Miracle-dz/UniFAR-FTUs huggingface dataset:
Beneficial Investigation of Extended-Range Electric Powertrains with Dual Motor Input and Automated Manual Transmission

With the functions of plug-in charging and on-board electric generation, extended-range electric vehicles (EREVs) can overcome limitations of pure electric vehicles, including short driving range, large battery requirement, and charging interruption on road. Current EREVs mainly employ a propulsion motor and single-speed transmission, which suffer poorer dynamic performance and lower motor efficiency. Therefore, this research proposes two novel EREV powertrains to overcome the above shortcomings. Both designs consist of the same electric drivetrain with dual propulsion motors and four-speed automated manual transmission. The fundamental difference is from the range extenders that connect the engines to the electric drivetrains in different ways. First, the parallel engine powertrain (PEP) connects the engine mechanically through a frictional clutch. Second, the series engine powertrain (SEP1) features the engine-generator unit for electric generation. The performance improvements of the proposed configurations are compared to the traditional single motor-series engine powertrain (SEP0). Based on dynamic requirements, the parameters of powertrain components are designed to ensure all configurations equivalently. The model predictive control-based energy management strategy with the forward dynamic programming solver is then designed. Performance indexes for comparison include electric consumption, fuel consumption and gas emissions (CO, HC, NOx, and PM). Simulation results in the driving cycle CBDC and ECEx5 show that, compared to the traditional SEP0, the SEP1 improves all performance indexes significantly, while the PEP decreases fuel consumption further but increases most noxious exhaust emissions considerably.
Speeding up Word Mover's Distance and its variants via properties of distances between embeddings

The Word Mover's Distance (WMD) proposed by Kusner et al. is a distance between documents that takes advantage of semantic relations among words that are captured by their embeddings. This distance proved to be quite effective, obtaining state-of-art error rates for classification tasks, but is also impracticable for large collections/documents due to its computational complexity. For circumventing this problem, variants of WMD have been proposed. Among them, Relaxed Word Mover's Distance (RWMD) is one of the most successful due to its simplicity, effectiveness, and also because of its fast implementations. Relying on assumptions that are supported by empirical properties of the distances between embeddings, we propose an approach to speed up both WMD and RWMD. Experiments over 10 datasets suggest that our approach leads to a significant speed-up in document classification tasks while maintaining the same error rates.
Learning vector representation of local content and matrix representation of local motion, with implications for V1

This paper proposes a representational model for image pair such as consecutive video frames that are related by local pixel displacements, in the hope that the model may shed light on motion perception in primary visual cortex (V1). The model couples the following two components. (1) The vector representations of local contents of images. (2) The matrix representations of local pixel displacements caused by the relative motions between the agent and the objects in the 3D scene. When the image frame undergoes changes due to local pixel displacements, the vectors are multiplied by the matrices that represent the local displacements. Our experiments show that our model can learn to infer local motions. Moreover, the model can learn Gabor-like filter pairs of quadrature phases.
On 2k-Hitchin’s equations and Higgs bundles: A survey

We study the [Formula: see text]-Hitchin’s equations introduced by Ward from the geometric viewpoint of Higgs bundles. After an introduction on Higgs bundles and [Formula: see text]-Hitchin’s equations, we review some elementary facts on complex geometry and Yang–Mills theory. Then, we study some properties of holomorphic vector bundles and Higgs bundles and we review the Hermite–Yang–Mills equations together with two functionals related to such equations. Using some geometric tools we show that, as far as Higgs bundles are concerned, [Formula: see text]-Hitchin’s equations are reduced to a set of two equations. Finally, we introduce a functional closely related to [Formula: see text]-Hitchin’s equations and we study some of its basic properties.
GroSS: Group-Size Series Decomposition for Whole Search-Space Training

We present Group-size Series (GroSS) decomposition, a mathematical formulation of tensor factorisation into a series of approximations of increasing rank terms. GroSS allows for dynamic and differentiable selection of factorisation rank, which is analogous to a grouped convolution. Therefore, to the best of our knowledge, GroSS is the first method to simultaneously train differing numbers of groups within a single layer, as well as all possible combinations between layers. In doing so, GroSS trains an entire grouped convolution architecture search-space concurrently. We demonstrate this through proof-of-concept architecture searches with performance objectives. GroSS represents a significant step towards liberating network architecture search from the burden of training and fine-tuning.
Deep Learning for Optimal Deployment of UAVs With Visible Light Communications

