Miracle-dz/UniFAR-FTUs huggingface dataset:
Cosmological constraints from DES Y1 cluster abundances and SPT multiwavelength data

We perform a joint analysis of the counts of redMaPPer clusters selected from the Dark Energy Survey (DES) Y1 data and multi-wavelength follow-up data collected within the 2500 deg$^2$ South Pole Telescope (SPT) SZ survey. The SPT follow-up data, calibrating the richness--mass relation of the optically selected redMaPPer catalog, enable the cosmological exploitation of the DES cluster abundance data. To explore possible systematics related to the modeling of projection effects, we consider two calibrations of the observational scatter on richness estimates: a simple Gaussian model which account only for the background contamination (BKG), and a model which further includes contamination and incompleteness due to projection effects (PRJ). Assuming either a $\Lambda$CDM+$\sum m_\nu$ or $w$CDM+$\sum m_\nu$ cosmology, and for both scatter models, we derive cosmological constraints consistent with multiple cosmological probes of the low and high redshift Universe, and in particular with the SPT cluster abundance data. This result demonstrates that the DES Y1 and SPT cluster counts provide consistent cosmological constraints, if the same mass calibration data set is adopted. It thus supports the conclusion of the DES Y1 cluster cosmology analysis which interprets the tension observed with other cosmological probes in terms of systematics affecting the stacked weak lensing analysis of optically--selected low--richness clusters. Finally, we analyse the first combined optically-SZ selected cluster catalogue obtained by including the SPT sample above the maximum redshift probed by the DES Y1 redMaPPer sample. Besides providing a mild improvement of the cosmological constraints, this data combination serves as a stricter test of our scatter models: the PRJ model, providing scaling relations consistent between the two abundance and multi-wavelength follow-up data, is favored over the BKG model.
Matrix Engines for High Performance Computing: A Paragon of Performance or Grasping at Straws?

Matrix engines or units, in different forms and affinities, are becoming a reality in modern processors; CPUs and otherwise. The current and dominant algorithmic approach to Deep Learning merits the commercial investments in these units, and deduced from the No. 1 benchmark in supercomputing, namely High Performance Linpack, one would expect an awakened enthusiasm by the HPC community, too. Hence, our goal is to identify the practical added benefits for HPC and machine learning applications by having access to matrix engines. For this purpose, we perform an in-depth survey of software stacks, proxy applications and benchmarks, and historical batch job records. We provide a cost-benefit analysis of matrix engines, both asymptotically and in conjunction with state-of-the-art processors. While our empirical data will temper the enthusiasm, we also outline opportunities to “misuse” these dense matrix-multiplication engines if they come for free.
Construction of Two Statistical Anomaly Features for Small-Sample APT Attack Traffic Classification

Advanced Persistent Threat (APT) attack, also known as directed threat attack, refers to the continuous and effective attack activities carried out by an organization on a specific object. They are covert, persistent and targeted, which are difficult to capture by traditional intrusion detection system(IDS). The traffic generated by the APT organization, which is the organization that launch the APT attack, has a high similarity, especially in the Command and Control(C2) stage. The addition of features for APT organizations can effectively improve the accuracy of traffic detection for APT attacks. This paper analyzes the DNS and TCP traffic of the APT attack, and constructs two new features, C2Load_fluct (response packet load fluctuation) and Bad_rate (bad packet rate). The analysis showed APT attacks have obvious statistical laws in these two features. This article combines two new features with common features to classify APT attack traffic. Aiming at the problem of data loss and boundary samples, we improve the Adaptive Synthetic(ADASYN) Sampling Approach and propose the PADASYN algorithm to achieve data balance. A traffic classification scheme is designed based on the AdaBoost algorithm. Experiments show that the classification accuracy of APT attack traffic is improved after adding new features to the two datasets so that 10 DNS features, 11 TCP and HTTP/HTTPS features are used to construct a Features set. On the two datasets, F1-score can reach above 0.98 and 0.94 respectively, which proves that the two new features in this paper are effective for APT traffic detection.
Spatio-Temporal Encoding Improves Neuromorphic Tactile Texture Classification

With the increase in interest in the deployment of robots in unstructured environments to work alongside humans, the development of human-like sense of touch for robots becomes important. In this work, we implement a multi-channel neuromorphic tactile system that encodes contact episodes as discrete spike events that mimic the behavior of slow adapting mechanoreceptors. We study the impact of information pooling across artificial mechanoreceptors on classification performance of spatially non-uniform naturalistic textures. We encoded the spatio-temporal activation patterns of mechanoreceptors through gray-level co-occurrence matrix computed from time-varying mean spiking rate-based tactile response volume. We found that this approach greatly improved texture classification in comparison to the use of individual mechanoreceptor response alone. In addition, the performance was also more robust to changes in sliding velocity. The importance of exploiting precise spatial and temporal correlations between sensory channels is evident from the fact that on either removal of precise temporal information or altering of spatial structure of response pattern, a significant performance drop was observed. This study thus demonstrates the superiority of population coding approaches that can exploit the precise spatio-temporal information encoded in activation patterns of mechanoreceptor populations. It, therefore, makes an advance in the direction of development of bio-inspired tactile systems required for realistic touch applications in robotics and prostheses.
Embedding Dynamic Attributed Networks by Modeling the Evolution Processes

