Miracle-dz/UniFAR-FTUs huggingface dataset:
Thermal order in conformal theories

It is widely expected that at sufficiently high temperatures order is always lost, e.g., magnets lose their ferromagnetic properties. We pose the question of whether this is always the case in the context of quantum field theory in $d$ space dimensions. More concretely, one can ask whether there exist critical points (CFTs) which break some global symmetry at arbitrary finite temperature. The most familiar CFTs do not exhibit symmetry breaking at finite temperature, and moreover, in the context of the $\mathrm{AdS}/\mathrm{CFT}$ correspondence, critical points at finite temperature are described by an uncharged black brane which obeys a no-hair theorem. Yet, we show that there exist CFTs which have some of their internal symmetries broken at arbitrary finite temperature. Our main example is a vector model which we study both in the epsilon expansion and arbitrary rank as well as the large rank limit (and arbitrary dimension). The large rank limit of the vector model displays a conformal manifold, a moduli space of vacua, and a deformed moduli space of vacua at finite temperature. The appropriate Nambu-Goldstone bosons including the dilatonlike particle are identified. Using these tools we establish symmetry breaking at finite temperature for finite small $\ensuremath{\epsilon}$. We also prove that a large class of other fixed points, which describe some of the most common quantum magnets, indeed behave as expected and do not break any global symmetry at finite temperature. We discuss some of the consequences of finite temperature symmetry breaking for the spectrum of local operators. Finally, we propose a class of fixed points which appear to be possible candidates for finite temperature symmetry breaking in $d=2$.
Projection & Probability-Driven Black-Box Attack

Generating adversarial examples in a black-box setting retains a significant challenge with vast practical application prospects. In particular, existing black-box attacks suffer from the need for excessive queries, as it is non-trivial to find an appropriate direction to optimize in the high-dimensional space. In this paper, we propose Projection & Probability-driven Black-box Attack (PPBA) to tackle this problem by reducing the solution space and providing better optimization. For reducing the solution space, we first model the adversarial perturbation optimization problem as a process of recovering frequency-sparse perturbations with compressed sensing, under the setting that random noise in the low-frequency space is more likely to be adversarial. We then propose a simple method to construct a low-frequency constrained sensing matrix, which works as a plug-and-play projection matrix to reduce the dimensionality. Such a sensing matrix is shown to be flexible enough to be integrated into existing methods like NES and BanditsTD. For better optimization, we perform a random walk with a probability-driven strategy, which utilizes all queries over the whole progress to make full use of the sensing matrix for a less query budget. Extensive experiments show that our method requires at most 24% fewer queries with a higher attack success rate compared with state-of-the-art approaches. Finally, the attack method is evaluated on the real-world online service, i.e., Google Cloud Vision API, which further demonstrates our practical potentials.
Molecular characterization of a cDNA-derived phosphagen kinase from Biomphalaria glabrata, the intermediate host of Schistosoma mansoni

The present study contains the first description of phosphagen kinase (PK) from the intermediate snail host, Biomphalaria glabrata. We determined the cDNA sequence of PK from the snail B. glabrata, a vector of Schistosoma mansoni, and then cloned the cDNA into a pMAL plasmid and expressed it in Escherichia coli as a fusion protein with maltose-binding protein (MBP). The recombinant protein, consisting of 353 amino acids, has a calculated molecular mass of 39,376 Da and an estimated isoelectric point (pI) of 6.12. B. glabrata PK showed significant activity with the substrate L-arginine, indicating AK, and has the following kinetic constants determined for the forward reaction: Km 0.26 mM, Kd 0.28 mM, kcat 20.3 s 1 and V max 30.4 mmol Pi/min/mg protein. Comparison of kcat/Km values with other AKs indicates that B. glabrata AK has a high catalytic e$ciency (78.12 s 1 mM ), although it exhibited weak synergism during substrate binding (Kd/Km 1.08). In view of the significance of AK in temporal energy bu#ering, this enzyme may be a novel molluscicide target to control B. glabrata and consequently control the transmission of schistosomiasis.
Synchronous Bidirectional Learning for Multilingual Lip Reading

