Miracle-dz/UniFAR-FTUs huggingface dataset:
GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinement

Advances in unsupervised learning of object-representations have culminated in the development of a broad range of methods for unsupervised object segmentation and interpretable object-centric scene generation. These methods, however, are limited to simulated and real-world datasets with limited visual complexity. Moreover, object representations are often inferred using RNNs which do not scale well to large images or iterative refinement which avoids imposing an unnatural ordering on objects in an image but requires the a priori initialisation of a fixed number of object representations. In contrast to established paradigms, this work proposes an embedding-based approach in which embeddings of pixels are clustered in a differentiable fashion using a stochastic stick-breaking process. Similar to iterative refinement, this clustering procedure also leads to randomly ordered object representations, but without the need of initialising a fixed number of clusters a priori. This is used to develop a new model, GENESIS-v2, which can infer a variable number of object representations without using RNNs or iterative refinement. We show that GENESIS-v2 performs strongly in comparison to recent baselines in terms of unsupervised image segmentation and object-centric scene generation on established synthetic datasets as well as more complex real-world datasets.
Frustratingly Easy Edit-based Linguistic Steganography with a Masked Language Model

With advances in neural language models, the focus of linguistic steganography has shifted from edit-based approaches to generation-based ones. While the latter’s payload capacity is impressive, generating genuine-looking texts remains challenging. In this paper, we revisit edit-based linguistic steganography, with the idea that a masked language model offers an off-the-shelf solution. The proposed method eliminates painstaking rule construction and has a high payload capacity for an edit-based model. It is also shown to be more secure against automatic detection than a generation-based method while offering better control of the security/payload capacity trade-off.
Charge dynamics in magnetically disordered Mott insulators

With the aid of both a semi-analytical and a numerically exact method we investigate the charge dynamics in the vicinity of half-filling in the oneand two-dimensional t-J model derived from a Fermi-Hubbard model in the limit of large interaction U and hence small exchange coupling J . The spin degrees of freedom are taken to be disordered. So we consider the limit 0 < J T W where W is the band width. We focus on evaluating the spectral density of a single hole excitation and the charge gap which separates the upper and the lower Hubbard band. One of the key findings is the evidence for the absence of sharp edges of the Hubbard band, instead Gaussian tails appear.
Transformer Transforms Salient Object Detection and Camouflaged Object Detection

—The transformer networks, which originate from machine translation, are particularly good at modeling long-range dependencies within a long sequence. Currently, the transformer networks are making revolutionary progress in various vision tasks ranging from high-level classiﬁcation tasks to low-level dense prediction tasks. In this paper, we conduct research on applying the transformer networks for salient object detection (SOD). Speciﬁcally, we adopt the dense transformer backbone for fully supervised RGB image based SOD, RGB-D image pair based SOD, and weakly supervised SOD within a uniﬁed framework based on the observation that the transformer backbone can provide accurate structure modeling, which makes it powerful in learning from weak labels with less structure information. Further, we ﬁnd that the vision transformer architectures do not offer direct spatial supervision, instead encoding position as a feature. Therefore, we investigate the contributions of two strategies to provide stronger spatial supervision through the transformer layers within our uniﬁed framework, namely deep supervision and difﬁculty-aware learning. We ﬁnd that deep supervision can get gradients back into the higher level features, thus leads to uniform activation within the same semantic object. Difﬁculty-aware learning on the other hand is capable of identifying the hard pixels for effective hard negative mining. We also visualize features of conventional backbone and transformer backbone before and after ﬁne-tuning them for SOD, and ﬁnd that transformer backbone encodes more accurate object structure information and more distinct semantic information within the lower and higher level features respectively. As an extension, we also apply our model to camouﬂaged object detection (COD) and achieve similar observations as the above three SOD related tasks. Extensive experimental results on various SOD and COD tasks illustrate that transformer networks can transform salient object detection and camouﬂaged object detection, leading to new benchmarks for each related task. The source code and experimental results are publicly available via
Posterior Sampling for Image Restoration using Explicit Patch Priors

