Miracle-dz/UniFAR-FTUs huggingface dataset:
DaeMon: Architectural Support for Efficient Data Movement in Disaggregated Systems

Resource disaggregation offers a cost effective solution to resource scaling, utilization, and failure-handling in data centers by physically separating hardware devices in a server. Servers are architected as pools of processor, memory, and storage devices, organized as independent failure-isolated components interconnected by a high-bandwidth network. A critical challenge, however, is the high performance penalty of accessing data from a remote memory module over the network. Addressing this challenge is difficult as disaggregated systems have high runtime variability in network latencies/bandwidth, and page migration can significantly delay critical path cache line accesses in other pages. This paper conducts a characterization analysis on different data movement strategies in fully disaggregated systems, evaluates their performance overheads in a variety of workloads, and introduces DaeMon, the first software-transparent mechanism to significantly alleviate data movement overheads in fully disaggregated systems. First, to enable scalability to multiple hardware components in the system, we enhance each compute and memory unit with specialized engines that transparently handle data migrations. Second, to achieve high performance and provide robustness across various network, architecture and application characteristics, we implement a synergistic approach of bandwidth partitioning, link compression, decoupled data movement of multiple granularities, and adaptive granularity selection in data movements. We evaluate DaeMon in a wide variety of workloads at different network and architecture configurations using a state-of-the-art accurate simulator. DaeMon improves system performance and data access costs by 2.39$\times$ and 3.06$\times$, respectively, over the widely-adopted approach of moving data at page granularity.
Skew Class-balanced Re-weighting for Unbiased Scene Graph Generation

An unbiased scene graph generation (SGG) algorithm referred to as Skew Class-Balanced Re-Weighting (SCR) is proposed for considering the unbiased predicate prediction caused by the long-tailed distribution. The prior works focus mainly on alleviating the deteriorating performances of the minority predicate predictions, showing drastic dropping recall scores, i.e., losing the majority predicate performances. It has not yet correctly analyzed the trade-off between majority and minority predicate performances in the limited SGG datasets. In this paper, to alleviate the issue, the Skew Class-Balanced Re-Weighting (SCR) loss function is considered for the unbiased SGG models. Leveraged by the skewness of biased predicate predictions, the SCR estimates the target predicate weight coefficient and then re-weights more to the biased predicates for better trading-off between the majority predicates and the minority ones. Extensive experiments conducted on the standard Visual Genome dataset and Open Image V4 and V6 show the performances and generality of the SCR with the traditional SGG models.
LeaFTL: A Learning-Based Flash Translation Layer for Solid-State Drives

In modern solid-state drives (SSDs), the indexing of flash pages is a critical component in their storage controllers. It not only affects the data access performance, but also determines the efficiency of the precious in-device DRAM resource. A variety of address mapping schemes and optimizations have been proposed. However, most of them were developed with human-driven heuristics. In this paper, we present a learning-based flash translation layer (FTL), named LeaFTL, which learns the address mapping to tolerate dynamic data access patterns via linear regression at runtime. By grouping a large set of mapping entries into a learned segment, it significantly reduces the memory footprint of the address mapping table, which further benefits the data caching in SSD controllers. LeaFTL also employs various optimization techniques, including out-of-band metadata verification to tolerate mispredictions, optimized flash allocation, and dynamic compaction of learned index segments. We implement LeaFTL with both a validated SSD simulator and a real open-channel SSD board. Our evaluation with various storage workloads demonstrates that LeaFTL saves the memory consumption of the mapping table by 2.9× and improves the storage performance by 1.4× on average, in comparison with state-of-the-art FTL schemes.
Deep Learning Based User ’ s Demand Predictor

Edge network caching and network virtualization are the most promising technologies among the key technologies to fulfill the requirements of next-generation cellular networks. Caching popular contents at edge nodes (base stations) becomes a promising solution to improve the user’s quality of services, as well as to reduce the network traffic for each Mobile Virtual Network Operator (MVNO). However, a decision to buy cache space to efficiently store popular contents is the challenging issue for each MVNO. So, in this paper, we proposed deep learning based user’s demand predictor to support cache space buying decision of each MVNO. We develop, train and test the prediction models using Keras libraries.
ReSQueing Parallel and Private Stochastic Convex Optimization

