Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of helical electromagnetic fields on the axion window

Primordial electromagnetic fields can strongly affect the cosmic evolution of axions, and vice versa. We show that if helical electromagnetic fields are coherently produced in the early universe, their remnants source a field velocity to the coupled axions and enhance the relic abundance of axion dark matter. We discuss the implications for the QCD axion and axion-like particles that are coupled to the SM or hidden gauge groups. For a QCD axion coupled to hidden photons, we find that the conventional window for the axion decay constant 108 GeV≲ f ≲ 1012 GeV can be completely closed due to overproduction of axion dark matter by helical electromagnetic fields as little as α Δ Neff≳ 10-12, where α is the gauge coupling and Δ N_eff is the effective extra relativistic degrees of freedom of the hidden photons.
PWCLO-Net: Deep LiDAR Odometry in 3D Point Clouds Using Hierarchical Embedding Mask Optimization

A novel 3D point cloud learning model for deep LiDAR odometry, named PWCLO-Net, using hierarchical embedding mask optimization is proposed in this paper. In this model, the Pyramid, Warping, and Cost volume (PWC) structure for the LiDAR odometry task is built to refine the estimated pose in a coarse-to-fine approach hierarchically. An attentive cost volume is built to associate two point clouds and obtain embedding motion patterns. Then, a novel trainable embedding mask is proposed to weigh the local motion patterns of all points to regress the overall pose and filter outlier points. The estimated current pose is used to warp the first point cloud to bridge the distance to the second point cloud, and then the cost volume of the residual motion is built. At the same time, the embedding mask is optimized hierarchically from coarse to fine to obtain more accurate filtering information for pose refinement. The trainable pose warp-refinement process is iteratively used to make the pose estimation more robust for outliers. The superior performance and effectiveness of our LiDAR odometry model are demonstrated on KITTI odometry dataset. Our method outperforms all recent learning-based methods and outperforms the geometry-based approach, LOAM with mapping optimization, on most sequences of KITTI odometry dataset. Our source codes will be released on https://github.com/IRMVLab/PWCLONet.
Target Reaching Behaviour for Unfreezing the Robot in a Semi-Static and Crowded Environment

Robot navigation in human semi-static and crowded environments can lead to the freezing problem, where the robot can not move due to the presence of humans standing on its path and no other path is available. Classical approaches of robot navigation do not provide a solution for this problem. In such situations, the robot could interact with the humans in order to clear its path instead of considering them as unanimated obstacles. In this work, we propose a robot behavior for a wheeled humanoid robot that complains with social norms for clearing its path when the robot is frozen due to the presence of humans. The behavior consists of two modules: 1) A detection module, which make use of the Yolo v3 algorithm trained to detect human hands and human arms. 2) A gesture module, which make use of a policy trained in simulation using the Proximal Policy Optimization algorithm. Orchestration of the two models is done using the ROS framework.
Defending Against Contagious Attacks on a Network with Resource Reallocation

In classic network security games, the defender distributes defending resources to the nodes of the network, and the attacker attacks a node, with the objective to maximize the damage caused. Existing models assume that the attack at node u causes damage only at u. However, in many real-world security scenarios, the attack at a node u spreads to the neighbors of u and can cause damage at multiple nodes, e.g., for the outbreak of a virus. In this paper, we consider the network defending problem against contagious attacks. Existing works that study shared resources assume that the resource allocated to a node can be shared or duplicated between neighboring nodes. However, in real world, sharing resource naturally leads to a decrease in defending power of the source node, especially when defending against contagious attacks. To this end, we study the model in which resources allocated to a node can only be transferred to its neighboring nodes, which we refer to as a reallocation process. We show that this more general model is difficult in two aspects: (1) even for a fixed allocation of resources, we show that computing the optimal reallocation is NP-hard; (2) for the case when reallocation is not allowed, we show that computing the optimal allocation (against contagious attack) is also NP-hard. For positive results, we give a mixed integer linear program formulation for the problem and a bi-criteria approximation algorithm. Our experimental results demonstrate that the allocation and reallocation strategies our algorithm computes perform well in terms of minimizing the damage due to contagious attacks.
Context information can increase revenue in ad auctions: Evidence from a policy change