Almost all existing methods for image restoration are based on optimizing the mean squared error (MSE), even though it is known that the best estimate in terms of MSE may yield a highly atypical image due to the fact that there are many plausible restorations for a given noisy image. In this paper, we show how to combine explicit priors on patches of natural images in order to sample from the posterior probability of a full image given a degraded image. We prove that our algorithm generates correct samples from the distribution $p(x|y) \propto \exp(-E(x|y))$ where $E(x|y)$ is the cost function minimized in previous patch-based approaches that compute a single restoration. Unlike previous approaches that computed a single restoration using MAP or MMSE, our method makes explicit the uncertainty in the restored images and guarantees that all patches in the restored images will be typical given the patch prior. Unlike previous approaches that used implicit priors on fixed-size images, our approach can be used with images of any size. Our experimental results show that posterior sampling using patch priors yields images of high perceptual quality and high PSNR on a range of challenging image restoration problems.
Optimal control problems with sparsity for phase field tumor growth models involving variational inequalities

This paper treats a distributed optimal control problem for a tumor growth model of Cahn– Hilliard type including chemotaxis. The evolution of the tumor fraction is governed by a variational inequality corresponding to a double obstacle nonlinearity occurring in the associated potential. In addition, the control and state variables are nonlinearly coupled and, furthermore, the cost functional contains a nondifferentiable term like the L1–norm in order to include sparsity effects which is of utmost relevance, especially time sparsity, in the context of cancer therapies as applying a control to the system reflects in exposing the patient to an intensive medical treatment. To cope with the difficulties originating from the variational inequality in the state system, we employ the so-called “deep quench approximation” in which the convex part of the double obstacle potential is approximated by logarithmic functions. For such functions, first-order necessary conditions of optimality can be established by invoking recent results. We use these results to derive corresponding optimality conditions also for the double obstacle case, by deducing a variational inequality in terms of the associated adjoint state variables. The resulting variational inequality can be exploited to also obtain sparsity results for the optimal controls.
Learning GMMs with Nearly Optimal Robustness Guarantees

In this work we solve the problem of robustly learning a high-dimensional Gaussian mixture model with k components from ǫ-corrupted samples up to accuracy Õ(ǫ) in total variation distance for any constant k and with mild assumptions on the mixture. This robustness guarantee is optimal up to polylogarithmic factors. At the heart of our algorithm is a new way to relax a system of polynomial equations which corresponds to solving an improper learning problem where we are allowed to output a Gaussian mixture model whose weights are low-degree polynomials. Email: cliu568@mit.edu. This work was supported in part by Ankur Moitra’s NSF CAREER Award CCF-1453261 and NSF Large CCF1565235. Email: moitra@mit.edu. This work was supported in part by a Microsoft Trustworthy AI Grant, NSF CAREER Award CCF-1453261, NSF Large CCF1565235, a David and Lucile Packard Fellowship and an ONR Young Investigator Award.
A P ] 2 0 A pr 2 02 1 THE WEAK ( 1 , 1 ) TYPE OF FOURIER INTEGRAL OPERATORS WITH COMPLEX PHASES

Let X and Y be two smooth manifolds of the same dimension. In this work we prove that the Fourier integral operators with complex non-degenerate phase functions in the class I ρ (X,Y ;C), 1/2 ≤ ρ ≤ 1, and of Segger-Sogge-Stein order are of weak (1,1) type. For ρ = 1, we prove the sharpness of this order for the weak (1,1) inequality for elliptic Fourier integral operators. For real phase functions, for such operators the local boundedness from H to L was proved by Seeger, Sogge and Stein in [14], and the local weak (1,1) inequality for ρ = 1 was proved by Tao [16]. Here we give an alternative simpler proof of Tao’s result in the spirit of the Seeger, Sogge and Stein proof, employing the atomic decomposition of L due to Abu-Shammala and Torchinsky. In fact, our method also allows to extend such local weak (1,1) results to the case of complex phase functions and to different rank conditions for the canonical relation.
Mapping Moral Valence of Tweets Following the Killing of George Floyd