In this paper, the problem of dynamical deployment of unmanned aerial vehicles (UAVs) equipped with visible light communication (VLC) capabilities for optimizing the energy efficiency of UAV-enabled networks is studied. In the studied model, the UAVs can simultaneously provide communications and illumination to service ground users. Since ambient illumination increases the interference over VLC links while reducing the illumination threshold of the UAVs, it is necessary to consider the illumination distribution of the target area for UAV deployment optimization. This problem is formulated as an optimization problem which jointly optimizes UAV deployment, user association, and power efficiency while meeting the illumination and communication requirements of users. To solve this problem, an algorithm that combines the machine learning framework of gated recurrent units (GRUs) with convolutional neural networks (CNNs) is proposed. Using GRUs and CNNs, the UAVs can model the long-term historical illumination distribution and predict the future illumination distribution. Given the prediction of illumination distribution, the original nonconvex optimization problem can be divided into two sub-problems and is then solved using a low-complexity, iterative algorithm. Then, the proposed algorithm enables UAVs to determine the their deployment and user association to minimize the total transmit power. Simulation results using real data from the Earth observations group (EOG) at NOAA/NCEI show that the proposed approach can achieve up to 68.9% reduction in total transmit power compared to a conventional optimal UAV deployment that does not consider the illumination distribution and user association.
Inpatient liaison psychiatry: the experience of two Irish general hospitals without psychiatric units

Abstract Objective: The aim of this study was to examine the provision of psychiatric care to inpatients in two Irish general hospitals without psychiatric units and to comment on (a) how this service model compares with previous Irish studies, (b) whether it meets the goals of liaison psychiatry, and (c) the implications for future service planning. Method: Demographic and clinical details relating to all the psychiatric consultations to inpatients (i.e. there was no emergency service to the casualty department) in two general hospitals were collected over a six month period. Results: Although the service was provided on a nonemergency basis the referral rate [1.6% of total admissions], patient characteristics, reasons for referral, diagnoses, and suggested management strategies were very similar to previous Irish studies. Assessment of deliberate self-harm cases was the most common reason for referral [37.9% of 145 referrals]. Notably, there was a high level of diagnostic accuracy from non-psychiatric colleagues in this liaison model. Conclusions: This study, of a service model characterised by an emphasis on liaison, points to efforts on the part of the psychiatrist to improve communication with medical and surgical colleagues as being of primary importance in the development of general hospital psychiatry services.
BOWEL AID AS SUPPLEMENTARY MANAGEMENT IN MESH COMPLICATION- RELATED PAIN: A CASE REPORT

Millions of women suffering from urinary incontinence (UI) and pelvic organ prolapse (POP) globally, and many had synthetic sub-urethral procedures which is one of the mainstay therapy of stress urinary incontinence. It is paramount to learn from patients with mesh complications; in this particular case, perineal pain and dyspareunia related to complication of synthetic suburethral sling procedure. This patient has cessation of debilitating pain with the conservative use of bowel aid, and yet she remains to be continent in uresis.
Deep Neural Network Fingerprinting by Conferrable Adversarial Examples

In Machine Learning as a Service, a provider trains a deep neural network and provides many users access. The hosted (source) model is susceptible to model stealing attacks, where an adversary derives a \emph{surrogate model} from API access to the source model. For post hoc detection of such attacks, the provider needs a robust method to determine whether a suspect model is a surrogate of their model. We propose a fingerprinting method for deep neural network classifiers that extracts a set of inputs from the source model so that only surrogates agree with the source model on the classification of such inputs. These inputs are a subclass of transferable adversarial examples which we call \emph{conferrable} adversarial examples that exclusively transfer with a target label from a source model to its surrogates. We propose a new method to generate these conferrable adversarial examples. We present an extensive study on the unremovability of our fingerprint against fine-tuning, weight pruning, retraining, retraining with different architectures, three model extraction attacks from related work, transfer learning, adversarial training, and two new adaptive attacks. Our fingerprint is robust against distillation, related model extraction attacks, and even transfer learning when the attacker has no access to the model provider's dataset. Our fingerprint is the first method that reaches an AUC of 1.0 in verifying surrogates, compared to an AUC of 0.63 by previous fingerprints.
Bird Flocking Inspired Control Strategy for Multi-UAV Collective Motion

UAV collective motion has become a hot research topic in recent years. The realization of UAV collective motion, however, relied heavily on centralized control method and suffered from instability. Inspired by bird flocking theory, a control strategy for UAV collective motion with distributed measure and control methods was proposed in this study. In order to appropriately adjust the inter-agent distance suitable for realization, the control law based on bird flocking theory was optimized, and the convergence of velocities and collision avoidance properties were presented through simulation results. Furthermore, the stable collective motion of two UAVs using visual relative information only with proposed strategy in both indoor and outdoor GPS-denied environments were realized.
Lambada: Interactive Data Analytics on Cold Data Using Serverless Cloud Infrastructure

