Miracle-dz/UniFAR-FTUs huggingface dataset:
Stability & Generalisation of Gradient Descent for Shallow Neural Networks without the Neural Tangent Kernel

We revisit on-average algorithmic stability of GD for training overparameterised shallow neural networks and prove new generalisation and excess risk bounds without the NTK or PL assumptions. In particular, we show oracle type bounds which reveal that the generalisation and excess risk of GD is controlled by an interpolating network with the shortest GD path from initialisation (in a sense, an interpolating network with the smallest relative norm). While this was known for kernelised interpolants, our proof applies directly to networks trained by GD without intermediate kernelisation. At the same time, by relaxing oracle inequalities developed here we recover existing NTK-based risk bounds in a straightforward way, which demonstrates that our analysis is tighter. Finally, unlike most of the NTK-based analyses we focus on regression with label noise and show that GD with early stopping is consistent.
A Data-driven feature selection and machine-learning model benchmark for the prediction of longitudinal dispersion coefficient

Longitudinal Dispersion(LD) is the dominant process of scalar transport in natural streams. An accurate prediction on LD coefficient(Dl) can produce a performance leap in related simulation. The emerging machine learning(ML) techniques provide a self-adaptive tool for this problem. However, most of the existing studies utilize an unproved quaternion feature set, obtained through simple theoretical deduction. Few studies have put attention on its reliability and rationality. Besides, due to the lack of comparative comparison, the proper choice of ML models in different scenarios still remains unknown. In this study, the Feature Gradient selector was first adopted to distill the local optimal feature sets directly from multivariable data. Then, a global optimal feature set (the channel width, the flow velocity, the channel slope and the cross sectional area) was proposed through numerical comparison of the distilled local optimums in performance with representative ML models. The channel slope is identified to be the key parameter for the prediction of LDC. Further, we designed a weighted evaluation metric which enables comprehensive model comparison. With the simple linear model as the baseline, a benchmark of single and ensemble learning models was provided. Advantages and disadvantages of the methods involved were also discussed. Results show that the support vector machine has significantly better performance than other models. Decision tree is not suitable for this problem due to poor generalization ability. Notably, simple models show superiority over complicated model on this low-dimensional problem, for their better balance between regression and generalization.
Blowing Bubbles around Intermediate-Mass Stars: Feedback from Main-Sequence Winds is not Enough

Numerous spherical ``shells" have been observed in young star-forming environments that host low- and intermediate-mass stars. These observations suggest that these shells may be produced by isotropic stellar wind feedback from young main-sequence stars. However, the driving mechanism for these shells remains uncertain because the momentum injected by winds is too low to explain their sizes and dynamics due to their low mass-loss rates. However, these studies neglect how the wind kinetic energy is transferred to the ISM and instead assume it is instantly lost via radiation, suggesting that these shells are momentum-driven. Intermediate-mass stars have fast ($v_w \gtrsim 1000$ km/s) stellar winds and therefore the energy injected by winds should produce energy-driven adiabatic wind bubbles that are larger than momentum-driven wind bubbles. Here, we explore if energy-driven wind feedback can produce the observed shells by performing a series of 3D magneto-hydrodynamic simulations of wind feedback from intermediate-mass and high-mass stars that are placed in a magnetized, turbulent molecular cloud. We find that, for the high-mass stars modeled, energy-driven wind feedback produces $\sim$pc scale wind bubbles in molecular clouds that agree with the observed shell sizes but winds from intermediate-mass stars can not produce similar shells because of their lower mass-loss rates and velocities. Therefore, such shells must be driven by other feedback processes inherent to low- and intermediate-mass star formation.
Local resonances and parametric level dynamics in the many-body localized phase

