Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Current State of Research in Explaining Ensemble Performance Using Margins

Empirical evidence shows that ensembles, such as bagging, boosting, random and rotation forests, generally perform better in terms of their generalization error than individual classifiers. To explain this performance, Schapire et al. (1998) developed an upper bound on the generalization error of an ensemble based on the margins of the training data, from which it was concluded that larger margins should lead to lower generalization error, everything else being equal. Many other researchers have backed this assumption and presented tighter bounds on the generalization error based on either the margins or functions of the margins. For instance, Shen and Li (2010) provide evidence suggesting that the generalization error of a voting classifier might be reduced by increasing the mean and decreasing the variance of the margins. In this article we propose several techniques and empirically test whether the current state of research in explaining ensemble performance holds. We evaluate the proposed methods through experiments with real and simulated data sets.
Limits on amplifiers of natural selection under death-Birth updating

The fixation probability of a single mutant invading a population of residents is among the most widely-studied quantities in evolutionary dynamics. Amplifiers of natural selection are population structures that increase the fixation probability of advantageous mutants, compared to well-mixed populations. Extensive studies have shown that many amplifiers exist for the Birth-death Moran process, some of them substantially increasing the fixation probability or even guaranteeing fixation in the limit of large population size. On the other hand, no amplifiers are known for the death-Birth Moran process, and computer-assisted exhaustive searches have failed to discover amplification. In this work we resolve this disparity, by showing that any amplification under death-Birth updating is necessarily bounded and transient. Our boundedness result states that even if a population structure does amplify selection, the resulting fixation probability is close to that of the well-mixed population. Our transience result states that for any population structure there exists a threshold r⋆ such that the population structure ceases to amplify selection if the mutant fitness advantage r is larger than r⋆. Finally, we also extend the above results to δ-death-Birth updating, which is a combination of Birth-death and death-Birth updating. On the positive side, we identify population structures that maintain amplification for a wide range of values r and δ. These results demonstrate that amplification of natural selection depends on the specific mechanisms of the evolutionary process.
Recognizing American Sign Language Manual Signs from RGB-D Videos

In this paper, we propose a 3D Convolutional Neural Network (3DCNN) based multi-stream framework to recognize American Sign Language (ASL) manual signs (consisting of movements of the hands, as well as non-manual face movements in some cases) in real-time from RGB-D videos, by fusing multimodality features including hand gestures, facial expressions, and body poses from multi-channels (RGB, depth, motion, and skeleton joints). To learn the overall temporal dynamics in a video, a proxy video is generated by selecting a subset of frames for each video which are then used to train the proposed 3DCNN model. We collect a new ASL dataset, ASL-100-RGBD, which contains 42 RGB-D videos captured by a Microsoft Kinect V2 camera, each of 100 ASL manual signs, including RGB channel, depth maps, skeleton joints, face features, and HDface. The dataset is fully annotated for each semantic region (i.e. the time duration of each word that the human signer performs). Our proposed method achieves 92.88 accuracy for recognizing 100 ASL words in our newly collected ASL-100-RGBD dataset. The effectiveness of our framework for recognizing hand gestures from RGB-D videos is further demonstrated on the Chalearn IsoGD dataset and achieves 76 accuracy which is 5.51 higher than the state-of-the-art work in terms of average fusion by using only 5 channels instead of 12 channels in the previous work.
Object-Agnostic Suction Grasp Affordance Detection in Dense Cluster Using Self-Supervised Learning.docx

In this paper we study grasp problem in dense cluster, a challenging task in warehouse logistics scenario. By introducing a two-step robust suction affordance detection method, we focus on using vacuum suction pad to clear up a box filled with seen and unseen objects. Two CNN based neural networks are proposed. A Fast Region Estimation Network (FRE-Net) predicts which region contains pickable objects, and a Suction Grasp Point Affordance network (SGPA-Net) determines which point in that region is pickable. So as to enable such two networks, we design a self-supervised learning pipeline to accumulate data, train and test the performance of our method. In both virtual and real environment, within 1500 picks (~5 hours), we reach a picking accuracy of 95% for known objects and 90% for unseen objects with similar geometry features.
A class of distributed event-triggered average consensus algorithms for multi-agent systems

