Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding Phase Transitions via Mutual Information and MMSE

The ability to understand and solve high-dimensional inference problems is essential for modern data science. This article examines high-dimensional inference problems through the lens of information theory and focuses on the standard linear model as a canonical example that is both rich enough to be practically useful and simple enough to be studied rigorously. In particular, this model can exhibit phase transitions where an arbitrarily small change in the model parameters can induce large changes in the quality of estimates. For this model, the performance of optimal inference can be studied using the replica method from statistical physics but, until recently, it was not known if the resulting formulas were actually correct. In this chapter, we present a tutorial description of the standard linear model and its connection to information theory. We also describe the replica prediction for this model and outline the authors' recent proof that it is exact.
The universal enveloping algebra of $\mathfrak{sl}_2$ and the Racah algebra

Let $\mathbb{F}$ denote a field with ${\rm char\,}\mathbb{F}\not=2$. The Racah algebra $\Re$ is the unital associative $\mathbb{F}$-algebra defined by generators and relations in the following way. The generators are $A$, $B$, $C$, $D$. The relations assert that \begin{equation*} [A,B]=[B,C]=[C,A]=2D \end{equation*} and each of the elements \begin{gather*} \alpha=[A,D]+AC-BA, \qquad \beta=[B,D]+BA-CB, \qquad \gamma=[C,D]+CB-AC \end{gather*} is central in $\Re$. Additionally the element $\delta=A+B+C$ is central in $\Re$. In this paper we explore the relationship between the Racah algebra $\Re$ and the universal enveloping algebra $U(\mathfrak{sl}_2)$. Let $a,b,c$ denote mutually commuting indeterminates. We show that there exists a unique $\mathbb{F}$-algebra homomorphism $\natural:\Re\to\mathbb{F}[a,b,c]\otimes_\mathbb{F} U(\mathfrak{sl}_2)$ that sends \begin{eqnarray*} A &\mapsto& a(a+1)\otimes 1+(b-c-a)\otimes x+(a+b-c+1)\otimes y-1\otimes xy, \\ B &\mapsto& b(b+1)\otimes 1+(c-a-b)\otimes y+(b+c-a+1)\otimes z-1\otimes yz, \\ C &\mapsto& c(c+1)\otimes 1+(a-b-c)\otimes z+(c+a-b+1)\otimes x-1\otimes zx, \\ D &\mapsto& 1\otimes (zyx+zx)+ (c+b(c+a-b))\otimes x +(a+c(a+b-c))\otimes y \\ && \qquad+(b+a(b+c-a))\otimes z +\,(b-c)\otimes xy+(c-a)\otimes yz+(a-b)\otimes zx, \end{eqnarray*} where $x,y,z$ are the equitable generators for $U(\mathfrak{sl}_2)$. We additionally give the images of $\alpha,\beta,\gamma,\delta,$ and certain Casimir elements of $\Re$ under $\natural$. We also show that the map $\natural$ is an injection and thus provides an embedding of $\Re$ into $\mathbb{F}[a,b,c]\otimes U(\mathfrak{sl}_2)$. We use the injection to show that $\Re$ contains no zero divisors.
Stability Analysis of Networked Control Systems: Comparison & Implementation

Networked control systems (NCSs) are receiving more and more attention these days. NCSs are systems where the control loop, in general consisting of a continuous-time plant and a discrete-time controller, is closed over a communication network. The interest for NCSs is motivated by the many benefits they offer such as ease of maintenance and installation, the large flexibility due to the use of distributed elements and the low costs. However, also some disadvantages arise. These disadvantages are caused by the unreliability and shared use of the network, resulting in time-varying delays, packet dropouts and variations in the sampling interval. There is an extensive literature available on the modelling of NCSs, including these network phenomena. However, most of the available work focusses only on one of these phenomena, while ignoring the others. Clearly, it is important to consider the combined presence of dropouts, time-varying sampling intervals and time-varying delays, as in any practical NCS these typically occur simultaneously. In this thesis, an overview is presented of the literature that considers all three network effects using a discrete-time modelling approach. This is done by performing a thorough comparison in terms of modelling accuracy, conservatism and complexity of the stability analysis methods for NCSs. In particular, three methods for modelling dropouts are considered, namely (i) modelling dropouts as prolongation of the sampling interval, (ii) modelling dropouts as prolongation of the delay and (iii) a new dropout modelling approach is proposed that is based on modelling dropouts explicitly using automata. In this work, extensions of the existing methods are given that include the considerations of varying sampling intervals and the usage of parameter-dependent Lyapunov functions to perform the stability analysis. Based on polytopic overapproximations of the resulting discrete-time NCS models, LMI-based stability conditions are provided. Herewith, the extensions of the existing approaches and the newly proposed method are compared in terms of modelling accuracy, conservatism and complexity of the stability analysis. The research presented in this thesis, also includes the design of a Matlab toolbox, in which two modelling approaches, i.e. method (ii) and (iii), and two ways to overapproximate the resulting discrete-time NCS models, i.e. based on the real Jordan form and the Cayley-Hamilton theorem, are implemented. This Matlab toolbox is useful for stability analysis of NCSs and can be used to perform further research.
Accelerated FDPS: Algorithms to use accelerators with FDPS