Lip reading has received increasing attention in recent years. This paper focuses on the synergy of multilingual lip reading. There are about as many as 7000 languages in the world, which implies that it is impractical to train separate lip reading models with large-scale data for each language. Although each language has its own linguistic and pronunciation rules, the lip movements of all languages share similar patterns due to the common structures of human organs. Based on this idea, we try to explore the synergized learning of multilingual lip reading in this paper, and further propose a synchronous bidirectional learning (SBL) framework for effective synergy of multilingual lip reading. We firstly introduce phonemes as our modeling units for the multilingual setting here. Phonemes are more closely related with the lip movements than the alphabet letters. At the same time, similar phonemes always lead to similar visual patterns no matter which type the target language is. Then, a novel SBL block is proposed to learn the rules for each language in a fill-in-the-blank way. Specifically, the model has to learn to infer the target unit given its bidirectional context, which could represent the composition rules of phonemes for each language. To make the learning process more targeted at each particular language, an extra task of predicting the language identity is introduced in the learning process. Finally, a thorough comparison on LRW (English) and LRW-1000 (Mandarin) is performed, which shows the promising benefits from the synergized learning of different languages and also reports a new state-of-the-art result on both datasets.
The Strong Screening Rule for SLOPE

Extracting relevant features from data sets where the number of observations ($n$) is much smaller then the number of predictors ($p$) is a major challenge in modern statistics. Sorted L-One Penalized Estimation (SLOPE), a generalization of the lasso, is a promising method within this setting. Current numerical procedures for SLOPE, however, lack the efficiency that respective tools for the lasso enjoy, particularly in the context of estimating a complete regularization path. A key component in the efficiency of the lasso is predictor screening rules: rules that allow predictors to be discarded before estimating the model. This is the first paper to establish such a rule for SLOPE. We develop a screening rule for SLOPE by examining its subdifferential and show that this rule is a generalization of the strong rule for the lasso. Our rule is heuristic, which means that it may discard predictors erroneously. We present conditions under which this may happen and show that such situations are rare and easily safeguarded against by a simple check of the optimality conditions. Our numerical experiments show that the rule performs well in practice, leading to improvements by orders of magnitude for data in the $p \gg n$ domain, as well as incurring no additional computational overhead when $n \gg p$. We also examine the effect of correlation structures in the design matrix on the rule and discuss algorithmic strategies for employing the rule. Finally, we provide an efficient implementation of the rule in our R package SLOPE.
BCI-Controlled Hands-Free Wheelchair Navigation with Obstacle Avoidance

Brain-Computer interfaces (BCI) are widely used in reading brain signals and converting them into real-world motion. However, the signals produced from the BCI are noisy and hard to analyze. This paper looks specifically towards combining the BCI's latest technology with ultrasonic sensors to provide a hands-free wheelchair that can efficiently navigate through crowded environments. This combination provides safety and obstacle avoidance features necessary for the BCI Navigation system to gain more confidence and operate the wheelchair at a relatively higher velocity. A population of six human subjects tested the BCI-controller and obstacle avoidance features. Subjects were able to mentally control the destination of the wheelchair, by moving the target from the starting position to a predefined position, in an average of 287.12 seconds and a standard deviation of 48.63 seconds after 10 minutes of training. The wheelchair successfully avoided all obstacles placed by the subjects during the test.
From uneventful Horizon to firewall in D-dimensional effective theory

Assuming the standard effective-field-theoretic formulation of Hawking radiation, we show explicitly how a generic effective theory predicts a firewall from an initially uneventful horizon for a spherically symmetric, uncharged black hole in [Formula: see text] dimensions for [Formula: see text]. The firewall is created via higher-derivative interactions within the scrambling time after the collapsing matter enters the trapping horizon. This result manifests the trans-Planckian problem of Hawking radiation and demonstrates the incompatibility between Hawking radiation and the uneventful horizon.
Distributed Stochastic Optimization over Time-Varying Noisy Network

