demonstration in 1995, terahertz time‐domain imaging has attracted an increasingly growing interest for its ability to reveal spectral fingerprints of materials and probe changes in refractive index and absorption, as well as detect the inner structure of complex objects via time‐of‐flight measurements. Practically, however, its widespread use has been hampered by the very long acquisition time typically required to spatially raster‐scan the object, and for each spatial point, record the field in time via a delay line. Here, this fundamental bottleneck is addressed by implementing a scanless single‐pixel imaging scheme, which sets the path for an unprecedented reduction of both system complexity and acquisition time. By properly exploiting natural wave diffraction, time‐to‐space encoding applied to terahertz point detection allows for an almost instantaneous capture of the terahertz waveforms, while multidimensional images are reconstructed via a computational approach. The scheme is a promising solution for the development of next‐generation fast and compact terahertz imagers perfectly suitable for high‐repetition‐rate laser sources.
Twisting Manin's universal quantum groups and comodule algebras

We introduce the notion of quantum-symmetric equivalence of two connected graded algebras, based on Morita--Takeuchi equivalences of their universal quantum groups, in the sense of Manin. We study homological and algebraic invariants of quantum-symmetric equivalence classes, and in particular prove that numerical $\mathrm{Tor}$-regularity, Castelnuovo--Mumford regularity, Artin--Schelter regularity, and the Frobenius property are invariant under any Morita--Takeuchi equivalence. We characterize 2-cocycle twists (which arise as a special case of quantum-symmetric equivalence) of Koszul duals, superpotentials and superpotential algebras, Nakayama automorphisms of twisted Frobenius algebras, and Artin--Schelter regular algebras. We also show that finite generation of Hochschild cohomology rings is preserved under certain 2-cocycle twists.
Memory and transduction prospects for silicon T centre devices

The T centre, a silicon-native spin-photon interface with telecommunications-band optical transitions and long-lived microwave qubits, oﬀers an appealing new platform for both optical quantum memory and microwave to optical telecommunications band transduction. A wide range of quantum memory and transduction schemes could be implemented on such a platform, with advantages and disadvantages that depend sensitively on the ensemble properties. In this work we characterize T centre spin ensembles to inform device design. We perform the ﬁrst T ensemble optical depth measurement and calculate the improvement in centre density or resonant optical enhancement required for eﬃcient optical quantum memory. We further demonstrate a coherent microwave interface by coherent population trapping (CPT) and Autler-Townes splitting (ATS). We then determine the most promising microwave and optical quantum memory protocol for such ensembles. By estimat-ing the memory eﬃciency both in free-space and in the presence of a cavity, we show that eﬃcient optical memory is possible with forecast densities. Finally, we formulate a transduction proposal and discuss the achievable eﬃciency and ﬁdelity.
Anomaly Detection on Financial Time Series by Principal Component Analysis and Neural Networks

A major concern when dealing with financial time series involving a wide variety ofmarket risk factors is the presence of anomalies. These induce a miscalibration of the models used toquantify and manage risk, resulting in potential erroneous risk measures. We propose an approachthat aims to improve anomaly detection in financial time series, overcoming most of the inherentdifficulties. Valuable features are extracted from the time series by compressing and reconstructingthe data through principal component analysis. We then define an anomaly score using a feedforwardneural network. A time series is considered to be contaminated when its anomaly score exceeds agiven cutoff value. This cutoff value is not a hand-set parameter but rather is calibrated as a neuralnetwork parameter throughout the minimization of a customized loss function. The efficiency of theproposed approach compared to several well-known anomaly detection algorithms is numericallydemonstrated on both synthetic and real data sets, with high and stable performance being achievedwith the PCA NN approach. We show that value-at-risk estimation errors are reduced when theproposed anomaly detection model is used with a basic imputation approach to correct the anomaly.
Energy transfer from tunneling electrons to excitons

Excitons in optoelectronic devices have been generated through optical excitation, external carrier injection, or employing pre-existing charges. Here, we reveal a new way to electrically generate excitons in transition metal dichalcogenides (TMDs). The TMD is placed on top of a gold-hBN-graphene tunnel junction, outside of the tunneling pathway. This electrically driven device features a photoemission spectrum with a distinct peak at the exciton energy of the TMD. We interpret this observation as exciton generation by energy transfer from tunneling electrons, which is further supported by a theoretical model based on inelastic electron tunneling. Our findings introduce a new paradigm for exciton creation in van der Waals heterostructures and provide inspiration for a new class of optoelectronic devices in which the optically active material is separated from the electrical pathway.
Capsule Network based Contrastive Learning of Unsupervised Visual Representations

—Capsule Networks have shown tremendous advance- ment in the past decade, outperforming the traditional CNNs in various task due to it’s equivariant properties. With the use of vector I/O which provides information of both magnitude and direction of an object or it’s part, there lies an enormous possibility of using Capsule Networks in unsupervised learning environment for visual representation tasks such as multi class image classiﬁca- tion. In this paper, we propose Contrastive Capsule (CoCa) Model which is a Siamese style Capsule Network using Contrastive loss with our novel architecture, training and testing algorithm. We evaluate the model on unsupervised image classiﬁcation CIFAR- 10 dataset and achieve a top-1 test accuracy of 70.50% and top-5 test accuracy of 98.10%. Due to our efﬁcient architecture our model has 31 times less parameters and 71 times less FLOPs than the current SOTA in both supervised and unsupervised learning. available at https://github.com/Harsh9524/CoCa
WS-3D-Lane: Weakly Supervised 3D Lane Detection With 2D Lane Labels

Compared to 2D lanes, real 3D lane data is difficult to collect accurately. In this paper, we propose a novel method for training 3D lanes with only 2D lane labels, called weakly supervised 3D lane detection WS-3D-Lane. By assumptions of constant lane width and equal height on adjacent lanes, we indirectly supervise 3D lane heights in the training. To overcome the problem of the dynamic change of the camera pitch during data collection, a camera pitch self-calibration method is proposed. In anchor representation, we propose a double-layer anchor with non-maximum suppression (NMS) method, which enables the anchor-based method to predict two lane lines that are close. Experiments are conducted on the base of 3D-LaneNet under two supervision methods. Under weakly supervised setting, our WS-3D-Lane outperforms previous 3D-LaneN et: F-score rises to 92.3% on Apollo 3D synthetic dataset, and F1 rises to 74.5% on ONCE-3DLanes. Meanwhile, WS-3D-Lane in purely supervised setting makes more increments and outperforms state-of-the-art. To the best of our knowledge, WS-3D- Lane is the first try of 3D lane detection under weakly supervised setting. Our code is available on https://github.com/SAIC-Vision/WS-3D-Lane.
The Beauty of Repetition in Machine Composition Scenarios

Repetition, a basic form of artistic creation, appears in most musical works and delivers enthralling aesthetic experiences. However, repetition remains underexplored in terms of automatic music composition. As an initial effort in repetition modelling, this paper focuses on generating motif-level repetitions via domain knowledge-based and example-based learning techniques. A novel repetition transformer (R-Transformer) that combines a Transformer encoder and a repetition-aware learner is trained on a new repetition dataset with 584,329 samples from different categories of motif repetition. The Transformer encoder learns the representation among music notes from the repetition dataset; the novel repetition-aware learner exploits repetitions' unique characteristics based on music theory. Experiments show that, with any given motif, R-Transformer can generate a large number of variable and beautiful repetitions. With ingenious fusion of these high-quality pieces, the musicality and appeal of machine-composed music have been greatly improved.