We introduce a new tool for stochastic convex optimization (SCO): a Reweighted Stochastic Query (ReSQue) estimator for the gradient of a function convolved with a (Gaussian) probability density. Combining ReSQue with recent advances in ball oracle acceleration [CJJ+20], [ACJ+21], we develop algorithms achieving state-of-the-art complexities for SCO in parallel and private settings. For a SCO objective constrained to the unit ball in $\mathbb{R}^{d}$, we obtain the following results (up to polylogarithmic factors). 1)We give a parallel algorithm obtaining optimization error $\epsilon_{\text {opt} }$ with $d^{1 / 3} \epsilon_{\text {opt} }^{-2 / 3}$ gradient oracle query depth and $d^{1 / 3} \epsilon_{\text {opt} }^{-2 / 3}+\epsilon_{\text {opt} }^{-2}$ gradient queries in total, assuming access to a bounded-variance stochastic gradient estimator. For $\epsilon_{\text {opt} } \in\left[d^{-1}, d^{-1 / 4}\right]$, our algorithm matches the state-of-the-art oracle depth of [BJL+19] while maintaining the optimal total work of stochastic gradient descent.2)Given n samples of Lipschitz loss functions, prior works [BFTT19], [BFGT20], [AFKT21], [KLL21] established that if $n \gt rsim d \epsilon_{\mathrm{dp}}^{-2},\left(\epsilon_{\mathrm{dp}}, \delta\right)$-differential privacy is attained at no asymptotic cost to the SCO utility. However, these prior works all required a superlinear number of gradient queries. We close this gap for sufficiently large $n \gt rsim d^{2} \epsilon_{{\bf d p}}^{-3}$, by using ReSQue to design an algorithm with near-linear gradient query complexity in this regime.
Numerical simulation for aerodynamic characteristics of two circular cylinders in staggered arrangement by a third-order upwind finite element method

We present numerical results for flow patterns around two circular cylinders in a staggered arrangement, and fluid forces of the cylinders which are placed in a uniform flow. The numerical results for flow around two circular cylinders are obtained by two- and three-dimensional computations. The distances of the in-line and transverse directions between the centers of two circular cylinders are taken into consideration as the parameters of the computation. Stabilization of numerical solutions is achieved by using a third-order upwind finite element method. In the case of tandem arrangement of the circular cylinders, it is well known from experimental data that, when two circular cylinders are placed at a distance of approximately diameters between the centers of cylinders, the fluid forces of both cylinders vary suddenly. Therefore, in this paper, the circular cylinders are put at two kinds of distances of 3- and 4-diameters to the in-line direction and are also put at distances of diameters to the transverse direction. The obtained results for two circular cylinders in the staggered arrangement are qualitatively compared with experimental data.
RiskProp: Account Risk Rating on Ethereum via De-anonymous Score and Network Propagation

As one of the most popular blockchain platforms supporting smart contracts, Ethereum has caught the interest of both investors and criminals. Differently from traditional financial scenarios, executing Know Your Customer verification on Ethereum is rather difficult due to the pseudonymous nature of the blockchain. Fortunately, as the transaction records stored in the Ethereum blockchain are publicly accessible, we can understand the behavior of accounts or detect illicit activities via transaction mining. Existing risk control techniques have primarily been developed from the perspectives of de-anonymizing address clustering and illicit account classification. However, these techniques cannot be used to ascertain the potential risks for all accounts and are limited by specific heuristic strate-gies or insufficient label information. These constraints motivate us to seek an effective rating method for quantifying the spread of risk in a transaction network. To the best of our knowledge, we are the first to address the problem of account risk rating on Ethereum by proposing a novel model called RiskProp, which includes a de-anonymous score to measure transaction anonymity and a network propagation mechanism to formulate the relationships between accounts and transactions. We demonstrate the effectiveness of RiskProp in overcoming the limitations of existing models by conducting experiments on real-world datasets from Ethereum. Through case studies on the detected high-risk accounts, we demonstrate that the risk assessment by RiskProp can be used to provide warnings for investors and protect them from possible financial losses, and the superior performance of risk score-based account classification experiments further verifies the effectiveness of our rating method.
Rate Region of MIMO RIS-assisted Broadcast Channels with Rate Splitting and Improper Signaling

In this paper, we study the achievable rate region of 1-layer rate splitting (RS) in the presence of hardware impairment (HWI) and improper Gaussian signaling (IGS) for a single-cell reconfigurable intelligent surface (RIS) assisted broadcast channel (BC). We assume that the transceivers may suffer from an imbalance in in-band and quadrature signals, which is known as I/Q imbalance (IQI). The received signal and noise can be improper when there exists IQI. Therefore, we employ IGS to compensate for IQI as well as to manage interference. Our results show that RS and RIS can significantly enlarge the rate region, where the role of RS is to manage interference while RIS mainly improves the coverage.
ENGNN: A General Edge-Update Empowered GNN Architecture for Radio Resource Management in Wireless Networks

