Miracle-dz/UniFAR-FTUs huggingface dataset:
A Binarizing NUV Prior and its Use for M-Level Control and Digital-to-Analog Conversion

Priors with a NUV representation (normal with unknown variance) have mostly been used for sparsity. In this paper, a novel NUV prior is proposed that effectively binarizes. While such a prior may have many uses, in this paper, we explore its use for discrete-level control (with M $\geq$ 2 levels) including, in particular, a practical scheme for digital-to-analog conversion. The resulting computations, for each planning period, amount to iterating forward-backward Gaussian message passing recursions (similar to Kalman smoothing), with a complexity (per iteration) that is linear in the planning horizon. In consequence, the proposed method is not limited to a short planning horizon and can therefore outperform "optimal" methods. A preference for sparse level switches can easily be incorporated.
Evaluation of hadronic emission in starburst galaxies and star-forming galaxies

In this work, we reanalyzed 11 years of spectral data from the Fermi Large Area Telescope (Fermi-LAT) of currently observed starburst galaxies (SBGs) and star-forming galaxies (SFGs). We used a one-zone model provided by NAIMA and the hadronic origin to explain the GeV observation data of the SBGs and SFGs. We found that a protonic distribution of a power-law form with an exponential cutoff can explain the spectra of most SBGs and SFGs. However, it cannot explain the spectral hardening components of NGC 1068 and NGC 4945 in the GeV energy band. Therefore, we considered the two-zone model to well explain these phenomena. We summarized the features of two model parameters, including the spectral index, cutoff energy, and proton energy budget. Similar to the evolution of supernova remnants (SNRs) in the Milky Way, we estimated the protonic acceleration limitation inside the SBGs to be the order of 102 TeV using the one-zone model; this is close to those of SNRs in the Milky Way.
Holistic Privacy and Usability of a Cryptocurrency Wallet

In this study, we overview the problems associated with the usability of cryptocurrency wallets, such as those used by ZCash, for end-users. The concept of"holistic privacy,"where information leaks in one part of a system can violate the privacy expectations of different parts of the system, is introduced as a requirement. To test this requirement with real-world software, we did a 60 person task-based evaluation of the usability of a ZCash cryptocurrency wallet by having users install and try to both send and receive anonymized ZCash transactions, as well as install a VPN and Tor. While the initial wallet installation was difficult, we found even a larger amount of difficulty integrating the ZCash wallet into network-level protection like VPNs or Tor, so only a quarter of users could complete a real-world purchase using the wallet.
Dynamic Defense Approach for Adversarial Robustness in Deep Neural Networks via Stochastic Ensemble Smoothed Model

Deep neural networks have been shown to suffer from critical vulnerabilities under adversarial attacks. This phenomenon stimulated the creation of different attack and defense strategies similar to those adopted in cyberspace security. The dependence of such strategies on attack and defense mechanisms makes the associated algorithms on both sides appear as closely reciprocating processes. The defense strategies are particularly passive in these processes, and enhancing initiative of such strategies can be an effective way to get out of this arms race. Inspired by the dynamic defense approach in cyberspace, this paper builds upon stochastic ensemble smoothing based on defense method of random smoothing and model ensemble. Proposed method employs network architecture and smoothing parameters as ensemble attributes, and dynamically change attribute-based ensemble model before every inference prediction request. The proposed method handles the extreme transferability and vulnerability of ensemble models under white-box attacks. Experimental comparison of ASR-vs-distortion curves with different attack scenarios shows that even the attacker with the highest attack capability cannot easily exceed the attack success rate associated with the ensemble smoothed model, especially under untargeted attacks.
Remarks on the spectral radius of $K_{r+1}$-saturated graphs

Write ρ (G) for the spectral radius of a graph G and Sn,r for the join Kr ∨ Kn−r. Let n > r ≥ 2 and G be a Kr+1-saturated graph of order n. Recently Kim, Kim, Kostochka, and O determined exactly the minimum value of ρ (G) for r = 2, and found an asymptotically tight bound on ρ (G) for r ≥ 3. They also conjectured that ρ (G) > ρ (Sn,r−1) , unless G = Sn,r−1. In this note their conjecture is proved.
Reconfiguring Diversity and Inclusion for AI Ethics

