Miracle-dz/UniFAR-FTUs huggingface dataset:
Galaxy Power Spectrum and Biasing Results from the LOFAR Two-meter Sky Survey (First Data Release)

The Low-Frequency Array (LOFAR) Two-meter Sky Survey (LoTSS) is an ongoing survey aiming to observe the entire northern sky, providing an excellent opportunity to study the distribution and evolution of the large-scale structure of the universe. The source catalog from the public LoTSS first data release (DR1) covers 1% of the sky, and shows correlated noise or fluctuations of the flux density calibration on scales of a few degrees. We explore the LoTSS DR1 to understand the survey systematics and data quality of this first data release. We produce catalog mocks to estimate uncertainties, and measure the angular clustering statistics of LoTSS galaxies, which fit the Lambda cold dark matter cosmology reasonably well. We employ a Markov Chain Monte Carlo–based Bayesian analysis to recover the best galaxy biasing scheme and multicomponent source fraction for LoTSS DR1 above 1 mJy assuming different possible redshift templates. After masking some noisy and uneven patches and with suitable flux density cuts, the LOFAR survey appears qualified for large-scale cosmological studies. The upcoming data releases from LOFAR are expected to be deeper and wider, and will therefore provide improved cosmological measurements.
Uncertainty Relation between Detection Probability and Energy Fluctuations

A classical random walker starting on a node of a finite graph will always reach any other node since the search is ergodic, namely it fully explores space, hence the arrival probability is unity. For quantum walks, destructive interference may induce effectively non-ergodic features in such search processes. Under repeated projective local measurements, made on a target state, the final detection of the system is not guaranteed since the Hilbert space is split into a bright subspace and an orthogonal dark one. Using this we find an uncertainty relation for the deviations of the detection probability from its classical counterpart, in terms of the energy fluctuations.
Low-Complexity Probing via Finding Subnetworks

The dominant approach in probing neural networks for linguistic properties is to train a new shallow multi-layer perceptron (MLP) on top of the model’s internal representations. This approach can detect properties encoded in the model, but at the cost of adding new parameters that may learn the task directly. We instead propose a subtractive pruning-based probe, where we find an existing subnetwork that performs the linguistic task of interest. Compared to an MLP, the subnetwork probe achieves both higher accuracy on pre-trained models and lower accuracy on random models, so it is both better at finding properties of interest and worse at learning on its own. Next, by varying the complexity of each probe, we show that subnetwork probing Pareto-dominates MLP probing in that it achieves higher accuracy given any budget of probe complexity. Finally, we analyze the resulting subnetworks across various tasks to locate where each task is encoded, and we find that lower-level tasks are captured in lower layers, reproducing similar findings in past work.
Pseudo-Supervised Deep Subspace Clustering

Auto-Encoder (AE)-based deep subspace clustering (DSC) methods have achieved impressive performance due to the powerful representation extracted using deep neural networks while prioritizing categorical separability. However, self-reconstruction loss of an AE ignores rich useful relation information and might lead to indiscriminative representation, which inevitably degrades the clustering performance. It is also challenging to learn high-level similarity without feeding semantic labels. Another unsolved problem facing DSC is the huge memory cost due to <inline-formula> <tex-math notation="LaTeX">$n\times n$ </tex-math></inline-formula> similarity matrix, which is incurred by the self-expression layer between an encoder and decoder. To tackle these problems, we use pairwise similarity to weigh the reconstruction loss to capture local structure information, while a similarity is learned by the self-expression layer. Pseudo-graphs and pseudo-labels, which allow benefiting from uncertain knowledge acquired during network training, are further employed to supervise similarity learning. Joint learning and iterative training facilitate to obtain an overall optimal solution. Extensive experiments on benchmark datasets demonstrate the superiority of our approach. By combining with the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-nearest neighbors algorithm, we further show that our method can address the large-scale and out-of-sample problems. The source code of our method is available: <monospace><uri>https://github.com/sckangz/SelfsupervisedSC</uri></monospace>.
Can Differential Privacy Practically Protect Collaborative Deep Learning Inference for the Internet of Things?