Network embedding has recently emerged as a promising technique to embed nodes of a network into low-dimensional vectors. While fairly successful, most existing works focus on the embedding techniques for static networks. But in practice, there are many networks that are evolving over time and hence are dynamic, e.g., the social networks. To address this issue, a high-order spatio-temporal embedding model is developed to track the evolutions of dynamic networks. Specifically, an activeness-aware neighborhood embedding method is first proposed to extract the high-order neighborhood information at each given timestamp. Then, an embedding prediction framework is further developed to capture the temporal correlations, in which the attention mechanism is employed instead of recurrent neural networks (RNNs) for its efficiency in computing and flexibility in modeling. Extensive experiments are conducted on four real-world datasets from three different areas. It is shown that the proposed method outperforms all the baselines by a substantial margin for the tasks of dynamic link prediction and node classification, which demonstrates the effectiveness of the proposed methods on tracking the evolutions of dynamic networks.
GAN Mask R-CNN: Instance semantic segmentation benefits from generativeadversarial networks

In designing instance segmentation ConvNets that reconstruct masks, segmentation is often taken as its literal definition -- assigning label to every pixel -- for defining the loss functions. That is, using losses that compute the difference between pixels in the predicted (reconstructed) mask and the ground truth mask -- a template matching mechanism. However, any such instance segmentation ConvNet is a generator, so we can lay the problem of predicting masks as a GANs game framework: We can think the ground truth mask is drawn from the true distribution, and a ConvNet like Mask R-CNN is an implicit model that infers the true distribution. Then, designing a discriminator in front of this generator will close the loop of GANs concept and more importantly obtains a loss that is trained not hand-designed. We show this design outperforms the baseline when trying on, without extra settings, several different domains: cellphone recycling, autonomous driving, large-scale object detection, and medical glands. Further, we observe in general GANs yield masks that account for better boundaries, clutter, and small details.
Interior Point Solving for LP-based prediction+optimisation

Solving optimization problems is the key to decision making in many real-life analytics applications. However, the coefficients of the optimization problems are often uncertain and dependent on external factors, such as future demand or energy or stock prices. Machine learning (ML) models, especially neural networks, are increasingly being used to estimate these coefficients in a data-driven way. Hence, end-to-end predict-and-optimize approaches, which consider how effective the predicted values are to solve the optimization problem, have received increasing attention. In case of integer linear programming problems, a popular approach to overcome their non-differentiabilty is to add a quadratic penalty term to the continuous relaxation, such that results from differentiating over quadratic programs can be used. Instead we investigate the use of the more principled logarithmic barrier term, as widely used in interior point solvers for linear programming. Specifically, instead of differentiating the KKT conditions, we consider the homogeneous self-dual formulation of the LP and we show the relation between the interior point step direction and corresponding gradients needed for learning. Finally our empirical experiments demonstrate our approach performs as good as if not better than the state-of-the-art QPTL (Quadratic Programming task loss) formulation of Wilder et al. and SPO approach of Elmachtoub and Grigas.
Learning to Infer Unseen Attribute-Object Compositions

The composition recognition of unseen attribute-object is critical to make machines learn to decompose and compose complex concepts like people. Most of the existing methods are limited to the composition recognition of single-attribute-object, and can hardly distinguish the compositions with similar appearances. In this paper, a graph-based model is proposed that can flexibly recognize both single- and multi-attribute-object compositions. The model maps the visual features of images and the attribute-object category labels represented by word embedding vectors into a latent space. Then, according to the constraints of the attribute-object semantic association, distances are calculated between visual features and the corresponding label semantic features in the latent space. During the inference, the composition that is closest to the given image feature among all compositions is used as the reasoning result. In addition, we build a large-scale Multi-Attribute Dataset (MAD) with 116,099 images and 8,030 composition categories. Experiments on MAD and two other single-attribute-object benchmark datasets demonstrate the effectiveness of our approach.
Exploring Spirituality and Technology Receptivity Among a Sample of Older Blacks to Inform a Tailored Chronic Disease Self-Management mHealth Intervention