Ad exchanges, i.e., platforms where real-time auctions for ad impressions take place, historically emphasized targeting based on users, and advertisers often did not know which sites their ads would appear on, i.e., the ad context. More recently, some ad exchanges have been encouraging publishers to provide accurate context information to ad buyers, allowing them to adjust their bids for ads at specific domains, subdomains or URLs. This paper explores the empirical effect of a change in context information provided by a major private European ad exchange. Analyzing this as a quasi-experiment using diff-in-diff, we find that average revenue per impression rose when the exchange provided subdomain information to ad buyers. This shows that ad context information is important to ad buyers and that they will act on it. We also find that revenue per impression rises for nearly all sites, which is what is predicted by auction theory when rational buyers with heterogeneous preferences are given more information. The exception to this are sites which had a low number of buyers prior to the policy change; consistent with theory, these sites with thin markets do not show a rise in prices. Our analysis adds evidence that ad exchanges with reputable publishers, particularly smaller volume, high-quality sites, should provide ad buyers with context information, which can be done at almost no cost.
Direction of the microjet produced by the collapse of a cavitation bubble located in a corner of a wall and a free surface

In this paper, we present a simplified theoretical model based on the method of images that predicts the direction of the microjet produced after the implosion of the cavitation bubble created in between a free interface and rigid wall. Our theoretical predictions have been verified by means of a thorough experimental study in which the distances of the pulsed-laser cavitation bubble to the wall and the free surface are varied in a systematic manner. In addition, we extend the predictions to arbitrary values of the corner angle, $\pi/(2n)$ with $n$ a natural number. The present analytical solution will be useful in the design of new strategies aimed at preventing the damage caused by cavitating bubbles over solid substrates
What governs the fluidic behavior of water near single DNA molecules at the micro/nano scale

The fluidic behavior of water at the micro/nano scale is studied by using of single DNA molecules as a model system. Stable curved DNA patterns with spans about one micron were generated by using of water flows, and observed by Atomic Force Microscopy. By rigorously comparing the numerical simulation results with these patterns, it is suggested that the form of the macroscopic hydrodynamic equation still works quantitatively well on the fluid flows at the nanoscale. The molecular effects, however, are still apparent that the effective viscosity of the adjacent water is considerably larger than its bulk value. Our observation is also helpful to understand of the dynamics of biomolecules in solutions from nanoscale to microscale.
Reviewing the Need for Explainable Artificial Intelligence (xAI)

The diffusion of artificial intelligence (AI) applications in organizations and society has fueled research on explaining AI decisions. The explainable AI (xAI) field is rapidly expanding with numerous ways of extracting information and visualizing the output of AI technologies (e.g. deep neural networks). Yet, we have a limited understanding of how xAI research addresses the need for explainable AI. We conduct a systematic review of xAI literature on the topic and identify four thematic debates central to how xAI addresses the black-box problem. Based on this critical analysis of the xAI scholarship we synthesize the findings into a future research agenda to further the xAI body of knowledge.
Message Passing Adaptive Resonance Theory for Online Active Semi-supervised Learning

Active learning is widely used to reduce labeling effort and training time by repeatedly querying only the most beneficial samples from unlabeled data. In real-world problems where data cannot be stored indefinitely due to limited storage or privacy issues, the query selection and the model update should be performed as soon as a new data sample is observed. Various online active learning methods have been studied to deal with these challenges; however, there are difficulties in selecting representative query samples and updating the model efficiently. In this study, we propose Message Passing Adaptive Resonance Theory (MPART) for online active semi-supervised learning. The proposed model learns the distribution and topology of the input data online. It then infers the class of unlabeled data and selects informative and representative samples through message passing between nodes on the topological graph. MPART queries the beneficial samples on-the-fly in stream-based selective sampling scenarios, and continuously improve the classification model using both labeled and unlabeled data. We evaluate our model on visual (MNIST, SVHN, CIFAR-10) and audio (NSynth) datasets with comparable query selection strategies and frequencies, showing that MPART significantly outperforms the competitive models in online active learning environments.
Improving Students Performance in Small-Scale Online Courses - A Machine Learning-Based Intervention

The birth of massive open online courses (MOOCs) has had an undeniable effect on how teaching is being delivered. It seems that traditional in-class teaching is becoming less popular with the young generation – the generation that wants to choose when, where and at what pace they are learning. As such, many universities are moving towards taking their courses, at least partially, online. However, online courses, although very appealing to the younger generation of learners, come at a cost. For example, the dropout rate of such courses are higher than that of more traditional ones, and the reduced in-person interaction with the teachers results in less timely guidance and intervention from the educators. Machine learning (ML)-based approaches have shown phenomenal successes in other domains. The existing stigma that applying ML-based techniques requires a large amount of data seems to be a bottleneck when dealing with small-scale courses with limited amounts of produced data. In this study, we show not only that the data collected from an online learning management system could be well utilized in order to predict students’ overall performance but also that it could be used to propose timely intervention strategies to boost the students’ performance level. The results of this study indicate that effective intervention strategies could be suggested as early as the middle of the course to change the course of students’ progress for the better. We also present an assistive pedagogical tool based on the outcome of this study, to assist in identifying challenging students and in suggesting early intervention strategies.
Perturbative algorithm for rotational decoherence