This paper proposes a class of distributed event-triggered algorithms that solve the average consensus problem in multi-agent systems. By designing events such that a specifically chosen Lyapunov function is monotonically decreasing, event-triggered algorithms succeed in reducing communications among agents while still ensuring that the entire system converges to the desired state. However, depending on the chosen Lyapunov function the transient behaviours can be very different. Moreover, performance requirements also vary from application to application. Consequently, we are instead interested in considering a class of Lyapunov functions such that each Lyapunov function produces a different event-triggered coordination algorithm to solve the multi-agent average consensus problem. The proposed class of algorithms all guarantee exponential convergence of the resulting system and exclusion of Zeno behaviours. This allows us to easily implement different algorithms that all guarantee correctness to meet varying performance needs. We show that our findings can be applied to the practical clock synchronisation problem in wireless sensor networks (WSNs) and further corroborate their effectiveness with simulation results.
A Fuzzy-Fuzzy Vault Scheme

In this paper an enhanced fuzzy vault scheme is proposed which we refer to as fuzzy-fuzzy vault scheme. The proposed scheme builds on the classical fuzzy vault by adding the concept of uncertainty and imprecision to the classical scheme. To lock a secret key K in the classical fuzzy vault the locking and unlocking elements are crisp or real elements and consequently the locking and unlocking operations are strict imperative. In the fuzzy-fuzzy vault scheme, Alice locks the secret key K using a set of fuzzy elements that belong to multi-fuzzy set A~ obtained from a universe public set of fuzzy elements in a multi-fuzzy set F~_q and projecting them on polynomial p. The elements in multi-fuzzy sets F~_q and A~ are fuzzy using m membership functions MF_i, i=1,2,. ,m. Alice selects a set k of fuzzy elements fuzzy with a specific membership function MF_K from A~ to lock the vault. To hide the genuine locking points Alice generates a set of fuzzy chaff points that some of them do not lie on polynomial p while the other fuzzy chaff points may lie on polynomial p but fuzzy with different membership functions other than the membership function MF_K used to lock the vault. To unlock the fuzzy-fuzzy vault and retrieve the secret key K , Bob should have a set of unlocking fuzzy elements belonging to multi-fuzzy set B~ which substantially overlap with A~ is required. Then Bob selects t'_(TF_ki) fuzzy elements from B~ which are close to the t_(TF_k) fuzzy elements from A~ used by Alice to lock the vault. We show that adding uncertainty and imprecision by introducing fuzzy theory will enhance the security threshold of the fuzzy vault.
Searching for fast extragalactic X-ray transients in Chandra surveys

Recent works have discovered two fast (≈10 ks) extragalactic X-ray transients in the Chandra Deep Field-South (CDF-S XT1 and XT2). These findings suggest that a large population of similar extragalactic transients might exist in archival X-ray observations. We develop a method that can effectively detect such transients in a single Chandra exposure, and systematically apply it to Chandra surveys of CDF-S, CDF-N, DEEP2, UDS, COSMOS, and E-CDF-S, totaling 19 Ms of exposure. We find 13 transient candidates, including CDF-S XT1 and XT2. With the aid of available excellent multiwavelength observations, we identify the physical nature of all these candidates. Aside from CDF-S XT1 and XT2, the other 11 sources are all stellar objects, and all of them have z-band magnitudes brighter than 20. We estimate an event rate of ${59^{+77}_{-38}\ \rm {evt\ yr^{-1}\, deg^{-2}}}$ for CDF-S XT-like transients with 0.5–7 keV peak fluxes log Fpeak ≳ −12.6 (erg cm−2 s−1). This event rate translates to ${\approx 15^{+20}_{-10}}$ transients existing among Chandra archival observations at Galactic latitudes |b| > 20°, which can be probed in future work. Future missions such as Athena and the Einstein Probe with large grasps (effective area × field of view) are needed to discover a large sample (∼thousands) of fast extragalactic X-ray transients.
EVALUATION OF A MULTILINGUAL TTS SYSTEM WITH RESPECT TO THE PROSODIC QUALITY

Improving the naturalness of synthetic speech is an essential task in developing a text-to-speech (TTS) system. Mainly, it depends on the quality of the prosody model which is utilized in the TTS system. For our TTS system called DreSS (Dresden Speech Synthesizer), we compared three different met hods for generating the F0 contour to each other as well as to other synthesizers. Natural speech samples were used as a reference. Results show, that on a naturalness scale from 0 to 4, the natural speech samples reach a maximum score of 3.6, with values of 1.9 for the best synthesis, the LPC-based one. The system with an intonation control basing on the Fujisaki model leads the group of PSOLA systems, which are closely clustered at a mean of 1.54.
The “silent” imprint of musical training

