Miracle-dz/UniFAR-FTUs huggingface dataset:
Unpaid Platform Complementors and the Network Effect Mirage

Platforms have evolved beyond just being organized as multi-sided markets with complementors selling to users. Complementors are often unpaid, working outside of a price system and driven by heterogeneous sources of motivation — which should affect how they respond to platform growth. Does reliance on network effects and strategies to attract large numbers of complementors remain advisable in such contexts? We test hypotheses related to these issues using data from 85 online multi-player game platforms with unpaid complementors. We find that complementor development responds to platform growth even without sales incentives, but that attracting complementors has a net zero effect on on-going development and fails to stimulate network effects. We discuss conditions under which a strategy of using unpaid crowd complementors remains advantageous.
Thunder: Thumbnail based Fast Lightweight Image Denoising Network

To achieve promising results on removing noise from real-world images, most of existing denoising networks are formulated with complex network structure, making them impractical for deployment. Some attempts focused on reducing the number of filters and feature channels but suffered from large performance loss, and a more practical and lightweight denoising network with fast inference speed is of high demand. To this end, a \textbf{Thu}mb\textbf{n}ail based \textbf{D}\textbf{e}noising Netwo\textbf{r}k dubbed Thunder, is proposed and implemented as a lightweight structure for fast restoration without comprising the denoising capabilities. Specifically, the Thunder model contains two newly-established modules: (1) a wavelet-based Thumbnail Subspace Encoder (TSE) which can leverage sub-bands correlation to provide an approximate thumbnail based on the low-frequent feature; (2) a Subspace Projection based Refine Module (SPR) which can restore the details for thumbnail progressively based on the subspace projection approach. Extensive experiments have been carried out on two real-world denoising benchmarks, demonstrating that the proposed Thunder outperforms the existing lightweight models and achieves competitive performance on PSNR and SSIM when compared with the complex designs.
A model of pseudo-Nambu-Goldstone dark matter from a softly broken SU(2) global symmetry with a U(1) gauge symmetry

A model of the pseudo-Nambu-Goldstone (pNG) dark matter (DM) is proposed. We assume that there is an SU(2)g global symmetry and a U(1)X gauge symmetry in the dark sector, and they are spontaneously broken into a U(1)D global symmetry after a scalar field develops a vacuum expectation value. We add a soft symmetry breaking term that breaks the SU(2)g global symmetry into the U(1)g global symmetry explicitly. Our model predicts a stable complex pNG particle under the U(1)D global symmetry. One of the virtues of the pNG DM models is that the models can explain the current null results in the direct detection experiments. The small momentum transfer suppresses the scattering amplitudes thanks to the low energy behavior of the Nambu-Goldstone boson. In our model, the soft symmetry breaking term is uniquely determined. This is the advantage of our model to some earlier works in which some soft symmetry breaking terms cannot be forbidden but are simply assumed to be absent to avoid the constraints from the direct detection experiments. We calculate the thermal relic abundance of the pNG DM and find that model can explain the measured value of the DM energy density under some constraints from the perturbative unitarity.
An Adaptive Contrastive Learning Model for Spike Sorting

Brain-computer interfaces (BCIs), is ways for electronic devices to communicate directly with the brain. For most medical-type brain-computer interface tasks, the activity of multiple units of neurons or local ﬁeld potentials is sufﬁcient for decoding. But for BCIs used in neuroscience research, it is important to separate out the activity of individual neurons. With the development of large-scale silicon technology and the increasing number of probe channels, artiﬁcially interpreting and labeling spikes is becoming increasingly impractical. In this paper, we propose a novel modeling framework: Adaptive Contrastive Learning Model that learns representations from spikes through contrastive learning based on the maximizing mutual information loss function as a theoretical basis. Based on the fact that data with similar features share the same labels whether they are multi-classiﬁed or binary-classiﬁed. With this theoretical support, we simplify the multi-classiﬁcation problem into multiple binary-classiﬁcation, improving both the accuracy and the runtime efﬁciency. Moreover, we also introduce a series of enhancements for the spikes, while solving the problem that the classiﬁcation effect is affected because of the overlapping spikes.
Sim-To-Real Transfer of Visual Grounding for Human-Aided Ambiguity Resolution

