Miracle-dz/UniFAR-FTUs huggingface dataset:
Electron doping of a double-perovskite flat-band system

Significance Flat-band electronic materials have been a keen research topic in theoretical materials physics due to their potential for hosting strongly correlated electrons and hence exotic physical phenomena. This study explores the electronic flat band (i.e., an electronic band that has minimal variation in energy over a significant portion of the Brillouin zone) located just above the Fermi level in Sr2FeSbO6. The origin of the flat band is theoretically determined, distinguishing it from ordinary bands, and to experimentally place the Fermi energy in that flat band, electron-doped samples are synthesized and analyzed, yielding some features that cannot be adequately described by classic physics in full. Our work shows that this doped double perovskite is an unusual electronic system worthy of further study.
Labor Income Risk and the Cross-Section of Expected Returns

This paper explores asset pricing implications of unemployment risk from sectoral shifts. I proxy for this risk using cross-industry dispersion (CID), deﬁned as a mean absolute deviation of returns of 49 industry portfolios. CID peaks during periods of accelerated sectoral reallocation and heightened uncertainty. I ﬁnd that expected stock returns are related cross-sectionally to the sensitivities of returns to innovations in CID. Annualized returns of the stocks with high sensitivity to CID are 5.9% lower than the returns of the stocks with low sensitivity. Abnormal returns with respect to the best factor model are 3.5%, suggesting that common factors can not explain this return spread. Stocks with high sensitivity to CID are likely to be the stocks, which beneﬁted from sectoral shifts. CID positively predicts unemployment through its long-term component, consistent with the hypothesis that CID is a proxy for unemployment risk from sectoral shifts.
HeMPPCAT: Mixtures of Probabilistic Principal Component analysers for data with heteroscedastic noise

Mixtures of probabilistic principal component analysis (MPPCA) is a well-known mixture model extension of principal component analysis (PCA). Similar to PCA, MPPCA assumes the data samples in each mixture contain homoscedastic noise. However, datasets with heterogeneous noise across samples are becoming increasingly common, as larger datasets are generated by collecting samples from several sources with varying noise profiles. The performance of MPPCA is suboptimal for data with heteroscedastic noise across samples. This paper proposes a heteroscedastic mixtures of probabilistic PCA technique (HeMPPCAT) that uses a gen-eralized expectation-maximization (GEM) algorithm to jointly estimate the unknown underlying factors, means, and noise variances under a heteroscedastic noise setting. Simulation results illustrate the improved factor estimates and clustering accuracies of HeMPPCAT compared to MPPCA.
Delta-MRI: Direct Deformation Estimation from Longitudinally Acquired K-Space Data

Longitudinal MRI is an important diagnostic imaging tool for evaluating the effects of treatment and monitoring disease progression. However, MRI, and particularly longitudinal MRI, is known to be time consuming. To accelerate imaging, compressed sensing (CS) theory has been applied to exploit sparsity, both on single image as on image sequence level. State-of-the-art CS methods however, are generally focused on image reconstruction, and consider analysis (e.g., alignment, change detection) as a post-processing step.In this study, we propose DELTA-MRI, a novel framework to estimate longitudinal image changes directly from a reference image and subsequently acquired, strongly sub-sampled MRI k-space data. In contrast to state-of-the-art longitudinal CS based imaging, our method avoids the conventional multi-step process of image reconstruction of subsequent images, image alignment, and deformation vector field computation. Instead, the set of follow-up images, along with motion and deformation vector fields that describe their relation to the reference image, are estimated in one go. Experiments show that DELTA-MRI performs significantly better than the state-of-the-art in terms of the normalized reconstruction error.
Disk drive pivot nonlinearity modeling and compensation through fuzzy logic