By varying the disorder realisation in the many-body localised (MBL) phase, we investigate the influence of resonances on spectral properties. The standard theory of the MBL phase is based on the existence of local integrals of motion (LIOM), and eigenstates of the time evolution operator can be described as LIOM configurations. We show that smooth variations of the disorder give rise to avoided level crossings, and we identify these with resonances between LIOM configurations. Through this parametric approach, we develop a theory for resonances in terms of standard properties of non-resonant LIOM. This framework describes resonances that are locally pairwise, and is appropriate in arbitrarily large systems deep within the MBL phase. We show that resonances are associated with large level curvatures on paths through the ensemble of disorder realisations, and we determine the curvature distribution. By considering the level repulsion associated with resonances we calculate the two-point correlator of the level density. We also find the distributions of matrix elements of local observables and discuss implications for low-frequency dynamics.
Channel-Wise Early Stopping without a Validation Set via NNK Polytope Interpolation

State-of-the-art neural network architectures continue to scale in size and deliver impressive generalization results, although this comes at the expense of limited interpretability. In particular, a key challenge is to determine when to stop training the model, as this has a significant impact on generalization. Convolutional neural networks (ConvNets) comprise high-dimensional feature spaces formed by the aggregation of multiple channels, where analyzing intermediate data representations and the model's evolution can be challenging owing to the curse of dimensionality. We present channel-wise DeepNNK (CW-DeepNNK), a novel channel-wise generalization estimate based on non-negative kernel regression (NNK) graphs with which we perform local polytope interpolation on low-dimensional channels. This method leads to instance-based interpretability of both the learned data representations and the relationship between channels. Motivated by our observations, we use CW-DeepNNK to propose a novel early stopping criterion that (i) does not require a validation set, (ii) is based on a task performance metric, and (iii) allows stopping to be reached at different points for each channel. Our experiments demonstrate that our proposed method has advantages as compared to the standard criterion based on validation set performance.
A Free and Fair Economy: A Game of Justice and Inclusion

Frequent violations of fair principles in real-life settings raise the fundamental question of whether such principles can guarantee the existence of a self-enforcing equilibrium in a free economy. We show that elementary principles of distributive justice guarantee that a pure-strategy Nash equilibrium exists in a finite economy where agents freely (and non-cooperatively) choose their inputs and derive utility from their pay. Chief among these principles is that: 1) your pay should not depend on your name, and 2) a more productive agent should not earn less. When these principles are violated, an equilibrium may not exist. Moreover, we uncover an intuitive condition---technological monotonicity---that guarantees equilibrium uniqueness and efficiency. We generalize our findings to economies with social justice and inclusion, implemented in the form of progressive taxation and redistribution, and guaranteeing a basic income to unproductive agents. Our analysis uncovers a new class of strategic form games by incorporating normative principles into non-cooperative game theory. Our results rely on no particular assumptions, and our setup is entirely non-parametric. Illustrations of the theory include applications to exchange economies, surplus distribution in a firm, contagion and self-enforcing lockdown in a networked economy, and bias in the academic peer-review system.
Logical Characterization of Coherent Uninterpreted Programs

An uninterpreted program (UP) is a program whose semantics is defined over the theory of uninterpreted functions. This is a common abstraction used in equivalence checking, compiler optimization, and program verification. While simple, the model is sufficiently powerful to encode counter automata, and, hence, undecidable. Recently, a class of UP programs, called coherent, has been proposed and shown to be decidable. We provide an alternative, logical characterization, of this result. Specifically, we show that every coherent program is bisimilar to a finite state system. Moreover, an inductive invariant of a coherent program is representable by a formula whose terms are of depth at most 1. We also show that the original proof, via automata, only applies to programs over unary uninterpreted functions. While this work is purely theoretical, it suggests a novel abstraction that is complete for coherent programs but can be soundly used on arbitrary uninterpreted (and partially interpreted) programs.
Finding Failures in High-Fidelity Simulation using Adaptive Stress Testing and the Backward Algorithm