Serverless computing has recently attracted a lot of attention from research and industry due to its promise of ultimate elasticity and operational simplicity. However, there is no consensus yet on whether or not the approach is suitable for data processing. In this paper, we present Lambada, a serverless distributed data processing framework designed to explore how to perform data analytics on serverless computing. In our analysis, supported with extensive experiments, we show in which scenarios serverless makes sense from an economic and performance perspective. We address several important technical questions that need to be solved to support data analytics and present examples from several domains where serverless offers a cost and performance advantage over existing solutions.
Long-time asymptotics of stochastic reaction systems

We study the stochastic dynamics of a system of interacting species in a stochastic environment by means of a continuous-time Markov chain with transition rates depending on the state of the environment. Models of gene regulation in systems biology take this form. We characterise the finite-time distribution of the Markov chain, provide conditions for ergodicity, and characterise the stationary distribution (when it exists) as a mixture of Poisson distributions. The mixture measure is uniquely identified as the law of a fixed point of a stochastic recurrence equation. This recursion is crucial for statistical computation of moments and other distributional features.
Cross-connection structure of locally inverse semigroups

Locally inverse semigroups are regular semigroups whose idempotents form pseudo-semilattices. In this article, we describe the structure of locally inverse semigroups using Nambooripad's cross-connection theory. We characterise the categories involved as `unambiguous categories' and provide a new structure theorem for locally inverse semigroups. Further, recall that there are two major structure theorems involving special classes of locally inverse semigroups: inverse semigroups (ESN Theorem via inductive groupoids) and completely $0$-simple semigroups (via Rees matrix construction); they seem unrelated. In this article, we specialise our cross-connection description of locally inverse semigroups to these classes to exposit a unification of these different approaches to the structure theory of semigroups. In particular, we show that the structure theorem in inverse semigroups can be obtained using only one category, quite analogous to the ESN Theorem; in a completely $0$-simple semigroup, we show that the cross-connection coincides with the structure matrix.
An ALMA CO(2–1) Survey of Nearby Palomar–Green Quasars

The properties of the molecular gas can shed light on the physical conditions of quasar host galaxies and the effect of feedback from accreting supermassive black holes. We present a new CO(2–1) survey of 23 Palomar–Green quasars conducted with the Atacama Large Millimeter/submillimeter Array. CO emission was successfully detected in 91% (21/23) of the objects, from which we derive CO luminosities, molecular gas masses, and velocity line widths. Together with CO(1–0) measurements in the literature for 32 quasars (detection rate 53%), there are 15 quasars with both CO(1–0) and CO(2–1) measurements and, in total, 40 sources with CO measurements. We find that the line ratio of is subthermal and broadly consistent with nearby galaxies and other quasars previously studied. No clear correlation is found between R21 and the intensity of the interstellar radiation field or the luminosity of the active nucleus. As with the general galaxy population, quasar host galaxies exhibit a strong, tight, and linear LIR– relation, with a normalization consistent with that of starburst systems. We investigate the molecular-to-total-gas mass fraction with the aid of total gas masses inferred from dust masses previously derived from infrared observations. Although the scatter is considerable, the current data do not suggest that the CO-to-H2 conversion factor of quasar host galaxies significantly differs from that of normal star-forming galaxies.
SPEECHMINER: A Framework for Investigating and Measuring Speculative Execution Vulnerabilities

SPEculative Execution side Channel Hardware (SPEECH) Vulnerabilities have enabled the notorious Meltdown, Spectre, and L1 terminal fault (L1TF) attacks. While a number of studies have reported different variants of SPEECH vulnerabilities, they are still not well understood. This is primarily due to the lack of information about microprocessor implementation details that impact the timing and order of various micro-architectural events. Moreover, to date, there is no systematic approach to quantitatively measure SPEECH vulnerabilities on commodity processors. This paper introduces SPEECHMINER, a software framework for exploring and measuring SPEECH vulnerabilities in an automated manner. SPEECHMINER empirically establishes the link between a novel two-phase fault handling model and the exploitability and speculation windows of SPEECH vulnerabilities. It enables testing of a comprehensive list of exception-triggering instructions under the same software framework, which leverages covert-channel techniques and differential tests to gain visibility into the micro-architectural state changes. We evaluated SPEECHMINER on 9 different processor types, examined 21 potential vulnerability variants, confirmed various known attacks, and identified several new variants.
Rotation of anisotropic particles in Rayleigh–Bénard turbulence

