Miracle-dz/UniFAR-FTUs huggingface dataset:
Chrome Dino Run using Reinforcement Learning

Reinforcement Learning is one of the most advanced set of algorithms known to mankind which can compete in games and perform at par or even better than humans. In this paper we study most popular model free reinforcement learning algorithms along with convolutional neural network to train the agent for playing the game of Chrome Dino Run. We have used two of the popular temporal difference approaches namely Deep Q-Learning, and Expected SARSA and also implemented Double DQN model to train the agent and finally compare the scores with respect to the episodes and convergence of algorithms with respect to timesteps.
A Survey of Deep Learning for Data Caching in Edge Network

The concept of edge caching provision in emerging 5G and beyond mobile networks is a promising method to deal both with the traffic congestion problem in the core network, as well as reducing latency to access popular content. In that respect, end user demand for popular content can be satisfied by proactively caching it at the network edge, i.e., at close proximity to the users. In addition to model-based caching schemes, learning-based edge caching optimizations have recently attracted significant attention, and the aim hereafter is to capture these recent advances for both model-based and data-driven techniques in the area of proactive caching. This paper summarizes the utilization of deep learning for data caching in edge network. We first outline the typical research topics in content caching and formulate a taxonomy based on network hierarchical structure. Then, many key types of deep learning algorithms are presented, ranging from supervised learning to unsupervised learning, as well as reinforcement learning. Furthermore, a comparison of state-of-the-art literature is provided from the aspects of caching topics and deep learning methods. Finally, we discuss research challenges and future directions of applying deep learning for caching.
Putting the semantics into semantic versioning

The long-standing aspiration for software reuse has made astonishing strides in the past few years. Many modern software development ecosystems now come with rich sets of publicly-available components contributed by the community. Downstream developers can leverage these upstream components, boosting their productivity. However, components evolve at their own pace. This imposes obligations on and yields benefits for downstream developers, especially since changes can be breaking, requiring additional downstream work to adapt to. Upgrading too late leaves downstream vulnerable to security issues and missing out on useful improvements; upgrading too early results in excess work. Semantic versioning has been proposed as an elegant mechanism to communicate levels of compatibility, enabling downstream developers to automate dependency upgrades. While it is questionable whether a version number can adequately characterize version compatibility in general, we argue that developers would greatly benefit from tools such as semantic version calculators to help them upgrade safely. The time is now for the research community to develop such tools: large component ecosystems exist and are accessible, component interactions have become observable through automated builds, and recent advances in program analysis make the development of relevant tools feasible. In particular, contracts (both traditional and lightweight) are a promising input to semantic versioning calculators, which can suggest whether an upgrade is likely to be safe.
Deep Query Intent Understanding at Scale

Understanding a user's query intent behind a search is critical for modern search engine success. Accurate query intent prediction allows the search engine to better serve the user's need by rendering results from more relevant categories. This paper aims to provide a comprehensive learning framework for modeling query intent under different stages of a search. We focus on the design for 1) predicting users' intents as they type in queries on-the-fly in typeahead search using character-level models; and 2) accurate word-level intent prediction models for complete queries. Various deep learning components for query text understanding are experimented. Offline evaluation and online A/B test experiments show that the proposed methods are effective in understanding query intent and efficient to scale for online search systems.
Automatic Storage Structure Selection for hybrid Workload

In the use of database systems, the design of the storage engine and data model directly affects the performance of the database when performing queries. Therefore, the users of the database need to select the storage engine and design data model according to the workload encountered. However, in a hybrid workload, the query set of the database is dynamically changing, and the design of its optimal storage structure is also changing. Motivated by this, we propose an automatic storage structure selection system based on learning cost, which is used to dynamically select the optimal storage structure of the database under hybrid workloads. In the system, we introduce a machine learning method to build a cost model for the storage engine, and a column-oriented data layout generation algorithm. Experimental results show that the proposed system can choose the optimal combination of storage engine and data model according to the current workload, which greatly improves the performance of the default storage structure. And the system is designed to be compatible with different storage engines for easy use in practical applications.
A Realistic Example in 2 Dimension that Gradient Descent Takes Exponential Time to Escape Saddle Points

Gradient descent is a popular algorithm in optimization, and its performance in convex settings is mostly well understood. In non-convex settings, it has been shown that gradient descent is able to escape saddle points asymptotically and converge to local minimizers [Lee et. al. 2016]. Recent studies also show a perturbed version of gradient descent is enough to escape saddle points efficiently [Jin et. al. 2015, Ge et. al. 2017]. In this paper we show a negative result: gradient descent may take exponential time to escape saddle points, with non-pathological two dimensional functions. While our focus is theoretical, we also conduct experiments verifying our theoretical result. Through our analysis we demonstrate that stochasticity is essential to escape saddle points efficiently.
How to define quantum mean-field solvable Hamiltonians using Lie algebras