The viral video documenting the killing of George Floyd by Minneapolis police officer Derek Chauvin inspired nationwide protests that brought national attention to widespread racial injustice and biased policing practices towards black communities in the United States. The use of social media by the Black Lives Matter movement was a primary route for activists to promote the cause and organize over 1,400 protests across the country. Recent research argues that moral discussions on social media are a catalyst for social change. This study sought to shed light on the moral dynamics shaping Black Lives Matter Twitter discussions by analyzing over 40,000 Tweets geo-located to Los Angeles. The goal of this study is to (1) develop computational techniques for mapping the structure of moral discourse on Twitter and (2) understand the connections between social media activism and protest.
Refined Littlewood identity for spin Hall–Littlewood symmetric rational functions

Fully inhomogeneous spin Hall-Littlewood symmetric rational functions $F_\lambda$ are multiparameter deformations of the classical Hall-Littlewood symmetric polynomials and can be viewed as partition functions in $\mathfrak{sl}(2)$ higher spin six vertex models. We obtain a refined Littlewood identity expressing a weighted sum of $F_\lambda$'s over all partitions $\lambda$ with even multiplicities as a certain Pfaffian. This Pfaffian can be derived as a partition function of the six vertex model in a triangle with suitably decorated domain wall boundary conditions. The proof is based on the Yang-Baxter equation.
Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset

How does the accuracy of deep neural network models trained to classify clinical images of skin conditions vary across skin color? While recent studies demonstrate computer vision models can serve as a useful decision support tool in healthcare and provide dermatologist-level classification on a number of specific tasks, darker skin is under-represented in the data. Most publicly available data sets do not include Fitzpatrick skin type labels. We annotate 16,577 clinical images sourced from two dermatology atlases with Fitzpatrick skin type labels and open-source these annotations. Based on these labels, we find that there are significantly more images of light skin types than dark skin types in this dataset. We train a deep neural network model to classify 114 skin conditions and find that the model is most accurate on skin types similar to those it was trained on. In addition, we evaluate how an algorithmic approach to identifying skin tones, individual typology angle, compares with Fitzpatrick skin type labels annotated by a team of human labelers.
SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud

We present Self-Ensembling Single-Stage object Detector (SE-SSD) for accurate and efficient 3D object detection in outdoor point clouds. Our key focus is on exploiting both soft and hard targets with our formulated constraints to jointly optimize the model, without introducing extra computation in the inference. Specifically, SE-SSD contains a pair of teacher and student SSDs, in which we design an effective IoU-based matching strategy to filter soft targets from the teacher and formulate a consistency loss to align student predictions with them. Also, to maximize the distilled knowledge for ensembling the teacher, we design a new augmentation scheme to produce shape-aware augmented samples to train the student, aiming to encourage it to infer complete object shapes. Lastly, to better exploit hard targets, we design an ODIoU loss to supervise the student with constraints on the predicted box centers and orientations. Our SE-SSD attains top performance compared with all prior published works. Also, it attains top precisions for car detection in the KITTI benchmark (ranked 1st and 2nd on the BEV and 3D leaderboards1, respectively) with an ultra-high inference speed. The code is available at https://github.com/Vegeta2020/SE-SSD.
Influence of direct deposition of dielectric materials on the optical response of monolayer WS2