Validating the safety of autonomous systems generally requires the use of high-fidelity simulators that adequately capture the variability of real-world scenarios. However, it is generally not feasible to exhaustively search the space of simulation scenarios for failures. Adaptive stress testing (AST) is a method that uses reinforcement learning to find the most likely failure of a system. AST with a deep reinforcement learning solver has been shown to be effective in finding failures across a range of different systems. This approach generally involves running many simulations, which can be very expensive when using a high-fidelity simulator. To improve efficiency, we present a method that first finds failures in a low-fidelity simulator. It then uses the backward algorithm, which trains a deep neural network policy using a single expert demonstration, to adapt the low-fidelity failures to high-fidelity. We have created a series of autonomous vehicle validation case studies that represent some of the ways low-fidelity and high-fidelity simulators can differ, such as time discretization. We demonstrate in a variety of case studies that this new AST approach is able to find failures with significantly fewer high-fidelity simulation steps than are needed when just running AST directly in high-fidelity. As a proof of concept, we also demonstrate AST on NVIDIA’s DriveSim simulator, an industry state-of-the-art high-fidelity simulator for finding failures in autonomous vehicles.
Do posture and straining influence urinary‐flow parameters in normal women?

The influence of posture of the pelvis and straining on urinary flow was investigated in 21 normal women, mainly physiotherapists, who were asked to urinate on an uro‐flow chair at their usual time and frequency. Subjects were at random instructed to urinate in five different test situations: anteversion, anteversion with straining, retroversion, retroversion with straining, and forward bending without straining. The urinary‐flow parameters investigated were volume, peak flow, time to peak, peak‐to‐end time, total time, and mean flow. The analysis was done by means of analysis of variance but only for micturition volumes >150 mL. The morphology of the urinary‐flow curves was examined for the presence of irregularities and increasing (after top) or decreasing (for top) curve tops and after‐dribbling. Results demonstrated no significant differences for peak flow, total time, and mean flow in the anteversion, retroversion, and the forward‐bending position. This holds for test situations and re‐test controls. However, straining increased the peak flow and mean flow rates in all positions and in all women, whereas it reduced the total voiding time. The voided volumes were lowest in anteversion. Irregularities were less frequent in the forward‐bending position. It can be concluded that the forward‐bending position is the most preferable urinating position to relax the pelvic floor muscles. Neurourol. Urodynam. 19:3–8, 2000. © 2000 Wiley‐Liss, Inc.
HAPS Selection for Hybrid RF/FSO Satellite Networks

Nonterrestrial networks have been attracting much interest from the industry and academia. Satellites and high-altitude platform station (HAPS) systems are expected to be the key enablers of next-generation wireless networks. In this article, we introduce a novel downlink satellite communication (SatCom) model, where free-space optical (FSO) communication is adopted between a satellite and a HAPS node. A hybrid FSO/radio-frequency transmission model is used between the HAPS node and the ground station (GS). In the first phase of transmission, the satellite selects the HAPS node that provides the highest signal-to-noise ratio. In the second phase, the selected HAPS decodes and forwards the signal to the GS. To evaluate the performance of the proposed system, outage probability expressions are derived for exponentiated Weibull and shadowed-Rician fading models while considering the atmospheric turbulence, stratospheric attenuation, and attenuation due to scattering, path loss, and pointing errors. Additionally, asymptotic analysis is carried out, and diversity gain is provided. Furthermore, the impacts of the aperture averaging technique, temperature, and wind speed are investigated. We also provide some important guidelines that can be helpful for the design of practical HAPS-aided SatCom. Finally, the results show that the use of HAPS improves the system performance and that the proposed model performs better than all other existing models.
Inclusion, equality and bias in designing online mass deliberative platforms

