Miracle-dz/UniFAR-FTUs huggingface dataset:
Critic Algorithms using Cooperative Networks

An algorithm is proposed for policy evaluation in Markov Decision Processes which gives good empirical results with respect to convergence rates. The algorithm tracks the Projected Bellman Error and is implemented as a true gradient based algorithm. In this respect this algorithm differs from TD($\lambda$) class of algorithms. This algorithm tracks the Projected Bellman Algorithm and is therefore different from the class of residual algorithms. Further the convergence of this algorithm is empirically much faster than GTD2 class of algorithms which aim at tracking the Projected Bellman Error. We implemented proposed algorithm in DQN and DDPG framework and found that our algorithm achieves comparable results in both of these experiments
VISA: An Ambiguous Subtitles Dataset for Visual Scene-aware Machine Translation

Existing multimodal machine translation (MMT) datasets consist of images and video captions or general subtitles which rarely contain linguistic ambiguity, making visual information not so effective to generate appropriate translations. We introduce VISA, a new dataset that consists of 40k Japanese-English parallel sentence pairs and corresponding video clips with the following key features: (1) the parallel sentences are subtitles from movies and TV episodes; (2) the source subtitles are ambiguous, which means they have multiple possible translations with different meanings; (3) we divide the dataset into Polysemy and Omission according to the cause of ambiguity. We show that VISA is challenging for the latest MMT system, and we hope that the dataset can facilitate MMT research.
The Role of Facial Expressions and Emotion in ASL

There is little prior work on quantifying the relationships between facial expressions and emotionality in American Sign Language. In this final report, we provide two methods for studying these relationships through probability and prediction. Using a large corpus of natural signing manually annotated with facial features paired with lexical emotion datasets, we find that there exist many relationships between emotionality and the face, and that a simple classifier can predict what someone is saying in terms of broad emotional categories only by looking at the face.
Learning Pixel Trajectories with Multiscale Contrastive Random Walks

A range of video modeling tasks, from optical flow to multiple object tracking, share the same fundamental challenge: establishing space-time correspondence. Yet, approaches that dominate each space differ. We take a step to-wards bridging this gap by extending the recent contrastive random walk formulation to much denser, pixel-level spacetime graphs. The main contribution is introducing hierarchy into the search problem by computing the transition matrix between two frames in a coarse-to-fine manner, forming a multiscale contrastive random walk when ex-tended in time. This establishes a unified technique for self-supervised learning of optical flow, keypoint tracking, and video object segmentation. Experiments demonstrate that, for each of these tasks, the unified model achieves performance competitive with strong self-supervised approaches specific to that task.11Project page at https://jasonbian97.github. io/flowwalk
A Low-Complexity Location-Based Hybrid Multiple Access and Relay Selection in V2X Multicast Communications

This study investigated relay-assisted mode 1 sidelink (SL) multicast transmission, which encounters interference from mode 2 SL transmission, for use in low-latency vehicle-to-everything communications. To accommodate mode 1–mode 2 SL traffic, we use the hybrid multiple access (MA) approach, which combines orthogonal MA (OMA) and nonorthogonal MA (NOMA) schemes. We introduce a low-complexity location-based hybrid MA algorithm and its associated relay selection that can be used when SL channel state information is unavailable.
Physically Embodied Deep Image Optimisation

Physical sketches are created by learning programs to control a drawing robot. A differentiable rasteriser is used to optimise sets of drawing strokes to match an input image, using deep networks to provide an encoding for which we can compute a loss. The optimised drawing primitives can then be translated into G-code commands which command a robot to draw the image using drawing instruments such as pens and pencils on a physical support medium.
Cosmological Results from the RAISIN Survey: Using Type Ia Supernovae in the Near Infrared as a Novel Path to Measure the Dark Energy Equation of State