In order to achieve high data rate and ubiquitous connectivity in future wireless networks, a key task is to efficiently manage the radio resource by judicious beamforming and power allocation. Unfortunately, the iterative nature of the commonly applied optimization-based algorithms cannot meet the low latency requirements due to the high computational complexity. For real-time implementations, deep learning-based approaches, especially the graph neural networks (GNNs), have been demonstrated with good scalability and generalization performance due to the permutation equivariance (PE) property. However, the current architectures are only equipped with the node-update mechanism, which prohibits the applications to a more general setup, where the unknown variables are also defined on the graph edges. To fill this gap, we propose an edge-update mechanism, which enables GNNs to handle both node and edge variables and prove its PE property with respect to both transmitters and receivers. Simulation results on typical radio resource management problems demonstrate that the proposed method achieves higher sum rate but with much shorter computation time than state-of-the-art methods and generalizes well on different numbers of base stations and users, different noise variances, interference levels, and transmit power budgets.
Causal Deep Learning: Causal Capsules and Tensor Transformers

We derive a set of causal deep neural networks whose architectures are a consequence of tensor (multilinear) factor analysis. Forward causal questions are addressed with a neural network architecture composed of causal capsules and a tensor transformer. The former estimate a set of latent variables that represent the causal factors, and the latter governs their interaction. Causal capsules and tensor transformers may be implemented using shallow autoencoders, but for a scalable architecture we employ block algebra and derive a deep neural network composed of a hierarchy of autoencoders. An interleaved kernel hierarchy preprocesses the data resulting in a hierarchy of kernel tensor factor models. Inverse causal questions are addressed with a neural network that implements multilinear projection and estimates the causes of effects. As an alternative to aggressive bottleneck dimension reduction or regularized regression that may camouflage an inherently underdetermined inverse problem, we prescribe modeling different aspects of the mechanism of data formation with piecewise tensor models whose multilinear projections are well-defined and produce multiple candidate solutions. Our forward and inverse neural network architectures are suitable for asynchronous parallel computation.
On the Complexity of Sub-Tree Scheduling for Wireless Sensor Networks with Partial Coverage

Given an undirected graph $G$ whose edge weights change over $s$ time slots, the sub-tree scheduling for wireless sensor networks with partial coverage asks to partition the vertices of $G$ in $s$ non-empty trees such that the total weight of the trees is minimized. In this note we show that the problem is NP-hard in both the cases where $s$ $(i)$ is part of the input and $(ii)$ is a fixed instance parameter. In both our proofs we reduce from the cardinality Steiner tree problem. We additionally give polynomial-time algorithms for structured inputs of the problem.
Unsupervised Acoustic Scene Mapping Based on Acoustic Features and Dimensionality Reduction

Classical methods for acoustic scene mapping require the estimation of time difference of arrival (TDOA) between microphones. Unfortunately, TDOA estimation is very sensitive to reverberation and additive noise. We introduce an unsupervised data-driven approach that exploits the natural structure of the data. Our method builds upon local conformal autoencoders (LOCA) - an offline deep learning scheme for learning standardized data coordinates from measurements. Our experimental setup includes a microphone array that measures the transmitted sound source at multiple locations across the acoustic enclosure. We demonstrate that LOCA learns a representation that is isometric to the spatial locations of the microphones. The performance of our method is evaluated using a series of realistic simulations and compared with other dimensionality-reduction schemes. We further assess the influence of reverberation on the results of LOCA and show that it demonstrates considerable robustness.
An Event-based Algorithm for Simultaneous 6-DOF Camera Pose Tracking and Mapping

Compared to regular cameras, Dynamic Vision Sensors or Event Cameras can output compact visual data based on a change in the intensity in each pixel location asynchronously. In this paper, we study the application of current image-based SLAM techniques to these novel sensors. To this end, the information in adaptively selected event windows is processed to form motion-compensated images. These images are then used to reconstruct the scene and estimate the 6-DOF pose of the camera. We also propose an inertial version of the event-only pipeline to assess its capabilities. We compare the results of different configurations of the proposed algorithm against the ground truth for sequences of two publicly available event datasets. We also compare the results of the proposed event-inertial pipeline with the state-of-the-art and show it can produce comparable or more accurate results provided the map estimate is reliable.
Causal Inference (C-inf) - closed form worst case typical phase transitions

In this paper we establish a mathematically rigorous connection between Causal inference (C-inf) and the low-rank recovery (LRR) . Using Random Duality Theory (RDT) concepts developed in [46,48,50] and novel mathematical strategies related to free probability theory, we obtain the exact explicit typical (and achievable) worst case phase transitions (PT). These PT precisely separate scenarios where causal inference via LRR is possible from those where it is not. We supplement our mathematical analysis with numerical experiments that conﬁrm the theoretical predictions of PT phenomena, and further show that the two closely match for fairly small sample sizes. We obtain simple closed form representations for the resulting PTs, which highlight direct relations between the low rankness of the target C-inf matrix and the time of the treatment. Hence, our results can be used to determine the range of C-inf’s typical applicability.
Vertex and face permutation order compression for efficient animation support