We describe algorithms implemented in FDPS (Framework for Developing Particle Simulators) to make efficient use of accelerator hardware such as GPGPUs (general-purpose computing on graphics processing units). We have developed FDPS to make it possible for researchers to develop their own high-performance parallel particle-based simulation programs without spending large amounts of time on parallelization and performance tuning. FDPS provides a high-performance implementation of parallel algorithms for particle-based simulations in a “generic” form, so that researchers can define their own particle data structure and interparticle interaction functions. FDPS compiled with user-supplied data types and interaction functions provides all the necessary functions for parallelization, and researchers can thus write their programs as though they are writing simple non-parallel code. It has previously been possible to use accelerators with FDPS by writing an interaction function that uses the accelerator. However, the efficiency was limited by the latency and bandwidth of communication between the CPU and the accelerator, and also by the mismatch between the available degree of parallelism of the interaction function and that of the hardware parallelism. We have modified the interface of the user-provided interaction functions so that accelerators are more efficiently used. We also implemented new techniques which reduce the amount of work on the CPU side and the amount of communication between CPU and accelerators. We have measured the performance of N-body simulations on a system with an NVIDIA Volta GPGPU using FDPS and the achieved performance is around 27% of the theoretical peak limit. We have constructed a detailed performance model, and found that the current implementation can achieve good performance on systems with much smaller memory and communication bandwidth. Thus, our implementation will be applicable to future generations of accelerator system.
Hierarchical Deep Multiagent Reinforcement Learning with Temporal Abstraction

Multiagent reinforcement learning (MARL) is commonly considered to suffer from non-stationary environments and exponentially increasing policy space. It would be even more challenging when rewards are sparse and delayed over long trajectories. In this paper, we study hierarchical deep MARL in cooperative multiagent problems with sparse and delayed reward. With temporal abstraction, we decompose the problem into a hierarchy of different time scales and investigate how agents can learn high-level coordination based on the independent skills learned at the low level. Three hierarchical deep MARL architectures are proposed to learn hierarchical policies under different MARL paradigms. Besides, we propose a new experience replay mechanism to alleviate the issue of the sparse transitions at the high level of abstraction and the non-stationarity of multiagent learning. We empirically demonstrate the effectiveness of our approaches in two domains with extremely sparse feedback: (1) a variety of Multiagent Trash Collection tasks, and (2) a challenging online mobile game, i.e., Fever Basketball Defense.
Neural Image Captioning

In recent years, the biggest advances in major Computer Vision tasks, such as object recognition, handwritten-digit identification, facial recognition, and many others. , have all come through the use of Convolutional Neural Networks (CNNs). Similarly, in the domain of Natural Language Processing, Recurrent Neural Networks (RNNs), and Long Short Term Memory networks (LSTMs) in particular, have been crucial to some of the biggest breakthroughs in performance for tasks such as machine translation, part-of-speech tagging, sentiment analysis, and many others. These individual advances have greatly benefited tasks even at the intersection of NLP and Computer Vision, and inspired by this success, we studied some existing neural image captioning models that have proven to work well. In this work, we study some existing captioning models that provide near state-of-the-art performances, and try to enhance one such model. We also present a simple image captioning model that makes use of a CNN, an LSTM, and the beam search1 algorithm, and study its performance based on various qualitative and quantitative metrics.
Excited nucleon spectrum and structure studies with CLAS and CLAS12