Introduction Having multiple chronic conditions (MCC) is the most common health condition in older US adults of which Blacks are disproportionally affected. The management of each condition presents many challenges. Blacks in the US frequently cite spirituality as facilitator to well-being. More information is needed to understand the many aspects of spirituality that older Black patients use to manage MCCs. Methods In the current study, focus groups were conducted with 30 black men and women with MCCs to examine how spirituality can be incorporated into a mobile health intervention designed to increase chronic disease self-management (CDSM) skills by improving health literacy. Groups discussed spiritual practices used to facilitate CDSM and their perceptions about mobile technology use. Results Inductive thematic analysis suggested that a chronic disease wellness plan that acknowledges the relationship between spirituality and health was preferred by most participants. Additionally, the desire for mobile health (mHealth) among this group points to an opportunity for intervention. Discussion Creating culturally appropriate educational messages about CDSM that incorporate spiritual practices may be a useful method for building sustainable CDSM skills. Next steps include the development of a mHealth intervention prototype based on the results and pre-testing it prior to deployment.
Reading between the Lines: Exploring Infilling in Visual Narratives

Generating long form narratives such as stories and procedures from multiple modalities has been a long standing dream for artificial intelligence. In this regard, there is often crucial subtext that is derived from the surrounding contexts. The general seq2seq training methods render the models shorthanded while attempting to bridge the gap between these neighbouring contexts. In this paper, we tackle this problem by using infilling techniques involving prediction of missing steps in a narrative while generating textual descriptions from a sequence of images. We also present a new large scale visual procedure telling (ViPT) dataset with a total of 46,200 procedures and around 340k pairwise images and textual descriptions that is rich in such contextual dependencies. Generating steps using infilling technique demonstrates the effectiveness in visual procedures with more coherent texts. We conclusively show a METEOR score of 27.51 on procedures which is higher than the state-of-the-art on visual storytelling. We also demonstrate the effects of interposing new text with missing images during inference. The code and the dataset will be publicly available at https://visual-narratives.github. io/Visual-Narratives/.
Hamilton-Jacobi Deep Q-Learning for Deterministic Continuous-Time Systems with Lipschitz Continuous Controls

In this paper, we propose Q-learning algorithms for continuous-time deterministic optimal control problems with Lipschitz continuous controls. Our method is based on a new class of Hamilton-Jacobi-Bellman (HJB) equations derived from applying the dynamic programming principle to continuous-time Q-functions. A novel semi-discrete version of the HJB equation is proposed to design a Q-learning algorithm that uses data collected in discrete time without discretizing or approximating the system dynamics. We identify the condition under which the Q-function estimated by this algorithm converges to the optimal Q-function. For practical implementation, we propose the Hamilton-Jacobi DQN, which extends the idea of deep Q-networks (DQN) to our continuous control setting. This approach does not require actor networks or numerical solutions to optimization problems for greedy actions since the HJB equation provides a simple characterization of optimal controls via ordinary differential equations. We empirically demonstrate the performance of our method through benchmark tasks and high-dimensional linear-quadratic problems.
Subgrouping University Students Based on Substance Use Pattern: A Latent Class Analysis

Purpose High-risk behaviors are the main causes of death and disability among youth and adults. Entering university might cause students to go through their first-hand experience of using substances. Aim This study aimed to detect the subgroups of students based on substance use and assess the effects of religiosity and parental support as well as other related factors on the membership of students in each latent class. Methods Using a multistage sampling method, this cross-sectional study was conducted in 2016 in Hormozgan University of Medical Sciences (n=524). All students completed a self-report questionnaire. This questionnaire contained questions about substance use, religious beliefs and familial support. The questions of substance use were prepared using the World Health Organization Alcohol, Smoking and Substance Involvement Screening Test (WHO ASSIST). To analyze the data, PROC LCA statistical method was run in SAS9.2. Results Three latent classes were identified: 1) nonuser (87.5%), 2) tobacco and illicit drug user (8.7%) and 3) polydrug user (3.8%). Having extramarital sex in the last month (OR=28.29, 95% CI; 8.45–94.76), living alone (OR=4.29, 95% CI; 1.01–18.35) and having a higher score of familial support (OR=0.94, 95% CI; 0.89–0.98) were associated with the polydrug user class. Hookah smoking had the highest (11.1%) and non-medical methylphenidate use had the lowest (2.3%) prevalence among the participants of the study. Conclusion This study revealed that 12.5% of the students were either tobacco and illicit drug users or polydrug users. Thus, focusing on the religiosity and familial support may help design some preventive programs for this stratum of young adults.
Hypoxic Non-Small-Cell Lung Cancer Cell-Secreted Exosomal microRNA-582-3p Drives Cancer Cell Malignant Phenotypes by Targeting Secreted Frizzled-Related Protein 1