Recent advances in levitated optomechanics provide new perspectives for the use of rotational degrees of freedom for the development of quantum technologies as well as for testing fundamental physics. As for the translational case, their use, especially in the quantum regime, is limited by environmental noises, whose characterization is fundamental in order to assess, control and minimize their effect, in particular decoherence. Here, we present a general perturbative approach to compute decoherence for a quantum system in a superposition of its rotational degrees of freedom. The specific cases of the dipole-dipole and quadrupole-quadrupole interactions are solved explicitly, and we show that the rotational degrees of freedom decohere on a time scale that is longer than the translational one.
MirrorVLC: Optimal Mirror Placement for Multi-Element VLC Networks

Visible Light Communication (VLC) is a rapidly growing technology which can supplement the current radio-frequency (RF) based wireless communication systems. VLC can play a huge part in solving the ever-increasing problem of spectrum scarcity because of the growing availability of Light Emitting Diodes (LEDs). One of the biggest advantages of VLC over other communication systems is that it can provide illumination and data communication simultaneously without needing any extra deployment. Although it is essential to provide data rate at a blazing speed to all the users nowadays, maintaining a satisfactory level in the distribution of lighting is also important. In this paper, we present a novel approach of using mirrors to enhance the illumination uniformity and throughput of an indoor multi-element VLC system architecture. In this approach, we improve the Signal-to-Interference plus Noise Ratio (SINR) of the system and overall illumination uniformity of the room by redirecting the reflected LED beams on the walls to darker spots with the use of mirrors. We formulate a joint optimization problem focusing on maximization of the SINR while maintaining a reasonable illumination uniformity across the room. We propose a two-stage solution of the optimization problem: design solution and communication solution. In the design optimization, we formulate an equivalent binary linear optimization to achieve the best illumination quality by optimizing the mirror placements and the LEDs’ transmit powers. In the communication problem, however, we aim to improve the throughput of the system using a fair utility metric based on maximizing the minimum user’s data rate. Due to non-convexity of the communication problem, we propose three different heuristic solutions and analyze their performance. We also show that about threefold increase in average illumination and fourfold increase in average throughput can be achieved when the mirror placement is applied which is a significant performance improvement.
Multiband Weighting of X-Ray Polarization Data

An optimal estimate for Stokes parameters is derived for the situation in X-ray astronomy where the instrument has a modulation factor that varies significantly with energy but the signals are very weak or mildly polarized. For such sources, the band of analysis may be broadened in order to obtain a significant polarization measurement. Optimal estimators are provided for the cases of binned and unbinned data and applied to data such as might be obtained for faint or weakly polarized sources observed using the Imaging X-ray Polarimetry Explorer. For a sample situation, the improvement in the minimum detectable polarization is 6%–7% using a count-weighted rms of the modulation factor, when compared to a count-weighted average. Improving the modulation factor, such as when using a neural network approach to Imaging X-ray Polarimetry Explorer event tracks, can provide additional improvement up to 10%–15%. The actual improvement depends on the spectral shape and the details of the instrument response functions.
Investigating two super-resolution methods for downscaling precipitation: ESRGAN and CAR.

In an effort to provide optimal inputs to downstream modeling systems (e.g., a hydrodynamics model that simulates the water circulation of a lake), we hereby strive to enhance resolution of precipitation fields from a weather model by up to 9x. We test two super-resolution models: the enhanced super-resolution generative adversarial networks (ESRGAN) proposed in 2017, and the content adaptive resampler (CAR) proposed in 2020. Both models outperform simple bicubic interpolation, with the ESRGAN exceeding expectations for accuracy. We make several proposals for extending the work to ensure it can be a useful tool for quantifying the impact of climate change on local ecosystems while removing reliance on energy-intensive, high-resolution weather model simulations.
Seiberg-Witten Theory and Topological Recursion

Kontsevich-Soibelman (2017) reformulated Eynard-Orantin topological recursion (2007) in terms of Airy structure which provides some geometrical insights into the relationship between the moduli space of curves and topological recursion. In this work, we investigate the analytical approach to this relationship using the Seiberg-Witten family of curves as the main example. In particular, we are going to show that the formula computing the Hitchin systems' Special Kahler's prepotential from the genus zero part of topological recursion as obtained by Baraglia-Huang (2017) can be generalized for a more general family of curves embedded inside a foliated symplectic surface, including the Seiberg-Witten family. Consequently, we obtain a similar formula relating the Seiberg-Witten prepotential to the genus zero part of topological recursion on a Seiberg-Witten curve. Finally, we investigate the connection between Seiberg-Witten theory and Frobenius manifolds which potentially enable the generalization of the current result to include the higher genus parts of topological recursion in the future.
Curiosity-driven 3D Scene Structure from Single-image Self-supervision