Activists, journalists, and scholars have long raised critical questions about the relationship between diversity, representation, and structural exclusions in data-intensive tools and services. We build on work mapping the emergent landscape of corporate AI ethics to center one outcome of these conversations: the incorporation of diversity and inclusion in corporate AI ethics activities. Using interpretive document analysis and analytic tools from the values in design field, we examine how diversity and inclusion work is articulated in public-facing AI ethics documentation produced by three companies that create application and services layer AI infrastructure: Google, Microsoft, and Salesforce. We find that as these documents make diversity and inclusion more tractable to engineers and technical clients, they reveal a drift away from civil rights justifications that resonates with the "managerialization of diversity" by corporations in the mid-1980s. The focus on technical artifacts - such as diverse and inclusive datasets - and the replacement of equity with fairness make ethical work more actionable for everyday practitioners. Yet, they appear divorced from broader DEI initiatives and relevant subject matter experts that could provide needed context to nuanced decisions around how to operationalize these values and new solutions. Finally, diversity and inclusion, as configured by engineering logic, positions firms not as "ethics owners" but as ethics allocators; while these companies claim expertise on AI ethics, the responsibility of defining who diversity and inclusion are meant to protect and where it is relevant is pushed downstream to their customers.
A Unified Pre-training Framework for Conversational AI

In this work, we explore the application of PLATO-2 on various dialogue systems, including open-domain conversation, knowledge grounded dialogue, and task-oriented conversation. PLATO-2 is initially designed as an open-domain chatbot, trained via two-stage curriculum learning. In the first stage, a coarse-grained response generation model is learned to fit the simplified one-to-one mapping relationship. This model is applied to the task-oriented conversation, given that the semantic mappings tend to be deterministic in task completion. In the second stage, another fine-grained generation model and an evaluation model are further learned for diverse response generation and coherence estimation, respectively. With superior capability on capturing one-to-many mapping, such models are suitable for the open-domain conversation and knowledge grounded dialogue. For the comprehensive evaluation of PLATO-2, we have participated in multiple tasks of DSTC9, including interactive evaluation of open-domain conversation (Track3-task2), static evaluation of knowledge grounded dialogue (Track3-task1), and end-to-end task-oriented conversation (Track2-task1). PLATO-2 has obtained the 1st place in all three tasks, verifying its effectiveness as a unified framework for various dialogue systems.
On the Sharp Baer–Suzuki Theorem for the 
 
 
 
 $ \pi $
 -Radical: Sporadic Groups

Let $ \pi $ be a proper subset of the set of all primes and $ {|\pi|\geq 2} $ . Denote the smallest prime not in $ \pi $ by $ r $ and let $ m=r $ if $ r=2,3 $ , and $ m=r-1 $ if $ r\geq 5 $ . We study the following conjecture: A conjugacy class $ D $ of a finite group $ G $ lies in the $ \pi $ -radical $ \mathrm{O}_{\pi}(G) $ of $ G $ if and only if every $ m $ elements of $ D $ generate a $ \pi $ -subgroup. We confirm this conjecture for the groups $ G $ whose every nonabelian composition factor is isomorphic to a sporadic or alternating group.
Speech Enhancement using Separable Polling Attention and Global Layer Normalization followed with PReLU

Single channel speech enhancement is a challenging task in speech community. Recently, various neural networks based methods have been applied to speech enhancement. Among these models, PHASEN and T-GSA achieve state-of-the-art performances on the publicly opened VoiceBank+DEMAND corpus. Both of the models reach the COVL score of 3.62. PHASEN achieves the highest CSIG score of 4.21 while T-GSA gets the highest PESQ score of 3.06. However, both of these two models are very large. The contradiction between the model performance and the model size is hard to reconcile. In this paper, we introduce three kinds of techniques to shrink the PHASEN model and improve the performance. Firstly, seperable polling attention is proposed to replace the frequency transformation blocks in PHASEN. Secondly, global layer normalization followed with PReLU is used to replace batch normalization followed with ReLU. Finally, BLSTM in PHASEN is replaced with Conv2d operation and the phase stream is simplified. With all these modifications, the size of the PHASEN model is shrunk from 33M parameters to 5M parameters, while the performance on VoiceBank+DEMAND is improved to the CSIG score of 4.30, the PESQ score of 3.07 and the COVL score of 3.73.
Machine learning framework for quantum sampling of highly constrained, continuous optimization problems

In recent years, there is a growing interest in using quantum computers for solving combinatorial optimization problems. In this work, we developed a generic, machine learning-based framework for mapping continuous-space inverse design problems into surrogate quadratic unconstrained binary optimization (QUBO) problems by employing a binary variational autoencoder and a factorization machine. The factorization machine is trained as a low-dimensional, binary surrogate model for the continuous design space and sampled using various QUBO samplers. Using the D-Wave Advantage hybrid sampler and simulated annealing, we demonstrate that by repeated resampling and retraining of the factorization machine, our framework finds designs that exhibit figures of merit exceeding those of its training set. We showcase the framework's performance on two inverse design problems by optimizing (i) thermal emitter topologies for thermophotovoltaic applications and (ii) diffractive meta-gratings for highly efficient beam steering. This technique can be further scaled to leverage future developments in quantum optimization to solve advanced inverse design problems for science and engineering applications.
Optical OFDM Waveform Construction by Combining Real and Imaginary Parts of IDFT