Abstract Inertialess anisotropic particles in a Rayleigh–Bénard turbulent flow show maximal tumbling rates for weakly oblate shapes, in contrast with the universal behaviour observed in developed turbulence where the mean tumbling rate monotonically decreases with the particle aspect ratio. This is due to the concurrent effect of turbulent fluctuations and of a mean shear flow whose intensity, we show, is determined by the kinetic boundary layers. In Rayleigh–Bénard turbulence prolate particles align preferentially with the fluid velocity, while oblate ones orient with the temperature gradient. This analysis elucidates the link between particle angular dynamics and small-scale properties of convective turbulence and has implications for the wider class of sheared turbulent flows.
Noisy, Greedy and Not So Greedy k-means++

The k-means++ algorithm due to Arthur and Vassilvitskii has become the most popular seeding method for Lloyd's algorithm. It samples the first center uniformly at random from the data set and the other $k-1$ centers iteratively according to $D^2$-sampling where the probability that a data point becomes the next center is proportional to its squared distance to the closest center chosen so far. k-means++ is known to achieve an approximation factor of $O(\log k)$ in expectation. Already in the original paper on k-means++, Arthur and Vassilvitskii suggested a variation called greedy k-means++ algorithm in which in each iteration multiple possible centers are sampled according to $D^2$-sampling and only the one that decreases the objective the most is chosen as a center for that iteration. It is stated as an open question whether this also leads to an $O(\log k)$-approximation (or even better). We show that this is not the case by presenting a family of instances on which greedy k-means++ yields only an $\Omega(\ell\cdot \log k)$-approximation in expectation where $\ell$ is the number of possible centers that are sampled in each iteration. We also study a variation, which we call noisy k-means++ algorithm. In this variation only one center is sampled in every iteration but not exactly by $D^2$-sampling anymore. Instead in each iteration an adversary is allowed to change the probabilities arising from $D^2$-sampling individually for each point by a factor between $1-\epsilon_1$ and $1+\epsilon_2$ for parameters $\epsilon_1 \in [0,1)$ and $\epsilon_2 \ge 0$. We prove that noisy k-means++ compute an $O(\log^2 k)$-approximation in expectation. We also discuss some applications of this result.
Centrality Dependence of Multiplicity Fluctuations from a Hydrodynamical Approach

As one of the possible signals for the whereabouts of the critical point on the QCD phase diagram, recently, the multiplicity fluctuations in heavy-ion collisions have aroused much attention. It is a crucial observation of the Beam Energy Scan program of the Relativistic Heavy Ion Collider. In this work, we investigate the centrality dependence of the multiplicity fluctuations regarding the recent measurements from STAR Collaboration. By employing a hydrodynamical approach, the present study is dedicated to the noncritical aspects of the phenomenon. To be specific, in addition to the thermal fluctuations, finite volume corrections, and resonance decay at the freeze-out surface, the model is focused on the properties of the hydrodynamic expansion of the system and the event-by-event initial fluctuations. It is understood that the real signal of the critical point can only be obtained after appropriately subtracting the background; the latter is investigated in the present work. Besides the experimental data, our results are also compared to those of the hadronic resonance gas, as well as the transport models.
Non-additive large deviation function for the particle densities of driven systems in contact

We investigate the non-equilibrium large deviation function of the particle densities in two steady-state driven systems exchanging particles at a vanishing rate. We first derive through a systematic multi-scale analysis the coarse-grained master equation satisfied by the distribution of the numbers of particles in each system. Assuming that this distribution takes for large systems a large deviation form, we obtain the equation (similar to a Hamilton–Jacobi equation) satisfied by the large deviation function of the densities. Depending on the systems considered, this equation may satisfy or not the macroscopic detailed balance property, i.e., a time-reversibility property at large deviation level. In the absence of macroscopic detailed balance, the large deviation function can be determined as an expansion close to a solution satisfying macroscopic detailed balance. In this case, the large deviation function is generically non-additive, i.e., it cannot be split as two separate contributions from each system. In addition, the large deviation function can be interpreted as a non-equilibrium free energy, as it satisfies a generalization of the second law of thermodynamics, in the spirit of the Hatano–Sasa relation. Some of the results are illustrated on an exactly solvable driven lattice gas model.
[Re] Learning to Learn By Self-Critique

This work is a reproducibility study of the paper of Antoniou and Storkey [2019], published at NeurIPS 2019. Our results are in parts similar to the ones reported in the original paper, supporting the central claim of the paper that the proposed novel method, called Self-Critique and Adapt (SCA), improves the performance of MAML++. The conducted additional experiments on the Caltech-UCSD Birds 200 dataset confirm the superiority of SCA compared to MAML++. In addition, the reproduced paper suggests a novel high-end version of MAML++ for which we could not reproduce the same results. We hypothesize that this is due to the many implementation details that were omitted in the original paper.