—Collaborative inference has recently emerged as an intriguing framework for applying deep learning to Internet of Things (IoT) applications, which works by splitting a DNN model into two subpart models respectively on resource-constrained IoT devices and the cloud. Even though IoT applications’ raw input data is not directly exposed to the cloud in such framework, revealing the local-part model’s intermediate output still entails privacy risks. For mitigation of privacy risks, differential privacy could be adopted in principle. However, the practicality of differential privacy for collaborative inference under various conditions remains unclear. For example, it is unclear how the calibration of the privacy budget ǫ will affect the protection strength and model accuracy in presence of the state-of-the-art reconstruction attack targeting collaborative inference, and whether a good privacy-utility balance exists. In this paper, we provide the ﬁrst systematic study to assess the effectiveness of differential privacy for protecting collaborative inference in presence of the reconstruction attack, through extensive empirical evaluations on various datasets. Our results show differential privacy can be used for collaborative inference when confronted with the reconstruction attack, with insights provided about privacy-utility trade-offs. Speciﬁcally, across the evaluated datasets, we observe there exists a suitable privacy budget range (particularly 100 ≤ ǫ ≤ 200 in our evaluation) providing a good trade-off between utility and privacy protection. Our key observation drawn from our study is that differential privacy tends to perform better in collaborative inference for datasets with smaller intra-class variations, which, to our knowledge, is the ﬁrst easy-to-adopt practical guideline.
Recent developments of the Uniform Mordell-Lang Conjecture

This expository survey is based on my online talk at the ICCM 2020. It aims to sketch key steps of the recent proof of the uniform Mordell-Lang conjecture for curves embedded into Jacobians (a question of Mazur). The full version of this conjecture is proved by combining Dimitrov-Gao-Habegger (https://annals.math.princeton.edu/articles/17715) and K\"{u}hne (arXiv:2101.10272). We include in this survey a detailed proof on how to combine these two results, which was implicitly done in another short paper of Dimitrov-Gao-Habegger (arXiv:2009.08505) but not explicitly written in existing literature. At the end of the survey we state some future aspects.
Effects of pomegranate seed flour on dough rheology and bread quality

ABSTRACT The objective of this study was to determine the effects of pomegranate seed flour (PSF) on dough and bread quality. PSF with 15.66% protein, 18.24% lipid, 52.22% dietary fiber, 40.60 g/kg GAE total phenolic and 1049.10 IC50 = μg/ml antiradical activity was substituted for bread wheat flour at 0%, 5%, 7.5% and 10% levels. While 5% PSF did not show any significant effect on water absorption, stability and pressure increased and development time and extensibility decreased as addition level increased. With the addition of PSF in wheat flour, decreases were observed in volumes, widths, heights and brightness of breads, while increases were detected in dietary fiber, a and b values and textural properties such as hardness and chewiness. The study demonstrated that fiber-enriched bread with good sensory and technological quality could be produced by substituting wheat flour with 5% of PSF.
Effects of radius ratio on annular centrifugal Rayleigh–Bénard convection

Abstract We report on a three-dimensional direct numerical simulation study of flow structure and heat transport in the annular centrifugal Rayleigh–Bénard convection (ACRBC) system, with cold inner and hot outer cylinders corotating axially, for the Rayleigh number range $Ra \in [{10^6},{10^8}]$ and radius ratio range $\eta = {R_i}/{R_o} \in [0.3,0.9]$ ($R_i$ and $R_o$ are the radius of the inner and outer cylinders, respectively). This study focuses on the dependence of flow dynamics, heat transport and asymmetric mean temperature fields on the radius ratio $\eta$. For the inverse Rossby number $Ro^{-1} = 1$, as the Coriolis force balances inertial force, the flow is in the inertial regime. The mechanisms of zonal flow revolving in the prograde direction in this regime are attributed to the asymmetric movements of plumes and the different curvatures of the cylinders. The number of roll pairs is smaller than the circular roll hypothesis as the convection rolls are probably elongated by zonal flow. The physical mechanism of zonal flow is verified by the dependence of the drift frequency of the large-scale circulation (LSC) rolls and the space- and time-averaged azimuthal velocity on $\eta$. The larger $\eta$ is, the weaker the zonal flow becomes. We show that the heat transport efficiency increases with $\eta$. It is also found that the bulk temperature deviates from the arithmetic mean temperature and the deviation increases as $\eta$ decreases. This effect can be explained by a simple model that accounts for the curvature effects and the radially dependent centrifugal force in ACRBC.
HindSight: A Graph-Based Vision Model Architecture For Representing Part-Whole Hierarchies

This paper presents a model architecture for encoding the representations of part-whole hierarchies in images in form of a graph. The idea is to divide the image into patches of different levels and then treat all of these patches as nodes for a fully connected graph. A dynamic feature extraction module is used to extract feature representations from these patches in each graph iteration. This enables us to learn a rich graph representation of the image that encompasses the inherent part-whole hierarchical information. Utilizing proper self-supervised training techniques, such a model can be trained as a general purpose vision encoder model which can then be used for various vision related downstream tasks (e.g., Image Classification, Object Detection, Image Captioning, etc.).
Multimodal fusion of EMG and vision for human grasp intent inference in prosthetic hand control