Type Ia supernovae (SNe Ia) are more precise standardizable candles when measured in the near-infrared (NIR) than in the optical. With this motivation, from 2012 to 2017 we embarked on the RAISIN program with the Hubble Space Telescope (HST) to obtain rest-frame NIR light curves for a cosmologically distant sample of 37 SNe Ia (0.2 ≲ z ≲ 0.6) discovered by Pan-STARRS and the Dark Energy Survey. By comparing higher-z HST data with 42 SNe Ia at z < 0.1 observed in the NIR by the Carnegie Supernova Project, we construct a Hubble diagram from NIR observations (with only time of maximum light and some selection cuts from optical photometry) to pursue a unique avenue to constrain the dark energy equation-of-state parameter, w. We analyze the dependence of the full set of Hubble residuals on the SN Ia host galaxy mass and find Hubble residual steps of size ∼0.06-0.1 mag with 1. 5σ−2. 5σ significance depending on the method and step location used. Combining our NIR sample with cosmic microwave background constraints, we find 1 + w = −0.17 ± 0.12 (statistical + systematic errors). The largest systematic errors are the redshift-dependent SN selection biases and the properties of the NIR mass step. We also use these data to measure H 0 = 75.9 ± 2.2 km s−1 Mpc−1 from stars with geometric distance calibration in the hosts of eight SNe Ia observed in the NIR versus H 0 = 71.2 ± 3.8 km s−1 Mpc−1 using an inverse distance ladder approach tied to Planck. Using optical data, we find 1 + w = −0.10 ± 0.09, and with optical and NIR data combined, we find 1 + w = −0.06 ± 0.07; these shifts of up to ∼0.11 in w could point to inconsistency in the optical versus NIR SN models. There will be many opportunities to improve this NIR measurement and better understand systematic uncertainties through larger low-z samples, new light-curve models, calibration improvements, and eventually by building high-z samples from the Roman Space Telescope.
Why should we search for vector-like leptons?

There are two strong arguments in favor of vector-like leptons and quarks: Flavor Democracy call for them, and E6 GUT predicts existence of iso-singlet quarks and iso-doublet leptons. Vector-like quarks (VLQ) are extensively searched by ATLAS and CMS collaborations, but this is not the case for vector-like leptons (VLL), while they have actually similar status from phenomenology viewpoint. In this study we argue that vector-like leptons should be included into the new physics search programs of energy-frontier colliders. We consider production of vector-like partners of the first SM family leptons at the HL-LHC, HE-LHC, FCC, ILC, CLIC, Muon Collider, as well as, at ep and {\mu}-p colliders. As for decays of vector-like leptons, we present branching ratios formulas to different channels for the most general case. Since there are many different production and decay channels for charged and neutral vector-like leptons, relevant studies should be done systematically. We invite the High Energy Physics community (both experimenters and phenomenologists) to actively participate in research on this topic.
Learning-From-Disagreement: A Model Comparison and Visual Analytics Framework

With the fast-growing number of classification models being produced every day, numerous model interpretation and comparison solutions have also been introduced. For example, LIME and SHAP can interpret what input features contribute more to a classifier's output predictions. Different numerical metrics (e.g., accuracy) can be used to easily compare two classifiers. However, few works can interpret the contribution of a data feature to a classifier in comparison with its contribution to another classifier. This comparative interpretation can help to disclose the fundamental difference between two classifiers, select classifiers in different feature conditions, and better ensemble two classifiers. To accomplish it, we propose a learning-from-disagreement (LFD) framework to visually compare two classification models. Specifically, LFD identifies data instances with disagreed predictions from two compared classifiers and trains a discriminator to learn from the disagreed instances. As the two classifiers' training features may not be available, we train the discriminator through a set of meta-features proposed based on certain hypotheses of the classifiers to probe their behaviors. Interpreting the trained discriminator with the SHAP values of different meta-features, we provide actionable insights into the compared classifiers. Also, we introduce multiple metrics to profile the importance of meta-features from different perspectives. With these metrics, one can easily identify meta-features with the most complementary behaviors in two classifiers, and use them to better ensemble the classifiers. We focus on binary classification models in the financial services and advertising industry to demonstrate the efficacy of our proposed framework and visualizations.
Cytochrome P450 1B1*2 Gene Polymorphism in a Turkish Population