Playing a musical instrument at a professional level is a complex multimodal task requiring information integration between different brain regions supporting auditory, somatosensory, motor, and cognitive functions. These kinds of task‐specific activations are known to have a profound influence on both the functional and structural architecture of the human brain. However, until now, it is widely unknown whether this specific imprint of musical practice can still be detected during rest when no musical instrument is used. Therefore, we applied high‐density electroencephalography and evaluated whole‐brain functional connectivity as well as small‐world topologies (i.e., node degree) during resting state in a sample of 15 professional musicians and 15 nonmusicians. As expected, musicians demonstrate increased intra‐ and interhemispheric functional connectivity between those brain regions that are typically involved in music perception and production, such as the auditory, the sensorimotor, and prefrontal cortex as well as Broca's area. In addition, mean connectivity within this specific network was positively related to musical skill and the total number of training hours. Thus, we conclude that musical training distinctively shapes intrinsic functional network characteristics in such a manner that its signature can still be detected during a task‐free condition. Hum Brain Mapp 37:536–546, 2016. © 2015 Wiley Periodicals, Inc.
The redshift-space momentum power spectrum – II. Measuring the growth rate from the combined 2MTF and 6dFGSv surveys

Measurements of the growth rate of structure, fσ8, in the low-redshift Universe allow stringent tests of the cosmological model. In this work, we provide new constraints on fσ8 at an effective redshift of z = 0.03 using the combined density and velocity fields measured by the 2MTF and 6dFGSv surveys. We do this by applying a new estimator of the redshift-space density and momentum (density-weighted velocity) power spectra, developed in the first paper of this series, to measured redshifts and peculiar velocities from these data sets. We combine this with models of the density and momentum power spectra in the presence of complex survey geometries and with an ensemble of simulated galaxy catalogues that match the survey selection functions and galaxy bias. We use these simulations to estimate the errors on our measurements and identify possible systematics. In particular, we are able to identify and remove biases caused by the non-Gaussianity of the power spectra by applying the Box-Cox transformation to the power spectra prior to fitting. After thorough validation of our methods we recover a constraint of $f\sigma _8(z_{\mathrm{eff}}=0.03)=0.404^{+0.082}_{-0.081}$ from the combined 2MTF and 6dFGSv data. This measurement is fully consistent with the expectations of general relativity and the Λ cold dark matter cosmological model. It is also comparable and complementary to constraints using different techniques on similar data, affirming the usefulness of our method for extracting cosmology from velocity fields.
Learning Structured Decision Problems with Unawareness

Structured models of decision making often assume an agent is aware of all possible states and actions in advance. This assumption is sometimes untenable. In this paper, we learn inﬂuence diagrams from both domain exploration and expert assertions in a way which guarantees convergence to optimal behaviour, even when the agent starts unaware of actions or belief variables that are critical to success. Our experiments show that our agent learns optimal behaviour on small and large decision problems, and that allowing an agent to conserve information upon discovering new possibilities results in faster convergence.
Private Compound Wildcard Queries Using Fully Homomorphic Encryption

Fully homomorphic encryption (FHE) brings a paradigm shift in cryptographic engineering by enabling us to resolve various unsolved problems. Among them, this work solves the problem to design a private database query (PDQ) protocol that supports compound queries with wildcard conditions on encrypted databases using FHE. More precisely, we consider a setting where clients outsource an encrypted database using FHE to a remote server, and later request results of compound queries including a wildcard search condition–given a set of attribute values $\lbrace A_1, A_2, \ldots, A_{n}\rbrace${A1,A2,. ,An} and a search pattern $W$W, retrieve a set of all attribute values $A_i$Ai's in which the pattern $W$W occurs. To this end, we first develop an algorithm for testing whether an encrypted string contains an encrypted pattern without revealing any information of the pattern, taking auxiliary encryptions as additional inputs. Then, using this algorithm, we design PDQ protocols on encrypted databases, which support compound queries using wildcard search conditions. Finally, we demonstrate proof-of-concept implementation results of our protocols by exploiting single-instruction-multiple-data operations and multi-threading techniques.
Resampling-based Assessment of Robustness to Distribution Shift for Deep Neural Networks

A novel resampling framework is proposed to evaluate the robustness and generalization capability of deep learning models with respect to distribution shift. We use Auto Encoder Variational Bayes to find a latent representation of the data, on which a Variational Gaussian Mixture Model is applied to deliberately create distribution shift by dividing the dataset into different clusters. Wasserstein distance is used to characterize the extent of distribution shift between the training and the testing data splits. We compare several conventional Convolutional Neural Network (CNN) architectures as well as Bayesian CNN models for image classification on the Fashion-MNIST dataset to assess their robustness under the deliberately created distribution shift.
Self-induced Scattering of Strahl Electrons in the Solar Wind