Objective: For transradial amputees, robotic prosthetic hands promise to regain the capability to perform daily living activities. Current control methods based on physiological signals such as electromyography (EMG) are prone to yielding poor inference outcomes due to motion artifacts, muscle fatigue, and many more. Vision sensors are a major source of information about the environment state and can play a vital role in inferring feasible and intended gestures. However, visual evidence is also susceptible to its own artifacts, most often due to object occlusion, lighting changes, etc. Multimodal evidence fusion using physiological and vision sensor measurements is a natural approach due to the complementary strengths of these modalities. Methods: In this paper, we present a Bayesian evidence fusion framework for grasp intent inference using eye-view video, eye-gaze, and EMG from the forearm processed by neural network models. We analyze individual and fused performance as a function of time as the hand approaches the object to grasp it. For this purpose, we have also developed novel data processing and augmentation techniques to train neural network components. Results: Our results indicate that, on average, fusion improves the instantaneous upcoming grasp type classification accuracy while in the reaching phase by 13.66% and 14.8%, relative to EMG (81.64% non-fused) and visual evidence (80.5% non-fused) individually, resulting in an overall fusion accuracy of 95.3%. Conclusion: Our experimental data analyses demonstrate that EMG and visual evidence show complementary strengths, and as a consequence, fusion of multimodal evidence can outperform each individual evidence modality at any given time.
MetricGAN+: An Improved Version of MetricGAN for Speech Enhancement

The discrepancy between the cost function used for training a speech enhancement model and human auditory perception usually makes the quality of enhanced speech unsatisfactory. Objective evaluation metrics which consider human perception can hence serve as a bridge to reduce the gap. Our previously proposed MetricGAN was designed to optimize objective metrics by connecting the metric with a discriminator. Because only the scores of the target evaluation functions are needed during training, the metrics can even be non-differentiable. In this study, we propose a MetricGAN+ in which three training techniques incorporating domain-knowledge of speech processing are proposed. With these techniques, experimental results on the VoiceBank-DEMAND dataset show that MetricGAN+ can increase PESQ score by 0.3 compared to the previous MetricGAN and achieve state-of-the-art results (PESQ score = 3.15).
On Mixed Iterated Revisions

Several forms of iterable belief change exist, differing in the kind of change and its strength: some operators introduce formulae, others remove them; some add formulae unconditionally, others only as additions to the previous beliefs; some only relative to the current situation, others in all possible cases. A sequence of changes may involve several of them: for example, the first step is a revision, the second a contraction and the third a refinement of the previous beliefs. The ten operators considered in this article are shown to be all reducible to three: lexicographic revision, refinement and severe withdrawal. In turn, these three can be expressed in terms of lexicographic revision at the cost of restructuring the sequence. This restructuring needs not to be done explicitly: an algorithm that works on the original sequence is shown. The complexity of mixed sequences of belief change operators is also analyzed. Most of them require only a polynomial number of calls to a satisfiability checker, some are even easier.
Massive Access in Media Modulation Based Massive Machine-Type Communications

The massive machine-type communications (mMTC) paradigm based on media modulation in conjunction with massive multi-input multi-output base stations (BSs) is emerging as a viable solution to support the massive connectivity for the future Internet-of-Things, in which the inherent massive access at the BSs poses significant challenges for device activity and data detection (DADD). This paper considers the DADD problem for both uncoded and coded media modulation based mMTC with a slotted access frame structure, where the device activity remains unchanged within one frame. Specifically, due to the slotted access frame structure and the adopted media modulated symbols, the access signals exhibit a doubly structured sparsity in both the time domain and the modulation domain. Inspired by this, a doubly structured approximate message passing (DS-AMP) algorithm is proposed for reliable DADD in the uncoded case. Also, we derive the state evolution of the DS-AMP algorithm to theoretically characterize its performance. As for the coded case, we develop a bit-interleaved coded media modulation scheme and propose an iterative DS-AMP (IDS-AMP) algorithm based on successive inference cancellation (SIC), where the signal components associated with the detected active devices are successively subtracted to improve the data decoding performance. In addition, the channel estimation problem for media modulation based mMTC is discussed and an efficient data-aided channel state information (CSI) update strategy is developed to reduce the training overhead in block fading channels. Finally, simulation results and computational complexity analysis verify the superiority of the proposed DS-AMP algorithm over state-of-the-art algorithms in the uncoded case. Also, our results confirm that the proposed SIC-based IDS-AMP algorithm can enhance the data decoding performance in the coded case and verify the validity of the proposed data-aided CSI update strategy.
BR-NS: an archive-less approach to novelty search