The pivot nonlinearity of a disk drive actuator is a complex phenomenon. Various models and compensation schemes were designed to cope with the nonlinearity. In this paper, a fuzzy logic based actuator pivot nonlinearity model is proposed. The fuzzy model can learn the dynamics of the actuator pivot nonlinearity by training with measured data. After the fuzzy model is trained, it can be augmented into a servo controller design as a disturbance observer to cancel the effects of the pivot nonlinearity. The fuzzy model, its modeling performance and a laboratory implementation to demonstrate its effectiveness in pivot nonlinearity compensation are presented.
Tidally perturbed gravity-mode pulsations in a sample of close eclipsing binaries

Context. Thanks to the high-precision photometry from space missions such as Kepler and TESS, tidal perturbations and tilting of pulsations have been detected in more than a dozen binary systems. However, only two of these were g-mode pulsators. Aims. We aim to detect tidally perturbed g modes in additional binary systems and characterise them observationally. Methods. We perform a custom data reduction of the available Kepler and TESS photometry of a well-studied sample of 35 binary systems with gamma Doradus pulsators. For each target, we model the binary signal using a sum of 100 sine waves, with frequencies at orbital harmonics, and measure significant pulsation frequencies by iteratively prewhitening the residual light curve. Pulsations are labelled as tidally perturbed g modes if they are part of both period-spacing patterns and orbital-frequency-spaced multiplets. After visual inspection and confirmation, the properties of these targets and g modes are characterised. Results. We detect tidally perturbed g-mode pulsations for five short-period binaries that are circularised and (almost) synchronously rotating: KIC3228863, KIC3341457, KIC4947528, KIC9108579, and KIC12785282. Tidally perturbed g modes that occur within the same star and have the same mode identification (k,m), are found to have near-identical relative amplitude and phase modulations, which are within their respective 1-sigma uncertainties also identical for the Kepler and TESS photometric passbands. By contrast, pulsations with different mode identification (k,m) are found to exhibit different modulations. Moreover, the observed amplitude and phase modulations are correlated, indicating that the binary tides primarily distort g-mode amplitudes on the stellar surface. The phase modulations are then primarily a geometric effect of the integration of the stellar flux over the visible stellar surface. (abbreviated)
Explainable Quantum Machine Learning

Methods of artiﬁcial intelligence (AI) and especially machine learning (ML) have been growing ever more complex, and at the same time have more and more impact on people’s lives. This leads to explainable AI (XAI) manifesting itself as an important research ﬁeld that helps humans to better comprehend ML systems. In parallel, quantum machine learning (QML) is emerging with the ongoing improvement of quantum computing hardware combined with its increasing availability via cloud services. QML enables quantum-enhanced ML in which quantum mechanics is exploited to facilitate ML tasks, typically in form of quantum-classical hybrid algorithms that combine quantum and classical resources. Quantum gates constitute the building blocks of gate-based quantum hardware and form circuits that can be used for quantum computations. For QML applications, quantum circuits are typically parameterized and their parameters are optimized classically such that a suitably deﬁned objective function is minimized. Inspired by XAI, we raise the question of explainability of such circuits by quantifying the importance of (groups of) gates for speciﬁc goals. To this end, we transfer and adapt the well-established concept of Shapley values to the quantum realm. The resulting attributions can be interpreted as explanations for why a speciﬁc circuit works well for a given task, improving the understanding of how to construct parameterized (or variational) quantum circuits, and fostering their human interpretability in general. An experimental evaluation on simulators and two superconducting quantum hardware devices demonstrates the beneﬁts of the proposed framework for classiﬁcation, generative modeling, transpilation, and optimization. Furthermore, our results shed some light on the role of speciﬁc gates in popular QML approaches.
VeraSel: Verifiable Random Selection for Mixnets Construction

The security and performance of Mixnets depends on the trustworthiness of the Mixnodes in the network. The challenge is to limit the adversary's influence on which Mixnodes operate in the network. A trusted party (such as the Mixnet operator) may ensure this, however, it is a single point of failure in the event of corruption or coercion. Therefore, we study the problem of how to select a subset of Mixnodes in a distributed way for Mixnet construction. We present VeraSel, a scheme that enables Mixnodes to be chosen according to their weights in a distributed, unbiased, and verifiable fashion using Verifiable Random Functions (VRFs). It is shown that VeraSel enables any party to learn and verify which nodes has been selected based on the commitments and proofs generated by each Mixnode with VRF.
REDAffectiveLM: Leveraging Affect Enriched Embedding and Transformer-based Neural Language Model for Readers' Emotion Detection