Designers of online deliberative platforms aim to counter the degrading quality of online debates and eliminate online discrimination based on class, race or gender. Support technologies such as machine learning and natural language processing open avenues for widening the circle of people involved in deliberation, moving from small groups to “crowd” scale. Some design features of large-scale online discussion systems allow larger numbers of people to discuss shared problems, enhance critical thinking, and formulate solutions. However, scaling up deliberation is challenging. We review the transdisciplinary literature on the design of digital mass-deliberation platforms and examine the commonly featured design aspects (e.g., argumentation support, automated facilitation, and gamiﬁcation). We ﬁnd that the literature is heavily focused on developing technical ﬁxes for scaling up deliberation, with a heavy western inﬂuence on design and test users skew young and highly educated. Contrastingly, there is a distinct lack of discussion on the nature of the design process, the inclusion of stakeholders and issues relating to inclusion, which may unwittingly per-petuate bias. Another tendency of deliberation platforms is to nudge participants to desired forms of argumentation, and simplifying deﬁnitions of good and bad arguments to ﬁt algorithmic purposes. Few studies bridge disciplines between deliberative theory, design and engineering. As a result, scaling up deliberation will likely advance in separate systemic siloes. We make design and process recommendations to correct this course and suggest avenues for future research.
Low-Complexity Improved-Throughput Generalised Spatial Modulation: Bit-to-Symbol Mapping, Detection and Performance Analysis

Low-complexity improved-throughput generalised spatial modulation (LCIT-GSM) is proposed. More explicitly, in GSM, extra information bits are conveyed implicitly by activating a fixed number $N_{a}$ out of $N_{t}$ transmit antennas (TAs) at a time. As a result, GSM has the advantage of a reduced number of radio-frequency (RF) chains and reduced inter-antenna interference (IAI) at the cost of a lower throughput than its multiplexing-oriented full-RF based counterparts. Variable-${N_a}$ GSM mitigates this throughput reduction by incorporating all possible TA activation patterns associated with a variable value $N_{a}$ ranging from $1$ to $N_{t}$ during a single channel-use, which maximises the throughput of GSM but suffers a high complexity of the mapping book design and demodulation. In order to mitigate the complexity, \emph{first of all}, we propose two efficient schemes for mapping the information bits to the TA activation patterns, which can be readily scaled to massive MIMO setups. \emph{Secondly}, in the absence of IAI, we derive a pair of low-complexity near-optimal detectors, one of them has a reduced search scope, while the other benefits from a decoupled single-stream based signal detection algorithm. \emph{Finally}, the performance of the proposed LCIT-GSM system is characterised by the error probability upper bound (UB). Our Monte Carlo based simulation results confirm the improved error performance of our proposed scheme, despite its reduced signal detection complexity.
The Pursuit and Evasion of Drones Attacking an Automated Turret

This paper investigates the pursuit-evasion problem of a defensive gun turret and one or more attacking drones. The turret must "visit" each attacking drone once, as quickly as possible, to defeat the threat. This constitutes a Shortest Hamiltonian Path (SHP) through the drones. The investigation considers situations with increasing fidelity, starting with a 2D kinematic model and progressing to a 3D dynamic model. In 2D we determine the region from which one or more drones can always reach a turret, or the region close enough to it where they can evade the turret. This provides optimal starting angles for n drones around a turret and the maximum starting radius for one and two drones.We show that safety regions also exist in 3D and provide a controller so that a drone in this region can evade the pan-tilt turret. Through simulations we explore the maximum range n drones can start and still have at least one reach the turret, and analyze the effect of turret behavior and the drones’ number, starting configuration, and behaviors.
Tracking Fast Neural Adaptation by Globally Adaptive Point Process Estimation for Brain-Machine Interface