As open-ended learning based on divergent search algorithms such as Novelty Search (NS) draws more and more attention from the research community, it is natural to expect that its application to increasingly complex real-world problems will require the exploration to operate in higher dimensional Behavior Spaces (BSs) which will not necessarily be Euclidean. Novelty Search traditionally relies on k-nearest neighbours search and an archive of previously visited behavior descriptors which are assumed to live in a Euclidean space. This is problematic because of a number of issues. On one hand, Euclidean distance and Nearest-neighbour search are known to behave differently and become less meaningful in high dimensional spaces. On the other hand, the archive has to be bounded since, memory considerations aside, the computational complexity of finding nearest neighbours in that archive grows linearithmically with its size. A sub-optimal bound can result in "cycling" in the behavior space, which inhibits the progress of the exploration. Furthermore, the performance of NS depends on a number of algorithmic choices and hyperparameters, such as the strategies to add or remove elements to the archive and the number of neighbours to use in k-nn search. In this paper, we discuss an alternative approach to novelty estimation, dubbed Behavior Recognition based Novelty Search (BR-NS), which does not require an archive, makes no assumption on the metrics that can be defined in the behavior space and does not rely on nearest neighbours search. We conduct experiments to gain insight into its feasibility and dynamics as well as potential advantages over archive-based NS in terms of time complexity.
Parasite common envelope evolution by triple-star systems

We study a scenario by which a giant wide tertiary star engulfs and forces a tight binary system of a white dwarf (WD) and a main sequence (MS) star to enter a common envelope evolution (CEE) with each other, and then unbinds the WD-MS common envelope. The WD-MS binary system, now with the WD inside the MS envelope, does not have sufficient orbital energy to unbind their common envelope. However, as they approach the center of the giant star Roche lobe overflow to the core of the giant star and/or merger of the WD with the core remove a large fraction of the WD-MS common envelope or all of it. Namely, the energy source for unbinding the WD-MS tight common envelope is the triple-star CEE. For that we term this scenario a parasite CEE. Overall, the destruction of the MS star absorbs energy from the triple-star system, a process that might lead to WD-core merger during the triple-star CEE. The parasite CEE leaves behind either one massive WD that in some cases might explode as a peculiar type Ia supernova or two close WDs that at later time might explode as a type Ia supernova. We very crudely estimate the rate of the parasite CEE to be a fraction of ≈ 0.001 out of all evolved triple stars.
Predicting cation ordering in MgAl2O4 using genetic algorithms and density functional theory

ABSTRACT Genetic algorithms (GAs) together with classical pair potentials and density functional theory (DFT) are used to investigate cation order in MgAl2O4 (Spinel). To efficiently locate the global minimum/minima on the system potential energy surface, corresponding to the ordered and fully equilibrated low-temperature phase, local structural optimizations are essential. Such energy minimizations are expensive at the DFT level, but a comparison of the distribution of the energy minima from DFT and popular classical pair potentials allows one to rapidly tune the GA parameters. We show that GAs are able to find, not only the global minimum on the potential energy, but also other low-energy cation configurations representing possible frozen-in disordered or metastable phases after quenching. The nature of these low-energy configurations can help to interpret the extent of kinetic trapping which hampers the comparison between different experimental studies.
Automated freezing of gait assessment with marker-based motion capture and multi-stage graph convolutional neural networks approaches expert-level detection

—Freezing of gait (FOG) is a common and debilitating gait impairment in Parkinson’s disease. Further insight in this phenomenon is hampered by the difﬁculty to objectively assess FOG. To meet this clinical need, this paper proposes a motion capture-based FOG assessment method driven by a novel deep neural network. The proposed network, termed multi-stage graph convolutional network (MS-GCN), combines the spatial-temporal graph convolutional network (ST-GCN) and the multi-stage temporal convolutional network (MS-TCN). The ST-GCN captures the hierarchical motion among the optical markers inherent to motion capture, while the multi-stage component reduces over-segmentation errors by reﬁning the predictions over multiple stages. The proposed model was validated on a dataset of fourteen freezers, fourteen non-freezers, and fourteen healthy control subjects. The experiments indicate that the proposed model outperforms state-of-the-art baselines. An in-depth quantitative and qualitative analysis demonstrates that the proposed model is able to achieve clinician-like FOG assessment. The proposed MS-GCN can provide an automated and objective alternative to labor-intensive clinician-based FOG assessment.
Cross-Cultural Similarity Features for Cross-Lingual Transfer Learning of Pragmatically Motivated Tasks

Much work in cross-lingual transfer learning explored how to select better transfer languages for multilingual tasks, primarily focusing on typological and genealogical similarities between languages. We hypothesize that these measures of linguistic proximity are not enough when working with pragmatically-motivated tasks, such as sentiment analysis. As an alternative, we introduce three linguistic features that capture cross-cultural similarities that manifest in linguistic patterns and quantify distinct aspects of language pragmatics: language context-level, figurative language, and the lexification of emotion concepts. Our analyses show that the proposed pragmatic features do capture cross-cultural similarities and align well with existing work in sociolinguistics and linguistic anthropology. We further corroborate the effectiveness of pragmatically-driven transfer in the downstream task of choosing transfer languages for cross-lingual sentiment analysis.