In optical communication systems, orthogonal frequency division multiplexing (OFDM) is widely used to combat inter-symbol interference (ISI) caused by multipath propagation. Optical systems which use intensity modulation and direct detection (IM/DD) can only transmit real valued symbols, but the inverse discrete Fourier transform (IDFT) or its computationally efficient form inverse-fast Fourier transform (IFFT) required for the OFDM waveform construction produces complex values. Hermitian symmetry is often used to obtain real valued symbols. For this purpose, some trigonometric transformations such as discrete cosine transform (DCT) are also used, however these transformations can eliminate the ISI only under certain conditions. In this paper, we propose a completely different method for the construction of OFDM waveform with IFFT to obtain real valued symbols by combining the real and imaginary parts (CRIP) of IFFT output electrically (E-CRIP) or optically (O-CRIP). Analytical analysis and simulation works are presented to show that compared to the Hermitian symmetric system, the proposed method slightly increases the spectral efficiency, eliminates ISI, significantly reduces the amount of needed calculation and does not effect the error performance. In addition, the O-CRIP method is less affected by clipping noise that may occur due to the imperfections of the transmitter front-ends.
Membership Inference Attacks on Deep Regression Models for Neuroimaging

Ensuring the privacy of research participants is vital, even more so in healthcare environments. Deep learning approaches to neuroimaging require large datasets, and this often necessitates sharing data between multiple sites, which is antithetical to the privacy objectives. Federated learning is a commonly proposed solution to this problem. It circumvents the need for data sharing by sharing parameters during the training process. However, we demonstrate that allowing access to parameters may leak private information even if data is never directly shared. In particular, we show that it is possible to infer if a sample was used to train the model given only access to the model prediction (black-box) or access to the model itself (white-box) and some leaked samples from the training data distribution. Such attacks are commonly referred to as Membership Inference attacks. We show realistic Membership Inference attacks on deep learning models trained for 3D neuroimaging tasks in a centralized as well as decentralized setup. We demonstrate feasible attacks on brain age prediction models (deep learning models that predict a person's age from their brain MRI scan). We correctly identified whether an MRI scan was used in model training with a 60% to over 80% success rate depending on model complexity and security assumptions.
Annihilation and recurrence of vortex-antivortex pairs in two-component Bose-Einstein condensates

The annihilation of vortex-antivortex pairs is a key event in two-dimensional Bose-Einstein condensates (BECs). It is known that dissipation or a catalyst vortex is required for the annihilation of the pairs in one-component BECs. We numerically confirmed in two-component BECs that the pairs can be annihilated even without any dissipation or catalyst vortices when the intercomponent interaction is strong. In addition, the pair is recreated alternately in two components under certain conditions, which we call the recurrence.
Effective pair interaction between impurity particles induced by a dense Fermi gas

We study the dynamics of a small number of impurity particles coupled to the ideal Fermi gas in a d-dimensional box. The impurities interact with the fermions via a two-body potential λv(x) where λ is a coupling constant and v(x) for instance a screened Coulomb potential. After taking the large-volume limit at positive Fermi momentum kF we consider the regime of high density of the fermions, that is, kF large compared to one. For coupling constants that scale like λ2 ∼ k F we show that the impurity particles effectively decouple from the fermions but evolve with an attractive pair interaction among each other which is induced by fluctuations in the Fermi gas.
Development of a Fast and Robust Gaze Tracking System for Game Applications

In this study, a novel eye tracking system using a visual camera is developed to extract human's gaze, and it can be used in modern game machines to bring new and innovative interactive experience to players. Central to the components of the system, is a robust iris-center and eye-corner detection algorithm basing on it the gaze is continuously and adaptively extracted. Evaluation tests were applied to nine people to evaluate the accuracy of the system and the results were 2.50 degrees (view angle) in horizontal direction and 3.07 degrees in vertical direction.
Extreme learning machine for the characterization of anomalous diffusion from single trajectories (AnDi-ELM)

The study of the dynamics of natural and artificial systems has provided several examples of deviations from Brownian behavior, generally defined as anomalous diffusion. The investigation of these dynamics can provide a better understanding of diffusing objects and their surrounding media, but a quantitative characterization from individual trajectories is often challenging. Efforts devoted to improving anomalous diffusion detection using classical statistics and machine learning have produced several new methods. Recently, the anomalous diffusion challenge (AnDi, www.andi-challenge.org) was launched to objectively assess these approaches on a common dataset, focusing on three aspects of anomalous diffusion: the inference of the anomalous diffusion exponent; the classification of the diffusion model; and the segmentation of trajectories. In this article, I describe a simple approach to tackle the tasks of the AnDi challenge by combining extreme learning machine and feature engineering (AnDi-ELM). The method reaches satisfactory performance while offering a straightforward implementation and fast training time with limited computing resources, making it a suitable tool for fast preliminary screening of anomalous diffusion.
CrossWalk: Fairness-enhanced Node Representation Learning