This paper is concerned with distributed stochastic multi-agent optimization problem over a class of time-varying network with slowly decreasing communication noise effects. This paper considers the problem in composite optimization setting which is more general in noisy network optimization. It is noteworthy that existing methods for noisy network optimization are Euclidean projection based. We present two related different classes of non-Euclidean methods and investigate their convergence behavior. One is distributed stochastic composite mirror descent type method (DSCMD-N) which provides a more general algorithm framework than former works in this literature. As a counterpart, we also consider a composite dual averaging type method (DSCDA-N) for noisy network optimization. Some main error bounds for DSCMD-N and DSCDA-N are obtained. The trade-off among stepsizes, noise decreasing rates, convergence rates of algorithm is analyzed in detail. To the best of our knowledge, this is the first work to analyze and derive convergence rates of optimization algorithm in noisy network optimization. We show that an optimal rate of $O(1/\sqrt{T})$ in nonsmooth convex optimization can be obtained for proposed methods under appropriate communication noise condition. Moveover, convergence rates in different orders are comprehensively derived in both expectation convergence and high probability convergence sense.
Variation in δ13C and δ15N of kelp is explained by light and productivity

: Stable isotope ratios of autotrophs commonly encompass a wide range and exhibit high variation. It is important to understand how the environments in which autotrophs grow influence stable isotope ratios because variation can limit our ability to make inferences. We sought to understand whether light intensity, water temperature and productivity influenced δ 13 C and δ 15 N of the kelp Ecklonia radiata . δ 13 C and δ 15 N exhibited a wide range: 10.9‰ for δ 13 C and 5.3‰ for δ 15 N. Spatial variability was high but could largely be accounted for by patterns in the intensity of light measured at the sea floor. Temperature typically had poorer explanatory power. Partial regression analyses indicated that light intensity was still a good predictor of patterns in δ 13 C after accounting for depth, providing confidence in inferences about the effects of light. In contrast, patterns in δ 15 N could be explained by depth alone after accounting for light intensity, indicating that other influences that change with depth are also plausible explanations for patterns in δ 15 N. Individual productivity was also positively correlated with δ 13 C and δ 15 N, but the amount of variation accounted for was always low. Sections of tissue taken from different parts of the thalli were positively correlated when all data were pooled, but there was significant spatial and temporal variation within this overall trend. Our results suggest that light intensity is an important influence on variation in δ 13 C. The results support a mechanism in which increased rates of photosynthesis (because of higher light availability) increase the demand for carbon so that a greater proportion of the available carbon is used, which in turn decreases fractionation.
Variance Constrained Autoencoding

Recent state-of-the-art autoencoder based generative models have an encoder-decoder structure and learn a latent representation with a pre-defined distribution that can be sampled from. Implementing the encoder networks of these models in a stochastic manner provides a natural and common approach to avoid overfitting and enforce a smooth decoder function. However, we show that for stochastic encoders, simultaneously attempting to enforce a distribution constraint and minimising an output distortion leads to a reduction in generative and reconstruction quality. In addition, attempting to enforce a latent distribution constraint is not reasonable when performing disentanglement. Hence, we propose the variance-constrained autoencoder (VCAE), which only enforces a variance constraint on the latent distribution. Our experiments show that VCAE improves upon Wasserstein Autoencoder and the Variational Autoencoder in both reconstruction and generative quality on MNIST and CelebA. Moreover, we show that VCAE equipped with a total correlation penalty term performs equivalently to FactorVAE at learning disentangled representations on 3D-Shapes while being a more principled approach.
Hierarchical Deep Convolutional Neural Networks for Multi-category Diagnosis of Gastrointestinal Disorders on Histopathological Images