We investigate the scattering of strahl electrons by microinstabilities as a mechanism for creating the electron halo in the solar wind. We develop a mathematical framework for the description of electron-driven microinstabilities and discuss the associated physical mechanisms. We find that an instability of the oblique fast-magnetosonic/whistler (FM/W) mode is the best candidate for a microinstability that scatters strahl electrons into the halo. We derive approximate analytic expressions for the FM/W instability threshold in two different βc regimes, where βc is the ratio of the core electrons’ thermal pressure to the magnetic pressure, and confirm the accuracy of these thresholds through comparison with numerical solutions to the hot-plasma dispersion relation. We find that the strahl-driven oblique FM/W instability creates copious FM/W waves under low-βc conditions when , where U0s is the strahl speed and wc is the thermal speed of the core electrons. These waves have a frequency of about half the local electron gyrofrequency. We also derive an analytic expression for the oblique FM/W instability for βc ∼ 1. The comparison of our theoretical results with data from the Wind spacecraft confirms the relevance of the oblique FM/W instability for the solar wind. The whistler heat-flux, ion-acoustic heat-flux, kinetic-Alfvén-wave heat-flux, and electrostatic electron-beam instabilities cannot fulfill the requirements for self-induced scattering of strahl electrons into the halo. We make predictions for the electron strahl close to the Sun, which will be tested by measurements from Parker Solar Probe and Solar Orbiter.
Detecting Atomicity Violations for Event-Driven Node.js Applications

Node.js has been widely-used as an event-driven server-side architecture. To improve performance, a task in a Node.js application is usually divided into a group of events, which are non-deterministically scheduled by Node.js. Developers may assume that the group of events (named atomic event group) should be atomically processed, without interruption. However, the atomicity of an atomic event group is not guaranteed by Node.js, and thus other events may interrupt the execution of the atomic event group, break down the atomicity and cause unexpected results. Existing approaches mainly focus on event race among two events, and cannot detect high-level atomicity violations among a group of events. In this paper, we propose NodeAV, which can predictively detect atomicity violations in Node.js applications based on an execution trace. Based on happens-before relations among events in an execution trace, we automatically identify a pair of events that should be atomically processed, and use predefined atomicity violation patterns to detect atomicity violations. We have evaluated NodeAV on real-world Node.js applications. The experimental results show that NodeAV can effectively detect atomicity violations in these Node.js applications.
Beamforming Optimization for Wireless Network Aided by Intelligent Reflecting Surface With Discrete Phase Shifts

Intelligent reflecting surface (IRS) is a cost-effective solution for achieving high spectrum and energy efficiency in future wireless networks by leveraging massive low-cost passive elements that are able to reflect the signals with adjustable phase shifts. Prior works on IRS mainly consider continuous phase shifts at reflecting elements, which are practically difficult to implement due to the hardware limitation. In contrast, we study in this paper an IRS-aided wireless network, where an IRS with only a finite number of phase shifts at each element is deployed to assist in the communication from a multi-antenna access point (AP) to multiple single-antenna users. We aim to minimize the transmit power at the AP by jointly optimizing the continuous transmit precoding at the AP and the discrete reflect phase shifts at the IRS, subject to a given set of minimum signal-to-interference-plus-noise ratio (SINR) constraints at the user receivers. The considered problem is shown to be a mixed-integer non-linear program (MINLP) and thus is difficult to solve in general. To tackle this problem, we first study the single-user case with one user assisted by the IRS and propose both optimal and suboptimal algorithms for solving it. Besides, we analytically show that as compared to the ideal case with continuous phase shifts, the IRS with discrete phase shifts achieves the same squared power gain in terms of asymptotically large number of reflecting elements, while a constant proportional power loss is incurred that depends only on the number of phase-shift levels. The proposed designs for the single-user case are also extended to the general setup with multiple users among which some are aided by the IRS. Simulation results verify our performance analysis as well as the effectiveness of our proposed designs as compared to various benchmark schemes.
Distributed Learning with Random Features

Distributed learning and random projections are the most common techniques in large scale nonparametric statistical learning. In this paper, we study the generalization properties of kernel ridge regression using both distributed methods and random features. Theoretical analysis shows the combination remarkably reduces computational cost while preserving the optimal generalization accuracy under standard assumptions. In a benign case, $\mathcal{O}(\sqrt{N})$ partitions and $\mathcal{O}(\sqrt{N})$ random features are sufficient to achieve $\mathcal{O}(1/N)$ learning rate, where $N$ is the labeled sample size. Further, we derive more refined results by using additional unlabeled data to enlarge the number of partitions and by generating features in a data-dependent way to reduce the number of random features.