The polymorphisms in genes encoding xenobiotic metabolising enzymes may be important in the individual susceptibility to various diseases including cancer due to the altered metabolism of carcinogens. Cytochrome P450 1B1 (CYP1B1) is an important enzyme of CYP superfamily and is involved in the formation of reactive estrogen metabolites as well as in the activation of environmental carcinogens. CYP1B1*2 variant of CYP1B1 gene has two linked polymorphisms, m1 and m2, resulting in Arg48Gly and Ala119Ser amino acid substitutions respectively. In this study, CYP1B1*2 m1 (rs10012) and m2 (rs1056827) polymorphisms were determined among 150 healthy individuals of a Turkish population by allele specific polymerase chain reaction (PCR) method and the results were compared with various populations. The frequency of CYP1B1*1/*1 (wild type), CYP1B1*1/*2 (heterozygous) and, CYP1B1*2/*2 (mutant) genotypes were found 47.3%, 44.7% and 8.0% respectively. The genotype frequencies were compared with previous studies conducted on different populations. The results show that the genotype frequencies of the CYP1B1*2 gene polymorphism in a Turkish population are similar to other Caucasian populations.
AstBERT: Enabling Language Model for Code Understanding with Abstract Syntax Tree

Using a pre-trained language model ( i.e. BERT) to apprehend source codes has attracted increasing attention in the natural language processing community. However, there are several challenges when it comes to applying these language models to solve programming language (PL) related problems directly, the signiﬁcant one of which is the lack of domain knowledge issue that substantially deterio-rates the model’s performance. To this end, we pro-pose the AstBERT model, a pre-trained language model aiming to better understand the PL using the abstract syntax tree (AST). Speciﬁcally, we collect a colossal amount of source codes (both java and python) from GitHub and incorporate the contextual code knowledge into our model through the help of code parsers, in which AST information of the source codes can be interpreted and integrated. We verify the performance of the proposed model on code information extraction and code search tasks, respectively. Experiment results show that our AstBERT model achieves state-of-the-art performance on both downstream tasks (with 96.4% for code information extraction task, and 57.12% for code search task).
Design of Optimal Deep Learning-Based Pancreatic Tumor and Nontumor Classification Model Using Computed Tomography Scans

Pancreatic tumor is a lethal kind of tumor and its prediction is really poor in the current scenario. Automated pancreatic tumor classification using computer-aided diagnosis (CAD) model is necessary to track, predict, and classify the existence of pancreatic tumors. Artificial intelligence (AI) can offer extensive diagnostic expertise and accurate interventional image interpretation. With this motivation, this study designs an optimal deep learning based pancreatic tumor and nontumor classification (ODL-PTNTC) model using CT images. The goal of the ODL-PTNTC technique is to detect and classify the existence of pancreatic tumors and nontumor. The proposed ODL-PTNTC technique includes adaptive window filtering (AWF) technique to remove noise existing in it. In addition, sailfish optimizer based Kapur's Thresholding (SFO-KT) technique is employed for image segmentation process. Moreover, feature extraction using Capsule Network (CapsNet) is derived to generate a set of feature vectors. Furthermore, Political Optimizer (PO) with Cascade Forward Neural Network (CFNN) is employed for classification purposes. In order to validate the enhanced performance of the ODL-PTNTC technique, a series of simulations take place and the results are investigated under several aspects. A comprehensive comparative results analysis stated the promising performance of the ODL-PTNTC technique over the recent approaches.
Using Joint Random Partition Models for Flexible Change Point Analysis in Multivariate Processes