Deep convolutional neural networks (CNNs) have been successful for a wide range of computer vision tasks including image classification. A specific area of application lies in digital pathology for pattern recognition in tissue-based diagnosis of gastrointestinal (GI) diseases. This domain can utilize CNNs to translate histopathological images into precise diagnostics. This is challenging since these complex biopsies are heterogeneous and require multiple levels of assessment. This is mainly due to structural similarities in different parts of the GI tract and shared features among different gut diseases. Addressing this problem with a flat model which assumes all classes (parts of the gut and their diseases) are equally difficult to distinguish leads to an inadequate assessment of each class. Since hierarchical model restricts classification error to each sub-class, it leads to a more informative model compared to a flat model. In this paper we propose to apply hierarchical classification of biopsy images from different parts of the GI tract and the receptive diseases within each. We embedded a class hierarchy into the plain VGGNet to take advantage of the hierarchical structure of its layers. The proposed model was evaluated using an independent set of image patches from 373 whole slide images. The results indicate that hierarchical model can achieve better results compared to the flat model for multi-category diagnosis of GI disorders using histopathological images.
Predicting the dark matter velocity distribution in galactic structures: tests against hydrodynamic cosmological simulations

Reducing theoretical uncertainties in Galactic dark matter (DM) searches is an important challenge as several experiments are now delving into the parameter space relevant to popular (particle or not) candidates. Since many DM signal predictions rely on the knowledge of the DM velocity distribution—direct searches, capture by stars, p-wave-suppressed or Sommerfeld-enhanced annihilation rate, microlensing of primordial black holes, etc—it is necessary to assess the accuracy of our current theoretical handle. Beyond Maxwellian approximations or ad-hoc extrapolations of fits on cosmological simulations, approaches have been proposed to self-consistently derive the DM phase-space distribution only from the detailed mass content of the Galaxy and some symmetry assumptions (e.g. the Eddington inversion and its anisotropic extensions). Although theoretically sound, these methods are still based on simplifying assumptions and their relevance to real galaxies can be questioned. In this paper, we use zoomed-in cosmological simulations to quantify the associated uncertainties. Assuming isotropy, we predict the speed distribution and its moments from the DM and baryonic content measured in simulations, and compare them with the true ones. {Taking as input galactic mass models fitted on full simulation data, we reach a predictivity down to ∼ 10% for some velocity-related observables, significantly better than some Maxwellian models. } This moderate theoretical error is particularly encouraging at a time when stellar surveys like the Gaia mission should allow us to improve constraints on Galactic mass models.
Multi-Instance Multi-Label Learning for Gene Mutation Prediction in Hepatocellular Carcinoma

Gene mutation prediction in hepatocellular carcinoma (HCC) is of great diagnostic and prognostic value for personalized treatments and precision medicine. In this paper, we tackle this problem with multi-instance multi-label learning to address the difficulties on label correlations, label representations, etc. Furthermore, an effective oversampling strategy is applied for data imbalance. Experimental results have shown the superiority of the proposed approach.
SurfelGAN: Synthesizing Realistic Sensor Data for Autonomous Driving

Autonomous driving system development is critically dependent on the ability to replay complex and diverse traffic scenarios in simulation. In such scenarios, the ability to accurately simulate the vehicle sensors such as cameras, lidar or radar is hugely helpful. However, current sensor simulators leverage gaming engines such as Unreal or Unity, requiring manual creation of environments, objects, and material properties. Such approaches have limited scalability and fail to produce realistic approximations of camera, lidar, and radar data without significant additional work. In this paper, we present a simple yet effective approach to generate realistic scenario sensor data, based only on a limited amount of lidar and camera data collected by an autonomous vehicle. Our approach uses texture-mapped surfels to efficiently reconstruct the scene from an initial vehicle pass or set of passes, preserving rich information about object 3D geometry and appearance, as well as the scene conditions. We then leverage a SurfelGAN network to reconstruct realistic camera images for novel positions and orientations of the self-driving vehicle and moving objects in the scene. We demonstrate our approach on the Waymo Open Dataset and show that it can synthesize realistic camera data for simulated scenarios. We also create a novel dataset that contains cases in which two self-driving vehicles observe the same scene at the same time. We use this dataset to provide additional evaluation and demonstrate the usefulness of our SurfelGAN model.
On Phase Offsets of 802.11 ac Commodity WiFi