Necessary and sufficient conditions for quantum Hamiltonians to be exactly solvable within mean-field (MF) theories have not been formulated so far. To resolve this problem, first, we define what MF theory is, independently of a Hamiltonian realization in a particular set of operators. Second, using a Lie-algebraic framework we formulate a criterion for a Hamiltonian to be MF solvable. The criterion is applicable for both distinguishable and indistinguishable particle cases. For the electronic Hamiltonians, our approach reveals the existence of MF solvable Hamiltonians of higher fermionic operator powers than quadratic. Some of the MF solvable Hamiltonians require different sets of quasi-particle rotations for different eigenstates, which reflects a more complicated structure of such Hamiltonians.
Audio-Visual Event Localization via Recursive Fusion by Joint Co-Attention

The major challenge in audio-visual event localization task lies in how to fuse information from multiple modalities effectively. Recent works have shown that the attention mechanism is beneficial to the fusion process. In this paper, we propose a novel joint attention mechanism with multi-modal fusion methods for audio-visual event localization. Particularly, we present a concise yet valid architecture that effectively learns representations from multiple modalities in a joint manner. Initially, visual features are combined with auditory features and then turned into joint representations. Next, we make use of the joint representations to attend to visual features and auditory features, respectively. With the help of this joint co-attention, new visual and auditory features are produced, and thus both features can enjoy the mutually improved benefits from each other. It is worth noting that the joint co-attention unit is recursive meaning that it can be performed multiple times for obtaining better joint representations progressively. Extensive experiments on the public AVE dataset have shown that the proposed method achieves significantly better results than the state-of-the-art methods.
GLOD: Gaussian Likelihood Out of Distribution Detector

Discriminative deep neural networks (DNNs) do well at classifying input associated with the classes they have been trained on. However, out-of-distribution (OOD) input poses a great challenge to such models and consequently represents a major risk when these models are used in safety-critical systems. In the last two years, extensive research has been performed in the domain of OOD detection. This research has relied mainly on training the model with OOD data or requiring additional computation for OOD detection. Such methods may not be applicable in many real world use cases. In this paper, we propose GLOD -- Gaussian likelihood out of distribution detector -- an extended DNN classifier capable of efficiently detecting OOD samples with no additional runtime overhead and without auxiliary training data. GLOD uses a layer that models the Gaussian density function of the trained classes. The layer outputs are used to estimate a Log-Likelihood Ratio which is employed to detect OOD samples. We evaluate GLOD's detection performance on SVHN, CIFAR-10 and CIFAR-100.
Image Stylization for Robust Features

Local features that are robust to both viewpoint and appearance changes are crucial for many computer vision tasks. In this work we investigate if photorealistic image stylization improves robustness of local features to not only day-night, but also weather and season variations. We show that image stylization in addition to color augmentation is a powerful method of learning robust features. We evaluate learned features on visual localization benchmarks, outperforming state of the art baseline models despite training without ground-truth 3D correspondences using synthetic homographies only. We use trained feature networks to compete in Long-Term Visual Localization and Map-based Localization for Autonomous Driving challenges achieving competitive scores.
Decision-making at Unsignalized Intersection for Autonomous Vehicles: Left-turn Maneuver with Deep Reinforcement Learning

Decision-making module enables autonomous vehicles to reach appropriate maneuvers in the complex urban environments, especially the intersection situations. This work proposes a deep reinforcement learning (DRL) based left-turn decision-making framework at unsignalized intersection for autonomous vehicles. The objective of the studied automated vehicle is to make an efficient and safe left-turn maneuver at a four-way unsignalized intersection. The exploited DRL methods include deep Q-learning (DQL) and double DQL. Simulation results indicate that the presented decision-making strategy could efficaciously reduce the collision rate and improve transport efficiency. This work also reveals that the constructed left-turn control structure has a great potential to be applied in real-time.
Cool, Luminous, and Highly Variable Stars in the Magellanic Clouds from ASAS-SN: Implications for Thorne–Żytkow Objects and Super-asymptotic Giant Branch Stars