The study of the spectrum and structure of excited nucleon states employing the electroproduction of exclusive reactions is an important avenue for exploring the nature of the non-perturbative strong interaction. The CLAS detector in Hall~B has provided the dominant part of the available world data on most relevant meson electroproduction channels off the nucleon in the resonance region for $Q^2$ up to 5~GeV$^2$. Analyses of CLAS data for the exclusive channels $\pi N$, $\eta N$, and $\pi^+ \pi^- p$ on a proton target have provided the only results available on the $Q^2$ evolution of the electro-excitation amplitudes for the transitions from the initial photon-proton to the final $N^*$ states in the mass range up to $W$=1.8~GeV. These electrocouplings allow for exploration of the internal structure of the produced excited nucleon states. This work has made it clear that consistent results from independent analyses of several exclusive channels with different resonance hadronic decay parameters and non-resonant backgrounds but the same $N^*$ electro-excitation amplitudes, is essential to have confidence in the extracted results. Starting in early 2018, a program to study the spectrum and structure of $N^*$ states in various exclusive electroproduction channels using the new CLAS12 spectrometer commenced. These studies will probe the structure of $N^*$ states in the mass range up to $W$=3~GeV and for $Q^2$ as low as 0.05~GeV$^2$ and as high as 10-12~GeV$^2$, thus providing a means to access $N^*$ structure information spanning a broad range of distance scales. Quasi-real photoproduction studies are also planned to search for additional $N^*$ states, the so-called hybrid baryons, for which the glue serves as an active structural component. In this talk the $N^*$ programs from both CLAS and CLAS12 will be reviewed.
Sketched MinDist

We consider sketch vectors of geometric objects $J$ through the \mindist function \[ v_i(J) = \inf_{p \in J} \|p-q_i\| \] for $q_i \in Q$ from a point set $Q$. Collecting the vector of these sketch values induces a simple, effective, and powerful distance: the Euclidean distance between these sketched vectors. This paper shows how large this set $Q$ needs to be under a variety of shapes and scenarios. For hyperplanes we provide direct connection to the sensitivity sample framework, so relative error can be preserved in $d$ dimensions using $Q = O(d/\varepsilon^2)$. However, for other shapes, we show we need to enforce a minimum distance parameter $\rho$, and a domain size $L$. For $d=2$ the sample size $Q$ then can be $\tilde{O}((L/\rho) \cdot 1/\varepsilon^2)$. For objects (e.g., trajectories) with at most $k$ pieces this can provide stronger \emph{for all} approximations with $\tilde{O}((L/\rho)\cdot k^3 / \varepsilon^2)$ points. Moreover, with similar size bounds and restrictions, such trajectories can be reconstructed exactly using only these sketch vectors.
Nature of a shell of young stars in the outskirts of the Small Magellanic Cloud

Context. Understanding the evolutionary history of the Magellanic Clouds requires an in-depth exploration and characterization of the stellar content in their outer regions, which ultimately are key to tracing the epochs and nature of past interactions. Aims. We present new deep images of a shell-like overdensity of stars in the outskirts of the Small Magellanic Cloud (SMC). The shell, also detected in photographic plates dating back to the fifties, is located at ∼1.9° from the center of the SMC in the north-east direction. Methods. The structure and stellar content of this feature were studied with multiband, optical data from the Survey of the MAgellanic Stellar History (SMASH) carried out with the Dark Energy Camera on the Blanco Telescope at Cerro Tololo Inter-American Observatory. We also investigate the kinematic of the stars in the shell using the Gaia Data Release 2. Results. The shell is composed of a young population with an age ∼150 Myr, with no contribution from an old population. Thus, it is hard to explain its origin as the remnant of a tidally disrupted stellar system. The spatial distribution of the young main-sequence stars shows a rich sub-structure, with a spiral arm-like feature emanating from the main shell and a separated small arc of young stars close to the globular cluster NGC 362. We find that the absolute g-band magnitude of the shell is Mg, shell = −10.78 ± 0.02, with a surface brightness of μg, shell = 25.81 ± 0.01 mag arcsec−2. Conclusion. We have not found any evidence that this feature is of tidal origin or a bright part of a spiral arm-like structure. Instead, we suggest that the shell formed in a recent star formation event, likely triggered by an interaction with the Large Magellanic Cloud and or the Milky Way, ∼150 Myr ago.
Evaluation of the criticality of in vitro neuronal networks: Toward an assessment of computational capacity