We analyze the phase offsets between RF chains of modern IEEE 802.11ac chips. We investigate both the 2.4 and 5 GHz bands on a per OFDM subcarrier level. Results reveal that the phase offset between receive antennas is due to random phase rotations semi-time-invariant with up to four possible values. Moreover, it is frequency-dependent. We propose a simple algorithm, which allows us to correct the phase offset on the fly without any calibration. As proof-of-concept, we implemented angle of arrival (AoA) using MUSIC algorithm. To achieve higher accuracy we stitched the thirteen overlapping 20 MHz channels available in 2.4 GHz band together to effectively have a single 80 MHz channel. Results show very good AoA precision although only two receive antennas were used.
A Solution to The Non-linearity of Electro-Optic Modulation Synthetic Aperture Lidar

Synthetic aperture laser radar has higher resolution, so requires higher modulated bandwidth. Because the data volume of chirp or pulse coding schemes is too large, it brings much pressure to data acquisition and data processing. So, we can use dechirp to reduce the amount of data. However, there is a seriously non-linear problem in phase modulation, which strictly prohibited the dechirp usage. Therefore, in order to solve the above problems, we propose a method based on Electro-Optic Modulators to achieve a large-bandwidth chirp signal. Meanwhile, give a method to resolve the seriously non-linear problems. Firstly, by properly setting the amplitude of the input signal of electro-optic modulators, the finite order signal coefficients are guaranteed to have an absolute advantage. Then, an ideal chirp signal can be obtained by filtering through an optical filter. Finally, the feasibility of the scheme is proved by theory and experiments.
A Hand Motion-guided Articulation and Segmentation Estimation

In this paper, we present a method for simultaneous articulation model estimation and segmentation of an articulated object in RGB-D images using human hand motion. Our method uses the hand motion in the processes of the initial articulation model estimation, ICP-based model parameter optimization, and region selection of the target object. The hand motion gives an initial guess of the articulation model: prismatic or revolute joint. The method estimates the joint parameters by aligning the RGB-D images with the constraint of the hand motion. Finally, the target regions are selected from the cluster regions which move symmetrically along with the articulation model. Our experimental results show the robustness of the proposed method for the various objects.
Literature Triage on Genomic Variation Publications by Knowledge-enhanced Multi-channel CNN

Background: To investigate the correlation between genomic variation and certain diseases or phenotypes, the fundamental task is to screen out the concerning publications from massive literature, which is called literature triage. Some knowledge bases, including UniProtKB/Swiss-Prot and NHGRI-EBI GWAS Catalog are created for collecting concerning publications. These publications are manually curated by experts, which is time-consuming. Moreover, the manual curation of information from literature is not scalable due to the rapidly increasing amount of publications. In order to cut down the cost of literature triage, machine-learning models were adopted to automatically identify biomedical publications. Methods: Comparing to previous studies utilizing machine-learning models for literature triage, we adopt a multi-channel convolutional network to utilize rich textual information and meanwhile bridge the semantic gaps from different corpora. In addition, knowledge embeddings learned from UMLS is also used to provide extra medical knowledge beyond textual features in the process of triage. Results: We demonstrate that our model outperforms the state-of-the-art models over 5 datasets with the help of knowledge embedding and multiple channels. Our model improves the accuracy of biomedical literature triage results. Conclusions: Multiple channels and knowledge embeddings enhance the performance of the CNN model in the task of biomedical literature triage. Keywords: Literature Triage; Knowledge Embedding; Multi-channel Convolutional Network