Technological advancements in web platforms allow people to express and share emotions towards textual write-ups written and shared by others. This brings about different interesting domains for analysis; emotion expressed by the writer and emotion elicited from the readers. In this paper, we propose a novel approach for Readers' Emotion Detection from short-text documents using a deep learning model called REDAffectiveLM. Within state-of-the-art NLP tasks, it is well understood that utilizing context-specific representations from transformer-based pre-trained language models helps achieve improved performance. Within this affective computing task, we explore how incorporating affective information can further enhance performance. Towards this, we leverage context-specific and affect enriched representations by using a transformer-based pre-trained language model in tandem with affect enriched Bi-LSTM+Attention. For empirical evaluation, we procure a new dataset REN-20k, besides using RENh-4k and SemEval-2007. We evaluate the performance of our REDAffectiveLM rigorously across these datasets, against a vast set of state-of-the-art baselines, where our model consistently outperforms baselines and obtains statistically significant results. Our results establish that utilizing affect enriched representation along with context-specific representation within a neural architecture can considerably enhance readers' emotion detection. Since the impact of affect enrichment specifically in readers' emotion detection isn't well explored, we conduct a detailed analysis over affect enriched Bi-LSTM+Attention using qualitative and quantitative model behavior evaluation techniques. We observe that compared to conventional semantic embedding, affect enriched embedding increases ability of the network to effectively identify and assign weightage to key terms responsible for readers' emotion detection.
SuperScaler: Supporting Flexible DNN Parallelization via a Unified Abstraction

With the growing model size, deep neural networks (DNN) are increasingly trained over massive GPU accelerators, which demands a proper parallelization plan that transforms a DNN model into fine-grained tasks and then schedules them to GPUs for execution. Due to the large search space, the contemporary parallelization plan generators often rely on empirical rules that couple transformation and scheduling, and fall short in exploring more flexible schedules that yield better memory usage and compute efficiency. This tension can be exacerbated by the emerging models with increasing complexity in their structure and model size. SuperScaler is a system that facilitates the design and generation of highly flexible parallelization plans. It formulates the plan design and generation into three sequential phases explicitly: model transformation, space-time scheduling, and data dependency preserving. Such a principled approach decouples multiple seemingly intertwined factors and enables the composition of highly flexible parallelization plans. As a result, SuperScaler can not only generate empirical parallelization plans, but also construct new plans that achieve up to 3.5X speedup compared to state-of-the-art solutions like DeepSpeed, Megatron and Alpa, for emerging DNN models like Swin-Transformer and AlphaFold2, as well as well-optimized models like GPT-3.
Environment for the Design and Automation of New CDPR Architectures

This paper presents a design and automation environment to study the control trajectory for new CDPR architectures, for instance CDPRs with an unusual number of cables or different motor location in the robot frame. In order to test the environment capabilities, an architecture of a planar under-constrained CDPR was designed, simulated, and implemented using standard industrial hardware. Both the simulated model and industrial prototype were running the same trajectories to determine the time delay and the error position between them. The tests have demonstrated that the simulated model of the CDPR reproduces the trajectories of the equivalent industrial prototype with a maximum deviation of 0.35% under loading and different speed conditions, despite the time delays produced by the data transmission and the non-deterministic communication protocols used to connect the industrial automation controller with the simulated model. The results have shown that the environment is suitable for trajectory control and workspace analysis of new CDPR architectures under different dynamic conditions.
A Semantic Modular Framework for Events Topic Modeling in Social Media