Previous work has demonstrated learning isolated 3D objects (voxel grids, point clouds, meshes, etc.) from 2D-only self-supervision. We here set out to extend this to entire 3D scenes made out of multiple objects, including their location, orientation and type, and the scenes illumination. Once learned, we can map arbitrary 2D images to 3D scene structure. We analyze why analysis-by-synthesis-like losses for supervision of 3D scene structure using differentiable rendering is not practical, as it almost always gets stuck in local minima of visual ambiguities. This can be overcome by a novel form of training: we use an additional network to steer the optimization itself to explore the full gamut of possible solutions i.e. to be curious, and hence, to resolve those ambiguities and find workable minima. The resulting system converts 2D images of different virtual or real images into complete 3D scenes, learned only from 2D images of those scenes.
Hippocampal representations emerge when training recurrent neural networks on a memory dependent maze navigation task.

Can neural networks learn goal-directed behaviour using similar strategies to the brain, by combining the relationships between the current state of the organism and the consequences of future actions? Recent work has shown that recurrent neural networks trained on goal based tasks can develop representations resembling those found in the brain, entorhinal cortex grid cells, for instance. Here we explore the evolution of the dynamics of their internal representations and compare this with experimental data. We observe that once a recurrent network is trained to learn the structure of its environment solely based on sensory prediction, an attractor based landscape forms in the network's representation, which parallels hippocampal place cells in structure and function. Next, we extend the predictive objective to include Q-learning for a reward task, where rewarding actions are dependent on delayed cue modulation. Mirroring experimental findings in hippocampus recordings in rodents performing the same task, this training paradigm causes nonlocal neural activity to sweep forward in space at decision points, anticipating the future path to a rewarded location. Moreover, prevalent choice and cue-selective neurons form in this network, again recapitulating experimental findings. Together, these results indicate that combining predictive, unsupervised learning of the structure of an environment with reinforcement learning can help understand the formation of hippocampus-like representations containing both spatial and task-relevant information.
Towards Intelligent Reconfigurable Wireless Physical Layer (PHY)

Next-generation wireless networks are getting significant attention because they promise 10-factor enhancement in mobile broadband along with the potential to enable new heterogeneous services. Services include massive machine type communications desired for Industrial 4.0 along with ultra-reliable low latency services for remote healthcare and vehicular communications. In this paper, we present the design of an intelligent and reconfigurable physical layer (PHY) to bring these services to reality. First, we design and implement the reconfigurable PHY via a hardware-software co-design approach on system-on-chip consisting of the ARM processor and field-programmable gate array (FPGA). The reconfigurable PHY is then made intelligent by augmenting it with online machine learning (OML) based decision-making algorithm. Such PHY can learn the environment (for example, wireless channel) and dynamically adapt the transceivers' configuration (i.e., modulation scheme, word-length) and select the wireless channel on-the-fly. Since the environment is unknown and changes with time, we make the OML architecture reconfigurable to enable dynamic switch between various OML algorithms on-the-fly. We have demonstrated the functional correctness of the proposed architecture for different environments and word-lengths. The detailed throughput, latency, and complexity analysis validate the feasibility and importance of the proposed intelligent and reconfigurable PHY in next-generation networks.
A question on generalization of partition functions of CY 3-folds in String Theory

This is an expositoray article on the topological string partition function promoting an extension of the partition function of open Gromov-Witten theory of CY 3-folds defined by the trace of vertex operators. We also give a brief survey of their connection to the theory of Hilbert scheme of points on surface. Specifically; we apply infinitely many Cassimir operators twisted to the vertex operator computing the amplitude. The case of finite number of twists has been well discussed in the mathematics and Physics literature.
A Chatbot for Information Security

Advancements in artificial intelligence (AI), speech recognition systems (ASR), and machine learning have enabled the development of intelligent computer programs called chatbots. Many chatbots have been proposed to provide different services in many areas such as customer service, sales and marketing. However, the use of chatbot as advisers in the field of information security is not yet considered. Furthermore, people, especially normal users who have no technical background, are unaware about many of aspects in information security. Therefore, in this paper we proposed a chatbot that acts as an adviser in information security. The proposed adviser uses a knowledge base with json file. Having such chatbot provides many features including raising the awareness in field of information security by offering accurate advice, based on different opinions from information security expertise, for many users on different. Furthermore, this chatbot is currently deployed through Telegram platform, which is one of widely used social network platforms. The deployment of the proposed chatbot over different platforms is considered as the future work.