The potential for machine learning systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. Much recent work has focused on developing algorithmic tools to assess and mitigate such unfairness. However, there is little work on enhancing fairness in graph algorithms. Here, we develop a simple, effective and general method, CrossWalk, that enhances fairness of various graph algorithms, including influence maximization, link prediction and node classification, applied to node embeddings. CrossWalk is applicable to any random walk based node representation learning algorithm, such as DeepWalk and Node2Vec. The key idea is to bias random walks to cross group boundaries, by upweighting edges which (1) are closer to the groups’ peripheries or (2) connect different groups in the network. CrossWalk pulls nodes that are near groups’ peripheries towards their neighbors from other groups in the embedding space, while preserving the necessary structural properties of the graph. Extensive experiments show the effectiveness of our algorithm to enhance fairness in various graph algorithms, including influence maximization, link prediction and node classification in synthetic and real networks, with only a very small decrease in performance.
GraphFormers: GNN-nested Language Models for Linked Text Representation

Linked text representation is critical for many intelligent web applications, such as online advertisement and recommender systems. Recent breakthroughs on pretrained language models and graph neural networks facilitate the development of corresponding techniques. However, the existing works mainly rely on cascaded model structures: the texts are independently encoded by language models at ﬁrst, and the textual embeddings are further aggregated by graph neural networks. We argue that the neighbourhood information is insufﬁciently utilized within the above process, which restricts the representation quality. In this work, we propose GraphFormers, where graph neural networks are nested alongside each transformer layer of the language models. On top of the above architecture, the linked texts will iteratively extract neighbourhood information for the enhancement of their own semantics. Such an iterative workﬂow gives rise to more effective utilization of neighbourhood information, which contributes to the representation quality. We further introduce an adaptation called unidirectional GraphFormers, which is much more efﬁcient and comparably effective; and we leverage a pretraining strategy called the neighbourhood-aware masked language modeling to enhance the training effect. We perform extensive experiment studies with three large-scale linked text datasets, whose results verify the effectiveness of our proposed methods.
Scaling up Memory-Efficient Formal Verification Tools for Tree Ensembles

To guarantee that machine learning models yield outputs that are not only accurate, but also robust, recent works propose formally verifying robustness properties of machine learning models. To be applicable to realistic safety-critical systems, the used verification algorithms need to manage the combinatorial explosion resulting from vast variations in the input domain, and be able to verify correctness properties derived from versatile and domain-specific requirements. In this paper, we formalise the VoTE algorithm presented earlier as a tool description, and extend the tool set with mechanisms for systematic scalability studies. In particular, we show a) how the separation of property checking from the core verification engine enables verification of versatile requirements, b) the scalability of the tool, both in terms of time taken for verification and use of memory, and c) that the algorithm has attractive properties that lend themselves well for massive parallelisation. We demonstrate the application of the tool in two case studies, namely digit recognition and aircraft collision avoidance, where the first case study serves to assess the resource utilisation of the tool, and the second to assess the ability to verify versatile correctness properties.
A 2.5D Vehicle Odometry Estimation for Vision Applications

This paper proposes a method to estimate the pose of a sensor mounted on a vehicle as the vehicle moves through the world, an important topic for autonomous driving systems. Based on a set of commonly deployed vehicular odometric sensors, with outputs available on automotive communication buses (e.g. CAN or FlexRay), we describe a set of steps to combine a planar odometry based on wheel sensors with a suspension model based on linear suspension sensors. The aim is to determine a more accurate estimate of the camera pose. We outline its usage for applications in both visualisation and computer vision.
Multimedia Edge Computing

In this paper, we investigate the recent studies on multimedia edge computing, from sensing not only traditional visual/audio data but also individuals’ geographical preference and mobility behaviors, to performing distributed machine learning over such data using the joint edge and cloud infrastructure and using evolutional strategies like reinforcement learning and online learning at edge devices to optimize the quality of experience for multimedia services at the last mile proactively. We provide both a retrospective view of recent rapid migration (resp. merge) of cloud multimedia to (resp. and) edge-aware multimedia and insights on the fundamental guidelines for designing multimedia edge computing strategies that target satisfying the changing demand of quality of experience. By showing the recent research studies and industrial solutions, we also provide future directions towards high-quality multimedia services over edge computing.