Novel computing hardwares are necessary to keep up with today's increasing demand for data storage and processing power. In this research project, we turn to the brain for inspiration to develop novel computing substrates that are self-learning, scalable, energy-efficient, and fault-tolerant. The overarching aim of this work is to develop computational models that are able to reproduce target behaviors observed in in vitro neuronal networks. These models will be ultimately be used to aid in the realization of these behaviors in a more engineerable substrate: an array of nanomagnets. The target behaviors will be identified by analyzing electrophysiological recordings of the neuronal networks. Preliminary analysis has been performed to identify when a network is in a critical state based on the size distribution of network-wide avalanches of activity, and the results of this analysis are reported here. This classification of critical versus non-critical networks is valuable in identifying networks that can be expected to perform well on computational tasks, as criticality is widely considered to be the state in which a system is best suited for computation. This type of analysis is expected to enable the identification of networks that are well-suited for computation and the classification of networks as perturbed or healthy.
CloudCoaster: Transient-aware Bursty DatacenterWorkload Scheduling

Today's clusters often have to divide resources among a diverse set of jobs. These jobs are heterogeneous both in execution time and in their rate of arrival. Execution time heterogeneity has lead to the development of hybrid schedulers that can schedule both short and long jobs to ensure good task placement. However, arrival rate heterogeneity, or burstiness, remains a problem in existing schedulers. These hybrid schedulers manage resources on statically provisioned cluster, which can quickly be overwhelmed by bursts in the number of arriving jobs. In this paper we propose CloudCoaster, a hybrid scheduler that dynamically resizes the cluster by leveraging cheap transient servers. CloudCoaster schedules jobs in an intelligent way that increases job performance while reducing overall resource cost. We evaluate the effectiveness of CloudCoaster through simulations on real-world traces and compare it against a state-of-art hybrid scheduler. CloudCoaster improves the average queueing delay time of short jobs by 4.8X while maintaining long job performance. In addition, CloudCoaster reduces the short partition budget by over 29.5%.
Wireless Network Slicing: Generalized Kelly Mechanism-Based Resource Allocation

Wireless network slicing (i.e., network virtualization) is one of the potential technologies for addressing the issue of rapidly growing demand in mobile data services related to 5G cellular networks. It logically decouples the current cellular networks into two entities: infrastructure providers (InPs) and mobile virtual network operators (MVNOs). The resources of base stations (e.g., resource blocks, transmission power, and antennas), which are owned by the InP, are shared with multiple MVNOs who need resources for their mobile users. Specifically, the physical resources of an InP are abstracted into multiple isolated network slices, which are then allocated to MVNO’s mobile users. In this paper, two-level allocation problem in network slicing is examined while enabling efficient resource utilization, inter-slice isolation (i.e., no interference among slices), and intra-slice isolation (i.e., no interference between users in the same slice). A generalized Kelly mechanism (GKM) is also designed, based on which the upper level of the resource allocation issue (i.e., between the InP and MVNOs) is addressed. The benefit of using such a resource bidding and allocation framework is that the seller (InP) does not need to know the true valuation of the bidders (MVNOs). For solving the lower level of resource allocation issue (i.e., between MVNOs and their mobile users), the optimal resource allocation is derived from each MVNO to its mobile users by using Karush–Kuhn–Tucker (KKT) conditions. Then, bandwidth resources are allocated to the users of MVNOs. Finally, the results of the simulation are presented to verify the theoretical analysis of our proposed two-level resource allocation problem in wireless network slicing.
Hardness of Bichromatic Closest Pair with Jaccard Similarity