The integration of two-dimensional transition metal dichalcogenide crystals (TMDCs) into a dielectric environment is critical for optoelectronic and photonic device applications. Here, we investigate the effects of direct deposition of different dielectric materials (Al2O3, SiO2, SiNx) onto atomically thin (monolayer) TMDC WS2 on its optical response. Atomic layer deposition (ALD), electron beam evaporation (EBE), plasma enhanced chemical vapour deposition (PECVD), and magnetron sputtering methods of material deposition are investigated. The photoluminescence (PL) measurements reveal quenching of the excitonic emission after all deposition processes. The reduction in neutral exciton PL is linked to the increased level of charge doping and associated rise of the trion emission, and/or the localized (bound) exciton emission. Furthermore, Raman spectroscopy allows us to clearly correlate the observed changes of excitonic emission with the increased levels of lattice disorder and defects. Overall, the EBE process results in the lowest level of doping and defect densities and preserves the spectral weight of the exciton emission in the PL, as well as the exciton oscillator strength. Encapsulation with ALD appears to cause chemical changes, which makes it distinct from all other techniques. Sputtering is revealed as the most aggressive deposition method for WS2, fully quenching its optical response. Our results demonstrate and quantify the effects of direct deposition of dielectric materials onto monolayer WS2, which can provide a valuable guidance for the efforts to integrate monolayer TMDCs into functional optoelectronic devices.
Operationalizing a National Digital Library: The Case for a Norwegian Transformer Model

In this work, we show the process of building a large-scale training set from digital and digitized collections at a national library. The resulting Bidirectional Encoder Representations from Transformers (BERT)-based language model for Norwegian outperforms multilingual BERT (mBERT) models in several token and sequence classification tasks for both Norwegian Bokmål and Norwegian Nynorsk. Our model also improves the mBERT performance for other languages present in the corpus such as English, Swedish, and Danish. For languages not included in the corpus, the weights degrade moderately while keeping strong multilingual properties. Therefore, we show that building high-quality models within a memory institution using somewhat noisy optical character recognition (OCR) content is feasible, and we hope to pave the way for other memory institutions to follow.
The Prognosis Value of PSPC1 Expression in Nasopharyngeal Cancer

Background Paraspeckle component 1 (PSPC1) is overexpressed in various cancer and correlated with poor survival in the patients. However, little is known about its expression and role in the progression of nasopharyngeal carcinomas (NPC). The purpose of this study is to examine PSPC1 expression in NPC and explore its role in clinical prognosis of radiation therapy. Methods The association of PSPC1 expression with clinicopathological features of 109 NPC patients was examined using partial correlation analysis. Cancer tissues were obtained prior to clinical treatment. All cases were diagnosed and pathologically confirmed to be poorly differentiated or undifferentiated NPC without distant metastasis. The patients were then treated with radiation and followed-up. Survival analysis was performed. Results Partial correlation analysis revealed that the PSPC1 expression in NPC was correlated with N classification, recurrence, prognosis and radiosensitivity in NPC patients, but not with the gender, age, pathohistological pattern, clinical stage, and T classification. The overexpression of PSPC1 was detected in 64 samples (58.72%). Kaplan–Meier survival analysis revealed that the overall survival (OS) was longer in NPC patients with PSPC1 low expression than that in those with PSPC1 high expression. Moreover, patients with the overexpression of PSPC1 had a low progression-free survival and distant metastasis-free survival rate, compared to those who had a low expression of PSPC1. Although not statistically significant, patients with high expression of PSPC1 had a lower locoregional recurrence-free survival rate than those with low expression, and the curves between the two groups was well separated. Conclusion PSPC1 overexpression was associated with poor prognosis for NPC, which might be a novel useful biomarker to predict the response of NPC to radiation therapy and its clinical outcome.
Motivational Resilience during COVID-19 across At-Risk Undergraduates