Change point analyses are concerned with identifying positions of an ordered stochastic process that undergo abrupt local changes of some underlying distribution. When multiple processes are observed, it is often the case that information regarding the change point positions is shared across the different processes. This work describes a method that takes advantage of this type of information. Since the number and position of change points can be described through a partition with contiguous clusters, our approach develops a joint model for these types of partitions. We describe computational strategies associated with our approach and illustrate improved performance in detecting change points through a small simulation study. We then apply our method to a financial data set of emerging markets in Latin America and highlight interesting insights discovered due to the correlation between change point locations among these economies.
High-resolution observations with ARTEMIS/JLS and the NRH. IV. Imaging spectroscopy of spike-like structures near the front of type-II bursts

Context. Narrowband bursts (spikes) are very small duration and bandwidth bursts which appear on dynamic spectra from microwave to decametric frequencies. They are believed to be manifestations of small-scale energy release through magnetic reconnection. Aims. We study the position of the spike-like structures relative to the front of type-II bursts and their role in the burst emission. Methods. We used high-sensitivity, low-noise dynamic spectra obtained with the acousto-optic analyzer (SAO) of the ARTEMIS-JLS solar radiospectrograph, in conjunction with high-time-resolution images from the Nançay Radioheliograph (NRH) in order to study spike-like bursts near the front of a type-II radio burst recorded at the west limb during the November 3, 2003 extreme solar event. The spike-like emission in the dynamic spectrum was enhanced by means of high-pass-time ﬁltering. Results. We identiﬁed a number of spikes in the NRH images. Due to the lower temporal resolution of the NRH, multiple spikes detected in the dynamic spectrum appeared as single structures in the images. These spikes had an average size of ≈ 200 (cid:48)(cid:48) and their observed brightness temperature was 1.4 to 5 . 6 × 10 9 K, providing a signiﬁcant contribution to the emission of the type-II burst front. At variance with a previous study on the type-IV associated spikes, we found no systematic displacement between the spike emission and the emission between spikes. At 327.0MHz, the type II emission was located about 0.3 R (cid:12) above the pre-existing continuum emission, which, in turn, was located 0.1 R (cid:12) above the western limb. Conclusions. This study, combined with our previous results, indicates that the spike-like chains aligned along the type II burst MHD shock front are not a perturbation of the type II emission, as in the case of type IV spikes, but a manifestation of the type II emission itself. The preponderance of these chains, together with the lack of isolated structures or irregular clusters, points towards some form of small-scale magnetic reconnection, organized along the type-II propagating front.
Adelie: continuous address space layout re-randomization for Linux drivers

While address space layout randomization (ASLR) has been extensively studied for user-space programs, the corresponding OS kernel's KASLR support remains very limited, making the kernel vulnerable to just-in-time (JIT) return-oriented programming (ROP) attacks. Furthermore, commodity OSs such as Linux restrict their KASLR range to 32 bits due to architectural constraints (e.g., x86-64 only supports 32-bit immediate operands for most instructions), which makes them vulnerable to even unsophisticated brute-force ROP attacks due to low entropy. Most in-kernel pointers remain static, exacerbating the problem when pointers are leaked. Adelie, our kernel defense mechanism, overcomes KASLR limitations, increases KASLR entropy, and makes successful ROP attacks on the Linux kernel much harder to achieve. First, Adelie enables the position-independent code (PIC) model so that the kernel and its modules can be placed anywhere in the 64-bit virtual address space, at any distance apart from each other. Second, Adelie implements stack re-randomization and address encryption on modules. Finally, Adelie enables efficient continuous KASLR for modules by using the PIC model to make it (almost) impossible to inject ROP gadgets through these modules regardless of gadget's origin. Since device drivers (typically compiled as modules) are often developed by third parties and are typically less tested than core OS parts, they are also often more vulnerable. By fully re-randomizing device drivers, the last two contributions together prevent most JIT ROP attacks since vulnerable modules are very likely to be a starting point of an attack. Furthermore, some OS instances in virtualized environments are specifically designated to run device drivers, where drivers are the primary target of JIT ROP attacks. Using a GCC plugin that we developed, we automatically modify different kinds of kernel modules. Since the prior art tackles only user-space programs, we solve many challenges unique to the kernel code. Our evaluation shows high efficiency of Adelie's approach: the overhead of the PIC model is completely negligible and re-randomization cost remains reasonable for typical use cases.
The Specialized High-Performance Network on Anton 3