Consider collections $\mathcal{A}$ and $\mathcal{B}$ of red and blue sets, respectively. Bichromatic Closest Pair is the problem of finding a pair from $\mathcal{A}\times \mathcal{B}$ that has similarity higher than a given threshold according to some similarity measure. Our focus here is the classic Jaccard similarity $|\textbf{a}\cap \textbf{b}|/|\textbf{a}\cup \textbf{b}|$ for $(\textbf{a},\textbf{b})\in \mathcal{A}\times \mathcal{B}$. We consider the approximate version of the problem where we are given thresholds $j_1>j_2$ and wish to return a pair from $\mathcal{A}\times \mathcal{B}$ that has Jaccard similarity higher than $j_2$ if there exists a pair in $\mathcal{A}\times \mathcal{B}$ with Jaccard similarity at least $j_1$. The classic locality sensitive hashing (LSH) algorithm of Indyk and Motwani (STOC '98), instantiated with the MinHash LSH function of Broder et al., solves this problem in $\tilde O(n^{2-\delta})$ time if $j_1\ge j_2^{1-\delta}$. In particular, for $\delta=\Omega(1)$, the approximation ratio $j_1/j_2=1/j_2^{\delta}$ increases polynomially in $1/j_2$. In this paper we give a corresponding hardness result. Assuming the Orthogonal Vectors Conjecture (OVC), we show that there cannot be a general solution that solves the Bichromatic Closest Pair problem in $O(n^{2-\Omega(1)})$ time for $j_1/j_2=1/j_2^{o(1)}$. Specifically, assuming OVC, we prove that for any $\delta>0$ there exists an $\varepsilon>0$ such that Bichromatic Closest Pair with Jaccard similarity requires time $\Omega(n^{2-\delta})$ for any choice of thresholds $j_2
Integration of imitation learning using GAIL and reinforcement learning using task-achievement rewards via probabilistic graphical model

The integration of reinforcement learning (RL) and imitation learning (IL) is an important problem that has long been studied in the field of intelligent robotics. RL optimizes policies to maximize the cumulative reward, whereas IL attempts to extract general knowledge about the trajectories demonstrated by experts, i.e, demonstrators. Because each has its own drawbacks, many methods combining them and compensating for each set of drawbacks have been explored thus far. However, many of these methods are heuristic and do not have a solid theoretical basis. This paper presents a new theory for integrating RL and IL by extending the probabilistic graphical model (PGM) framework for RL, control as inference. We develop a new PGM for RL with multiple types of rewards, called probabilistic graphical model for Markov decision processes with multiple optimality emissions (pMDP-MO). Furthermore, we demonstrate that the integrated learning method of RL and IL can be formulated as a probabilistic inference of policies on pMDP-MO by considering the discriminator in generative adversarial imitation learning (GAIL) as an additional optimality emission. We adapt the GAIL and task-achievement reward to our proposed framework, achieving significantly better performance than policies trained with baseline methods. GRAPHICAL ABSTRACT
The Review Paper Comparing Sobel And Canny With Genetic Algorithm And Novel Approach

This paper gives an overview of sobel and canny operators and then with the experimented results found that the genetic algorithm that works with improved sobel operator produces better results in comparison to them . but this scope is limited to gray scale images thus the novel approach that maximizes the objective function has been reviewed and concluded better outputs.
The missing dwarf galaxies of the Local Group

We study the Local Group (LG) dwarf galaxy population predicted by the APOSTLE ΛCDM cosmological hydrodynamics simulations. These indicate that: (i) the total mass within 3 Mpc of the Milky Way-Andromeda midpoint (M3Mpc) typically exceeds ∼3 times the sum of the virial masses (M200crit) of the two primaries and (ii) the dwarf galaxy formation efficiency per unit mass is uniform throughout the volume. This suggests that the satellite population within the virial radii of the Milky Way and Andromeda should make up fewer than one third of all LG dwarfs within 3 Mpc. This is consistent with the fraction of observed LG galaxies with stellar mass M* > 107 M⊙ that are satellites (12 out of 42; i.e., 28 per cent). For the APOSTLE galaxy mass-halo mass relation, the total number of such galaxies further suggests a LG mass of M3Mpc ∼ 1013 M⊙. At lower galaxy masses, however, the observed satellite fraction is substantially higher (42 per cent for M* > 105 M⊙). If this is due to incompleteness in the field sample, then ∼50 dwarf galaxies at least as massive as the Draco dwarf spheroidal must be missing from the current LG field dwarf inventory. The incompleteness interpretation is supported by the pronounced flattening of the LG luminosity function below M* ∼ 107 M⊙, and by the scarcity of low-surface brightness LG field galaxies compared to satellites. The simulations indicate that most missing dwarfs should lie near the virial boundaries of the two LG primaries, and predict a trove of nearby dwarfs that await discovery by upcoming wide-field imaging surveys.