Stars with unusual properties can provide a wealth of information about rare stages of stellar evolution and exotic physics. However, determining the true nature of peculiar stars is often difficult. In this work, we conduct a systematic search for cool and luminous stars in the Magellanic Clouds with extreme variability, motivated by the properties of the unusual Small Magellanic Cloud star and Thorne–Żytkow Object (TŻO) candidate HV 2112. Using light curves from ASAS-SN, we identify 38 stars with surface temperatures T < 4800 K, luminosities (L/L⊙) > 4.3, variability periods >400 days, and variability amplitudes ΔV > 2.5 mag. Eleven of these stars possess the distinctive double-peaked light-curve morphology of HV 2112. We use the pulsation properties and derived occurrence rates for these 12 objects to constrain their nature. From comparisons to stellar populations and models, we find that one star may be a red supergiant with large-amplitude pulsations. For the other 11 stars, we derive current masses of ∼5–10 M⊙, below the theoretical minimum mass of ∼15 M⊙ for TŻOs to be stable, casting doubt on this interpretation. Instead, we find that the temperatures, luminosities, mass-loss rates (MLRs), and periods of these stars are consistent with predictions for super-asymptotic giant branch (s-AGB) stars that have begun carbon burning but have not reached the superwind phase. We infer lifetimes in this phase of ∼(1–7) × 104 yr, also consistent with an s-AGB interpretation. If confirmed, these objects would represent the first identified population of s-AGB stars, illuminating the transition between low- and high-mass stellar evolution.
Rotation-Invariant Gait Identification with Quaternion Convolutional Neural Networks

A desireable property of accelerometric gait-based identification systems is robustness to new device orientations presented by users during testing but unseen during the training phase. However, traditional Convolutional neural networks (CNNs) used in these systems compensate poorly for such transformations. In this paper, we target this problem by introducing Quaternion CNN, a network architecture which is intrinsically layer-wise equivariant and globally invariant under 3D rotations of an array of input vectors. We show empirically that this network indeed significantly outperforms a traditional CNN in a multi-user rotation-invariant gait classification setting . Lastly, we demonstrate how the kernels learned by this QCNN can also be visualized as basis-independent but origin- and chirality-dependent trajectory fragments in the euclidean space, thus yielding a novel mode of feature visualization and extraction.
Noncommutative-geometry wormholes without exotic matter

A fundamental property of Morris-Thorne wormholes is the so-called flare-out condition that automatically results in a violation of the null energy condition (NEC) in classical general relativity. By contrast, in $f(R)$ modified gravity, the material threading the wormhole may actually satisfy the NEC, while, at the same time, the effective stress-energy tensor arising from the modified theory violates the NEC, thereby sustaining the wormhole. It is shown in this paper that noncommutative geometry, an offshoot of string theory, can be viewed as a special case of $f(R)$ gravity and can therefore sustain a wormhole without the use of exotic matter. It also provides a motivation for the choice of $f(R)$ in the modified gravitational theory.
A real-time TDDFT study of femtosecond laser driven monolayer NbSe2

We studied femtosecond laser driven electronic response of monolayer NbSe$_2$ using state-of-the-art computational methods, synthesis and optical characterization. Earlier studies have found distinct signatures of charge density wave (CDW) ordered phases in the ground state ($\sim$ 0 K) of NbSe$_2$ monolayer, in co-existence with superconducting phase. Driving such systems with ultra-short (femtosecond) laser pulse can provide the highly sought knowledge on how to effectively control various exotic phases (e.g. CDW) of monolayer NbSe$_2$ with external control parameters such as pulse intensity. This will not only provide a fundamental understanding of non-equilibrium phase-transitions in NbSe$_2$, but also will open path-forward to revolutionize quantum information technologies such as valleytronics. Inspired by this, we have studied higher harmonic generation (HHG) in monolayer NbSe$_2$ under various intensities of femtosecond laser pulse using real-time time-dependent density functional theory (RT-TDDFT). Our computation predicted distinct signatures in HHG spectra for some higher harmonic modes in the presence of CDW orders in monolayer NbSe$_2$. Excitation energies under strong pulse, and power-law behavior of HHG spectra are also reported.
On Socially Distant Neighbors: Using Binaries to Constrain the Density of Objects in the Galactic Center