The advancement of social media contributes to the growing amount of content they share frequently. This framework provides a sophisticated place for people to report various real-life events. Detecting these events with the help of natural language processing has received researchers' attention, and various algorithms have been developed for this goal. In this paper, we propose a Semantic Modular Model (SMM) consisting of 5 different modules, namely Distributional Denoising Autoencoder, Incremental Clustering, Semantic Denoising, Defragmentation, and Ranking and Processing. The proposed model aims to (1) cluster various documents and ignore the documents that might not contribute to the identification of events, (2) identify more important and descriptive keywords. Compared to the state-of-the-art methods, the results show that the proposed model has a higher performance in identifying events with lower ranks and extracting keywords for more important events in three English Twitter datasets: FACup, SuperTuesday, and USElection. The proposed method outperformed the best reported results in the mean keyword-precision metric by 7.9\%.
InfiniCity: Infinite-Scale City Synthesis

Toward infinite-scale 3D city synthesis, we propose a novel framework, InfiniCity, which constructs and renders an unconstrainedly large and 3D-grounded environment from random noises. InfiniCity decomposes the seemingly impractical task into three feasible modules, taking advantage of both 2D and 3D data. First, an infinite-pixel image synthesis module generates arbitrary-scale 2D maps from the bird’s-eye view. Next, an octree-based voxel completion module lifts the generated 2D map to 3D octrees. Finally, a voxel-based neural rendering module texturizes the voxels and renders 2D images. InfiniCity can thus synthesize arbitrary-scale and traversable 3D city environments. We quantitatively and qualitatively demonstrate the efficacy of the proposed framework.
Study of Dynamic Interaction Between Low Re Aerodynamic Load and Flexible-Biomimetic Wings with Tailorable Stiffness by FSI Modeling

In the present work, we investigate dynamic interaction and response of ﬂexible bio-inspired morphing wing structure to a low Reynolds aerodynamic load. The aspects of inspiration are as follows. First, the segmentation of the wing into rigid and ﬂexible segments. Considering a leading edge constitution of bone and muscle. In addition to a ﬂexible trailing edge composed of feathers. Second, the material properties provided by experimental biology in literature are adopted such as the bending stiﬀness and Young’s modulus. The development of numerical models allowing non uniform distribution of properties are developed and implemented into an OpenFoam ﬁnite volume solver that couples ﬂuid dynamics to a structural solid dynamics solver through the FSI interface. In the course of this work, the validation is performed for a NACA6409 airfoil considering a rigid segment of 40% and ﬂexible segment 60% chord length in order to test the aero-structure behavior for an aerodynamic load of air ﬂow at low Reynolds number R 𝖊 of 5 × 10 5 for the ﬂuid and feather inspired material properties. The results suggest that bio-inspired techniques can be reproduced in engineering conﬁgurations.
Dissecting the Mid-infrared Heart of M83 with JWST

We present a first look at the MRS observations of the nucleus of the nearby galaxy M83, taken with MIRI on board JWST. The observations show a rich set of emission features from the ionized gas, warm molecular gas, and dust. To begin dissecting the complex processes in this part of the galaxy, we divide the observations into four different regions. We find that the strength of the emission features varies strongly from region to region, with the southeast region displaying the weakest features tracing the dust continuum and interstellar medium (ISM) properties. Comparison between the cold molecular gas traced by the 12CO (1–0) transition with the Atacama Large Millimeter/submillimeter Array and the H2 S(1) transition shows a similar spatial distribution. This is in contrast to the distribution of the much warmer H2 emission from the S(7) transition found to be concentrated around the optical nucleus. We use the rotational emission lines and model the H2 excitation to estimate a total molecular gas mass accounting for the warm H2 component of M(>50 K) H2 = 67.90 (±5.43) × 106 M ⊙. We compare this value to the total gas mass inferred by probing the cold H2 gas through the 12CO (1–0) emission, M(CO) H2 = 17.15 × 106 M ⊙. We estimate that ∼75% of the total molecular gas mass is contained in the warm H2 component. We also identify [O iv] 25.89 μm and [Fe ii] 25.99 μm emission. We propose that the diffuse [Fe ii] 25.99 μm emission might be tracing shocks created during the interactions between the hot wind produced by the starburst and the much cooler ISM above the galactic plane. More detailed studies are needed to confirm such a scenario.
RawHash: enabling fast and accurate real-time analysis of raw nanopore signals for large genomes