Brain-machine interfaces (BMIs) help the disabled restore body functions by translating neural activity into digital commands to control external devices. Neural adaptation, where the brain signals change in response to external stimuli or movements, plays an important role in BMIs. When subjects purely use neural activity to brain-control a prosthesis, some neurons will actively explore a new tuning property to accomplish the movement task. The prediction of this neural tuning property can help subjects adapt more efficiently to brain control and maintain a good decoding performance. Existing prediction methods track the slow change of the tuning property in the manual control, which is not suitable for the fast neural adaptation in brain control. In order to identify the active neurons in brain control and track their tuning property changes, we propose a globally adaptive point process method (GaPP) to estimate the neural modulation state from spike trains, decompose the states into the hyper preferred direction and reconstruct the kinematics in a dual-model framework. We implement the method on real data from rats performing a two-lever discrimination task under manual control and brain control. The results show our method successfully predicts the neural modulation state and identifies the neurons that become active in brain control. Compared to the existing method, ours tracks the fast changes of the hyper preferred direction from manual control to brain control more accurately and efficiently and reconstructs the kinematics better and faster.
$\text{M}$, $\text{B}$ and $\text{Co}_1$ are recognisable by their prime graphs

Abstract. The prime graph, or Gruenberg–Kegel graph, of a finite group G is the graph Γ(G) whose vertices are the prime divisors of |G|, and whose edges are the pairs {p, q} for which G contains an element of order pq. A finite group G is recognisable by its prime graph if every finite group H with Γ(H) = Γ(G) is isomorphic to G. By a result of Cameron and Maslova, every such group must be almost simple, so one natural case to investigate is that in which G is one of the 26 sporadic simple groups. Existing work of various authors answers the question of recognisability by prime graph for all but three of these groups, namely the Monster, M, the Baby Monster, B, and the first Conway group, Co1. We prove that these three groups are recognisable by their prime graphs.
The Variance and the Asymptotic Distribution of the Length of Longest $k$-alternating Subsequences

We obtain an explicit formula for the variance of the number of $k$-peaks in a uniformly random permutation. This is then used to obtain an asymptotic formula for the variance of the length of longest $k$-alternating subsequence in random permutations. Also a central limit is proved for the latter statistic.
Multi-element analysis of Tinishu Akaki River sediment, Ethiopia, by ICP-MS after microwave assisted digestion

This work describes a method for multi-element analysis of cadmium, cobalt, chromium, copper, manganese, nickel, lead and zinc in Tinishu Akaki River sediment, Ethiopia, by inductively coupled plasma mass spectrometry. River sediment samples were collected between November 2002 and January 2003 for the first campaign, and between November 2003 and January 2004 for the second campaign. The samples were kept frozen until they were lyophilized, grounded in a mortar and pestle and sieved. A fraction less than 63 mu m was taken for the analysis. Different acid combinations were investigated for the digestion of sediment samples: HCl/HNO3, HClO4/HNO3 and HF/HCl/HNO3/ H3BO(3) in a microwave digestion system. Complete dissolution of materials was achieved with the latter. Optimum values for instrumental parameters of the ICP-MS were sought and set up. Multi-element standard solutions were used for the calibration of the procedure. The methodology and accuracy was evaluated by the analysis of certified reference materials of sedimental origin. Very low limits of detection were obtained for all elements, which were less than 0.65 mu g g(-1), except for Zn, which was 2.8 mu g g(-1). Good precision, with relative standard deviations better than 7% for most of the elements, was found.
Efficiency and Productivity of Commercial Banks: Evidence from Bangladesh

* Corresponding Author Accepted : 16 July 2019; Published online : 20 July, 2019 DOI: https://doi.org/10.5281/zenodo.3344973 Abstract: The study investigates the relative technical efficiency and productivity change of Bangladeshi commercial banks during the period 2013 to 2017. We utilize the Data Envelopment Analysis (DEA) technique to evaluate the efficiency of sample banks and The Malmquist productivity index (MPI) to assess the productivity expands of banks over time. The empirical results indicate that listed commercial banks in Bangladesh were technically inefficient. We discover that there are only six banks which have been technically efficient, while rests of the sample banks are classified as technically inefficient. The study also illustrates that only 3 among the 19 banks employed in this paper displayed an overall improvement in productivity and sixteen banks presented an overall decline in productivity. The outcomes form this study are anticipated to add notably toward resolution-making for controllers, lawmakers, bank managers, shareholders, and also to the existing information on technical efficiency and level of productivity of the Bangladesh banking area.