Media reports suggest the switch to online courses due to COVID-19 has “demotivated” undergraduates. Our semester-long study of motivation for biology was in progress when COVID-19 was declared a pandemic. [ABSTRACT] Media reports suggest the switch to online courses due to COVID-19 has “demotivated” undergraduates. Our semester-long study of motivation for biology was in progress when COVID-19 was declared a pandemic. We analyze changes in student (N = 182) motivation from before and after. Across variables, subgroups of students changed in adaptive and maladaptive ways; some remained stable. In cross-tabulations, one significant difference was found by sex, and a number of adaptive and maladaptive differences by race and socioeconomic status (SES). Despite obvious burdens on low-SES groups, undergraduate motivation was affected positively and negatively in this sample; only some variables were related to intention to remain in STEM.
IRF1 regulates the progression of colorectal cancer via interferon-induced proteins

Radiation is one of the main methods for the treatment of colorectal cancer (CRC) before or after surgery. However, radiotherapy tolerance of patients with CRC is often a major concern. Interferon regulatory factor 1 (IRF1) is a member of the IRF family and is involved in the development of multiple diseases, including tumors. The present study investigated the role of IRF1 in the development and radiation sensitivity of CRC. Immunohistochemistry was performed to examine the expression levels of IRF1 in tissue samples from patients with CRC, as well as in nude mice. MTT, 5-ethynyl-20-deoxyuridine, colony formation, cell cycle alteration and apoptosis assays were performed in CRC cell lines. Western blotting and immunofluorescence were used to detect the expression levels of a series of proteins. RNA sequencing was applied to identify genes whose expression was upregulated by IRF1 overexpression. Xenograft nude mouse models and hematoxylin and eosin staining were used to validate the present findings in vivo. It was revealed that the expression levels of IRF1 were significantly lower in CRC tissues than in adjacent tissues. IRF1 upregulation inhibited cell proliferation and colony formation, caused G1 cell arrest, promoted cell apoptosis, and enhanced the sensitivity of CRC cells to X-ray irradiation. The role of IRF1 in promoting the radiosensitivity of CRC was further demonstrated in nude mice with CRC xenografts. In addition, RNA sequencing revealed that overexpression of IRF1 in CRC cells significantly increased the expression levels of interferon-induced protein family members interferon α inducible protein 6, interferon induced transmembrane protein 1 and interferon induced protein 35 (fold change >2.0). In summary, the present study demonstrated that the upregulation of IRF1 inhibited the progression and promoted the radiosensitivity of CRC, likely by regulating interferon-induced proteins.
Esophagogastric Cancer After Sleeve Gastrectomy: A Systematic Review of Case Reports

Introduction Sleeve gastrectomy (SG) is the most commonly performed bariatric procedure. It has been shown that bariatric surgery reduces cancer risk. However, the risk of esophagogastric cancer after SG has not been defined yet and the development of cancer in the esophagus and stomach remains a matter of concern. Methods Web of Science, PubMed and Embase databases were searched. Articles that described the diagnosis and management of esophageal or gastric cancer after SG were considered. Results Seventeen esophagogastric cancer patients after SG were included. The age of the patients ranged from 21 to 64 years. Tumors were diagnosed after an interval of 33.9 ±22.8 months from SG (range 4 months–96 months). There were 4 esophageal cancers,4 gastroesophageal cancers and 9 gastric cancers; adenocarcinoma was the most frequent tumor histology (88.2%). The most commonly reported symptoms were food intolerance/dyspepsia (50.0%), vomiting/nausea/regurgitation (35.7%). Upper gastrointestinal endoscopy (UGIE) with biopsy was used for diagnosis in most of the patients. Surgery was performed in 10 patients (58.8%), while 4 patients were treated by endoscopic procedures (23.5%). The mean follow-up length was 12.2 months (range 3 months– 36 months) and the overall disease-free survival rate was 88.9%. Conclusion The development of esophagogastric cancer after SG is still not well defined but it may occur at any time. Preoperative and follow-up UGIE are essential in order to allow for prevention, early diagnosis. Further epidemiologic studies are needed to investigate the post-SG-related risk of esophagogastric cancer development.