Background Hypoxic environment and exosomes (exos)-mediated intercellular communication are crucial for cancer invasion and metastasis, but the mechanisms are not yet fully understood. In this study, we investigated the regulatory effect of hypoxic tumor cell-secreted exosomal miR-582-3p on non-small-cell lung cancer (NSCLC) cell malignant phenotypes. Methods The concentration and diameters of exos were evaluated by nanosight particle tracking analysis. microRNA-582-3p (miR-582-3p) expression was detected by quantitative real-time PCR. The fluorescent dye PKH26 was used to label exos. The direct interaction between miR-582-3p and secreted frizzled-related protein 1 (SFRP1) was determined by dual-luciferase activity assay. NSCLC cell proliferation, migration, and invasion abilities were assessed by cell count kit-8 assay, wound healing assay, and transwell migration and invasion assay. Western blot analysis was performed to detect the protein expression level. Results Hypoxic NSCLC cell-derived exos promoted the proliferation, migration, and invasion of normoxic NSCLC cells. miR-582-3p expression was upregulated in hypoxic NSCLC cells and hypoxic NSCLC cell-secreted exos. Hypoxic NSCLC cell-derived exos transmitted miR-582-3p to normoxic NSCLC cells. Hypoxic NSCLC cell-secreted exosomal miR-582-3p promoted the proliferation, migration, and invasion of normoxic NSCLC cells. miR-582-3p inhibited the expression of SFRP1 protein by binding to its 3ʹ-UTR. In addition, enforced expression of SFRP1 restrained malignant phenotypes of normoxic NSCLC cells, which was abrogated by hypoxic NSCLC cell-secreted exosomal miR-582-3p. Conclusion Hypoxic NSCLC cell-secreted exosomal miR-582-3p drives cancer cell malignant phenotypes by targeting SFRP1, which provides a better understanding of cancer metastasis and may facilitate the development of therapeutics against human NSCLC.
COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning

Reinforcement learning has been applied to a wide variety of robotics problems, but most of such applications involve collecting data from scratch for each new task. Since the amount of robot data we can collect for any single task is limited by time and cost considerations, the learned behavior is typically narrow: the policy can only execute the task in a handful of scenarios that it was trained on. What if there was a way to incorporate a large amount of prior data, either from previously solved tasks or from unsupervised or undirected environment interaction, to extend and generalize learned behaviors? While most prior work on extending robotic skills using pre-collected data focuses on building explicit hierarchies or skill decompositions, we show in this paper that we can reuse prior data to extend new skills simply through dynamic programming. We show that even when the prior data does not actually succeed at solving the new task, it can still be utilized for learning a better policy, by providing the agent with a broader understanding of the mechanics of its environment. We demonstrate the effectiveness of our approach by chaining together several behaviors seen in prior datasets for solving a new task, with our hardest experimental setting involving composing four robotic skills in a row: picking, placing, drawer opening, and grasping, where a +1/0 sparse reward is provided only on task completion. We train our policies in an end-to-end fashion, mapping high-dimensional image observations to low-level robot control commands, and present results in both simulated and real world domains. Additional materials and source code can be found on our project website: this https URL
Discovering and Interpreting Conceptual Biases in Online Communities

Language carries implicit human biases, functioning both as a reflection and a perpetuation of stereotypes that people carry with them. Recently, ML-based NLP methods such as word embeddings have been shown to learn such language biases with striking accuracy. This capability of word embeddings has been successfully exploited as a tool to quantify and study human biases. However, previous studies only consider a predefined set of conceptual biases to attest (e.g., whether gender is more or less associated with particular jobs), or just discover biased words without helping to understand their meaning at the conceptual level. As such, these approaches are either unable to find conceptual biases that have not been defined in advance, or the biases they find are difficult to interpret and study. This makes existing approaches unsuitable to discover and interpret biases in online communities, as such communities may carry different biases than those in mainstream culture. This paper proposes a general, data-driven approach to automatically discover and help interpret conceptual biases encoded in word embeddings. We apply this approach to study the conceptual biases present in the language used in online communities and experimentally show the validity and stability of our method.
Nonlinear cosmological structure with ultralight bosons via modified gravity

Ultra-light bosons as dark matter has become a model of major interest in Cosmology, due to the possible imprint of a distinct signature in the cosmic structure both at the linear and non-linear scales. In this work we show that the equations of motion for density perturbations for this kind of models can be written in terms of a modified gravitational potential. Taking advantage of this parallelism, we use the MG-PICOLA code originally developed for modified gravity models to evolve the density field of axion models with and without self-interaction. Our results indicate that the quantum potential adds extra suppression of power at the non-linear level, and it is even capable of smoothing any bumpy features initially present in the mass power spectrum.