Nanopore sequencers generate electrical raw signals in real-time while sequencing long genomic strands. These raw signals can be analyzed as they are generated, providing an opportunity for real-time genome analysis. An important feature of nanopore sequencing, Read Until, can eject strands from sequencers without fully sequencing them, which provides opportunities to computationally reduce the sequencing time and cost. However, existing works utilizing Read Until either 1) require powerful computational resources that may not be available for portable sequencers or 2) lack scalability for large genomes, rendering them inaccurate or ineffective. We propose RawHash, the first mechanism that can accurately and efficiently perform real-time analysis of nanopore raw signals for large genomes using a hash-based similarity search. To enable this, RawHash ensures the signals corresponding to the same DNA content lead to the same hash value, regardless of the slight variations in these signals. RawHash achieves an accurate hash-based similarity search via an effective quantization of the raw signals such that signals corresponding to the same DNA content have the same quantized value and, subsequently, the same hash value. We evaluate RawHash on three applications: 1) read mapping, 2) relative abundance estimation, and 3) contamination analysis. Our evaluations show that RawHash is the only tool that can provide high accuracy and high throughput for analyzing large genomes in real-time. When compared to the state-of-the-art techniques, UNCALLED and Sigmap, RawHash provides 1) 25.8× and 3.4× better average throughput and 2) significantly better accuracy for large genomes, respectively. Source code is available at https://github.com/CMU-SAFARI/RawHash.
Efficient Training Under Limited Resources

Training time budget and size of the dataset are among the factors affecting the performance of a Deep Neural Network (DNN). This paper shows that Neural Architecture Search (NAS), Hyper Parameters Optimization (HPO), and Data Augmentation help DNNs perform much better while these two factors are limited. However, searching for an optimal architecture and the best hyperparameter values besides a good combination of data augmentation techniques under low resources requires many experiments. We present our approach to achieving such a goal in three steps: reducing training epoch time by compressing the model while maintaining the performance compared to the original model, preventing model overfitting when the dataset is small, and performing the hyperparameter tuning. We used NOMAD, which is a blackbox optimization software based on a derivative-free algorithm to do NAS and HPO. Our work achieved an accuracy of 86.0 % on a tiny subset of Mini-ImageNet at the ICLR 2021 Hardware Aware Efficient Training (HAET) Challenge and won second place in the competition. The competition results can be found at haet2021.github. io/challenge and our source code can be found at github.com/DouniaLakhmiri/ICLR\_HAET2021.
Towards NeuroAI: Introducing Neuronal Diversity into Artificial Neural Networks

Throughout history, the development of artificial intelligence, particularly artificial neural networks, has been open to and constantly inspired by the increasingly deepened understanding of the brain, such as the inspiration of neocognitron, which is the pioneering work of convolutional neural networks. Per the motives of the emerging field: NeuroAI, a great amount of neuroscience knowledge can help catalyze the next generation of AI by endowing a network with more powerful capabilities. As we know, the human brain has numerous morphologically and functionally different neurons, while artificial neural networks are almost exclusively built on a single neuron type. In the human brain, neuronal diversity is an enabling factor for all kinds of biological intelligent behaviors. Since an artificial network is a miniature of the human brain, introducing neuronal diversity should be valuable in terms of addressing those essential problems of artificial networks such as efficiency, interpretability, and memory. In this Primer, we first discuss the preliminaries of biological neuronal diversity and the characteristics of information transmission and processing in a biological neuron. Then, we review studies of designing new neurons for artificial networks. Next, we discuss what gains can neuronal diversity bring into artificial networks and exemplary applications in several important fields. Lastly, we discuss the challenges and future directions of neuronal diversity to explore the potential of NeuroAI.