Stars often reside in binary configurations. The nuclear star cluster surrounding the supermassive black hole (SMBH) in the Galactic Center (GC) is expected to include a binary population. In this dense environment, a binary frequently encounters and interacts with neighboring stars. These interactions vary from small perturbations to violent collisions. In the former case, weak gravitational interactions unbind a soft binary over the evaporation timescale, which depends on the binary properties as well as the density of surrounding objects and velocity dispersion. Similarly, collisions can also unbind a binary, and the collision rate depends on the density. Thus, the detection of a binary with known properties can constrain the density profile in the GC with implications for the number of compact objects, which are otherwise challenging to detect. We estimate the density necessary to unbind a binary within its lifetime for an orbit of arbitrary eccentricity about the SMBH. We find that the eccentricity has a minimal impact on the density constraint. In this proof of concept, we demonstrate that this procedure can probe the density in the GC using hypothetical young and old binaries as examples. Similarly, a known density profile provides constraints on the binary orbital separation. Our results highlight the need to consider multiple dynamical processes in tandem. In certain cases, often closer to the SMBH, the collision timescale rather than the evaporation timescale gives the more stringent density constraint, while other binaries farther from the SMBH provide unreliable density constraints because they migrate inward due to mass segregation.
A Standardized Radiograph-Agnostic Framework and Platform For Evaluating AI Radiological Systems

Radiology has been essential to accurately diagnosing diseases and assessing responses to treatment. The challenge however lies in the shortage of radiologists globally. As a response to this, a number of Artificial Intelligence solutions are being developed. The challenge Artificial Intelligence radiological solutions however face is the lack of a benchmarking and evaluation standard, and the difficulties of collecting diverse data to truly assess the ability of such systems to generalise and properly handle edge cases. We are proposing a radiograph-agnostic platform and framework that would allow any Artificial Intelligence radiological solution to be assessed on its ability to generalise across diverse geographical location, gender and age groups.
Skein-Theoretic Methods for Unitary Fusion Categories

Unitary fusion categories (UFCs) have gained increased attention due to emerging connections with quantum physics. We discuss how UFCs can be understood as fusion categories equipped with a "positive dagger structure" and apply this in a graphical context. Given a fusion rule $q\otimes q \cong \mathbf{1}\oplus\bigoplus^k_{i=1}x_{i}$ in a UFC $\mathcal{C}$, we extract information using skein-theoretic methods and a rotation operator. For instance, we classify all associated framed link invariants when $k=1,2$ and $\mathcal{C}$ is ribbon. In particular, we also consider the instances where $q$ is antisymmetrically self-dual. Some of this work is reformulated from the perspective of braid representations factoring through the Hecke and Temperley-Lieb algebras. Our main results follow from considering the action of the rotation operator on a "canonical basis". Assuming self-duality of the summands $x_{i}$, some general observations are made e.g. the real-symmetricity of the $F$-matrix $F^{qqq}_q$. We then find explicit formulae for $F^{qqq}_q$ when $k=2$ and $\mathcal{C}$ is ribbon, and see that the spectrum of the rotation operator distinguishes between the Kauffman and Dubrovnik polynomials. Finally, we apply some of our results in a physical setting (where $\mathcal{C}$ is a unitary modular category) and provide some worked examples: quantum entanglement is discussed using the graphical calculus, framed links are interpreted as Wilson loops, and a theorem is given on the duality of topological charges.
Maximum Customers' Satisfaction in One-way Car-sharing: Modeling, Exact and Heuristic Solving

One-way car-sharing systems are transportation systems that allow customers to rent cars at stations scattered around the city, use them for a short journey, and return them at any station. The maximum customers' satisfaction problem concerns the task of assigning the cars, initially located at given stations, to maximize the number of satisfied customers. We consider the problem with two stations where each customer has exactly two demands in opposite directions between both stations, and a customer is satisfied only if both their demands are fulfilled. For solving this problem, we propose mixed-integer programming (MIP) models and matheuristics based on local search. We created a benchmark of instances used to test the exact and heuristic approaches. Additionally, we proposed a preprocessing procedure to reduce the size of the instance. Our MIP models can solve to optimality 85% of the proposed instances with 1000 customers in 10 minutes, with an average gap smaller than 0.1% for all these instances. For larger instances (2500 and 5000 customers), except for some particular cases, they presented an average gap smaller than 0.8%. Also, our local-based matheuristics presented small average gaps which are better than the MIP models in some larger instances.
Crossing the Gap: A Deep Dive into Zero-Shot Sim-to-Real Transfer for Dynamics

Zero-shot sim-to-real transfer of tasks with complex dynamics is a highly challenging and unsolved problem. A number of solutions have been proposed in recent years, but we have found that many works do not present a thorough evaluation in the real world, or underplay the significant engineering effort and task-specific fine tuning that is required to achieve the published results. In this paper, we dive deeper into the sim-to-real transfer challenge, investigate why this is such a difficult problem, and present objective evaluations of a number of transfer methods across a range of real-world tasks. Surprisingly, we found that a method which simply injects random forces into the simulation performs just as well as more complex methods, such as those which randomise the simulator’s dynamics parameters, or adapt a policy online using recurrent network architectures.