In MPEG-4, 3D mesh coding (3DMC) achieves 40:1 to 50:1 compression ratio over 3-D meshes (in VRML IndexedFaceSet representation) without noticeable visual degradation. This substantial gain comes not for free: it changes the vertex and face permutation order of the original 3-D mesh model. This vertex and face permutation order change may cause a serious problem for animation, editing operation, and special effects, where the original permutation order information is critical not only to the mesh representation, but also to the related tools. To fix this problem, we need to transmit the vertex and face permutation order information additionally. This additional transmission causes the unexpected increase of the bitstream size. In this paper, we proposed a novel vertex and face permutation order compression algorithm to address the vertex and face permutation order change by the 3DMC encoding with the minimal increase of side information. Our proposed vertex and face permutation order coding method is based on the adaptive probability model, which makes allocating one fewer bits codeword to each vertex and face permutation order in every distinguishable unit as encoding proceeds. Additionally to the adaptive probability model, we further increased the coding efficiency of the proposed method by representing and encoding each vertex and face permutation order per connected component (CC). Simulation results demonstrated that the proposed algorithm can encode the vertex and face permutation order losslessly while making up to 12% bit-saving compared with the logarithmic representation based on the fixed probability model.
Problems of intense negative ion beam transport (invited)

The results of research of physical processes in intense negative ion beams without magnetic fields are presented. As a result of this research: (1) mechanisms of space charge compensation in negative ion beams are determined, (2) stability of compensated states with respect to excitation of plasma oscillations is analyzed, (3) mechanisms of nonlinear limitation of oscillation amplitudes are determined, and (4) values of steady and alternating fields in the beam are found and their influence on transport characteristics of the beams is estimated. It is demonstrated that ion-ion instability at low gas pressure may lead to catastrophic consequences: practically total decompensation of the beam and unacceptable angular scattering. A method of stabilization of this instability, and a technique of focusing negative ion beams by space charge fields are proposed.
Approximations of higher-order fractional differentiators and integrators using indirect discretization

This paper describes new approximations of fractional order integrators (FOIs) and fractional order differentiators (FODs) by using a continued fraction expansion-based indirect discretization scheme. Different tenth-order fractional blocks have been derived by applying three different s-to-z transforms described earlier by Al-Alaoui, namely new two-segment, four-segment, and new optimized four-segment operators. A new addition has been done in the new optimized four-segment operator by modifying it by the zero reflection method. All proposed half (s$^{\pm 1/2})$ and one-fourth (s$^{\pm 1/4})$ differentiator and integrator models fulfill the stability criterion. The tenth-order fractional differ-integrators (s$^{\pm \alpha })$ based on the modified new optimized four-segment rule show tremendously improved results with relative magnitude errors (dB) of $\le $ -15 dB for $\alpha $ = 1/2 and $\le $ -20 dB for $\alpha $ = 1/4 in the full range of Nyquist frequency so these have been further analyzed. The main contribution of this paper lies in the reduction of these tenth-order blocks into four new fifth-order blocks of half and one-fourth order models of FODs and FOIs. The analyses of magnitude and phase responses show that the proposed new fifth-order half and one-fourth differ-integrators closely approximate their ideal counterparts and outperform the existing ones.
Data-constrained Solar Modeling with GX Simulator

To facilitate the study of solar flares and active regions, we have created a modeling framework, the freely distributed GX Simulator IDL package, that combines 3D magnetic and plasma structures with thermal and nonthermal models of the chromosphere, transition region, and corona. Its object-based modular architecture, which runs on Windows, Mac, and Unix/Linux platforms, offers the ability to either import 3D density and temperature distribution models, or to assign numerically defined coronal or chromospheric temperatures and densities, or their distributions, to each individual voxel. GX Simulator can apply parametric heating models involving average properties of the magnetic field lines crossing a given voxel, as well as compute and investigate the spatial and spectral properties of radio, (sub)millimeter, EUV, and X-ray emissions calculated from the model, and quantitatively compare them with observations. The package includes a fully automatic model production pipeline that, based on minimal users input, downloads the required SDO/HMI vector magnetic field data, performs potential or nonlinear force-free field extrapolations, populates the magnetic field skeleton with parameterized heated plasma coronal models that assume either steady-state or impulsive plasma heating, and generates non-LTE density and temperature distribution models of the chromosphere that are constrained by photospheric measurements. The standardized models produced by this pipeline may be further customized through specialized IDL scripts, or a set of interactive tools provided by the graphical user interface. Here, we describe the GX Simulator framework and its applications.