Molecular dynamics (MD) simulation, a computationally intensive method that provides invaluable insights into the behavior of biomolecules, typically requires large-scale parallelization. Implementation of fast parallel MD simulation demands both high bandwidth and low latency for inter-node communication, but in current semiconductor technology, neither of these properties is scaling as quickly as intra-node computational capacity. This disparity in scaling necessitates architectural innovations to maximize the utilization of computational units. For Anton 3, the latest in a family of highly successful special-purpose supercomputers designed for MD simulations, we thus designed and built a completely new specialized network as part of our ASIC. Tightly integrating this network with specialized computation pipelines enables Anton 3 to perform simulations orders of magnitude faster than any general-purpose supercomputer, and to outperform its predecessor, Anton 2 (the state of the art prior to Anton 3), by an order of magnitude. In this paper, we present the three key features of the network that contribute to the high performance of Anton 3. First, through architectural optimizations, the network achieves very low end-to-end inter-node communication latency for fine-grained messages, allowing for better overlap of computation and communication. Second, novel application-specific compression techniques reduce the size of most messages sent between nodes, thereby increasing effective inter-node bandwidth. Lastly, a new hardware synchronization primitive, called a network fence, supports fast fine-grained synchronization tailored to the data flow within a parallel MD application. These application-driven specializations to the network are critical for Anton 3’s MD simulation performance advantage over all other machines.
Addressing Maximization Bias in Reinforcement Learning with Two-Sample Testing

Value-based reinforcement-learning algorithms have shown strong results in games, robotics, and other real-world applications. Overestimation bias is a known threat to those algorithms and can lead to dramatic performance decreases or even complete algorithmic failure. We frame the bias problem statistically and consider it an instance of estimating the maximum expected value (MEV) of a set of random variables. We propose the $T$-Estimator (TE) based on two-sample testing for the mean, that flexibly interpolates between over- and underestimation by adjusting the significance level of the underlying hypothesis tests. A generalization, termed $K$-Estimator (KE), obeys the same bias and variance bounds as the TE while relying on a nearly arbitrary kernel function. We introduce modifications of $Q$-Learning and the Bootstrapped Deep $Q$-Network (BDQN) using the TE and the KE, and prove convergence in the tabular setting. Furthermore, we propose an adaptive variant of the TE-based BDQN that dynamically adjusts the significance level to minimize the absolute estimation bias. All proposed estimators and algorithms are thoroughly tested and validated on diverse tasks and environments, illustrating the bias control and performance potential of the TE and KE.
Belief Revision in Sentential Decision Diagrams

Belief revision is the task of modifying a knowledge base when new information becomes available, while also respecting a number of desirable properties. Classical belief revision schemes have been already specialised to \emph{binary decision diagrams} (BDDs), the classical formalism to compactly represent propositional knowledge. These results also apply to \emph{ordered} BDDs (OBDDs), a special class of BDDs, designed to guarantee canonicity. Yet, those revisions cannot be applied to \emph{sentential decision diagrams} (SDDs), a typically more compact but still canonical class of Boolean circuits, which generalizes OBDDs, while not being a subclass of BDDs. Here we fill this gap by deriving a general revision algorithm for SDDs based on a syntactic characterisation of Dalal revision. A specialised procedure for DNFs is also presented. Preliminary experiments performed with randomly generated knowledge bases show the advantages of directly perform revision within SDD formalism.