Service robots should be able to interact naturally with non-expert human users, not only to help them in various tasks but also to receive guidance in order to resolve ambiguities that might be present in the instruction. We consider the task of visual grounding, where the agent segments an object from a crowded scene given a natural language description. Modern holistic approaches to visual grounding usually ignore language structure and struggle to cover generic domains, therefore relying heavily on large datasets. Additionally, their transfer performance in RGB-D datasets suffers due to high visual discrepancy between the benchmark and the target domains. Modular approaches marry learning with domain modeling and exploit the compositional nature of language to decouple visual representation from language parsing, but either rely on external parsers or are trained in an end-to-end fashion due to the lack of strong supervision. In this work, we seek to tackle these limitations by introducing a fully decoupled modular framework for compositional visual grounding of entities, attributes, and spatial relations. We exploit rich scene graph annotations generated in a synthetic domain and train each module independently. Our approach is evaluated both in simulation and in two real RGB-D scene datasets. Experimental results show that the decoupled nature of our framework allows for easy integration with domain adaptation approaches for Sim-To-Real visual recognition, offering a data-efficient, robust, and interpretable solution to visual grounding in robotic applications.
GMM-based Codebook Construction and Feedback Encoding in FDD Systems

We propose a precoder codebook construction and feedback encoding scheme which is based on Gaussian mixture models (GMMs). In an offline phase, the base station (BS) first fits a GMM to uplink (UL) training samples. Thereafter, it designs a codebook in an unsupervised manner by exploiting the GMM's clustering capability. We design one codebook entry per GMM component. After offloading the GMM-but not the codebook-to the mobile terminal (MT) in the online phase, the MT utilizes the GMM to determine the best fitting codebook entry. To this end, no channel estimation is necessary at the MT. Instead, the MT's observed signal is used to evaluate how responsible each component of the GMM is for the signal. The feedback consists of the index of the GMM component with the highest responsibility and the BS then employs the corresponding codebook entry. Simulation results show that the proposed codebook design and feedback encoding scheme outperforms conventional Lloyd clustering based codebook design algorithms, especially in configurations with reduced pilot overhead.
Empirical Phase Diagram for Three-layer Neural Networks with Infinite Width

Substantial work indicates that the dynamics of neural networks (NNs) is closely related to their initialization of parameters. Inspired by the phase diagram for two-layer ReLU NNs with infinite width (Luo et al., 2021), we make a step towards drawing a phase diagram for three-layer ReLU NNs with infinite width. First, we derive a normalized gradient flow for three-layer ReLU NNs and obtain two key independent quantities to distinguish different dynamical regimes for common initialization methods. With carefully designed experiments and a large computation cost, for both synthetic datasets and real datasets, we find that the dynamics of each layer also could be divided into a linear regime and a condensed regime, separated by a critical regime. The criteria is the relative change of input weights (the input weight of a hidden neuron consists of the weight from its input layer to the hidden neuron and its bias term) as the width approaches infinity during the training, which tends to $0$, $+\infty$ and $O(1)$, respectively. In addition, we also demonstrate that different layers can lie in different dynamical regimes in a training process within a deep NN. In the condensed regime, we also observe the condensation of weights in isolated orientations with low complexity. Through experiments under three-layer condition, our phase diagram suggests a complicated dynamical regimes consisting of three possible regimes, together with their mixture, for deep NNs and provides a guidance for studying deep NNs in different initialization regimes, which reveals the possibility of completely different dynamics emerging within a deep NN for its different layers.
Windowed Green function method for wave scattering by periodic arrays of 2D obstacles

This paper introduces a novel boundary integral equation (BIE) method for the numerical solution of problems of planewave scattering by periodic line arrays of two‐dimensional penetrable obstacles. Our approach is built upon a direct BIE formulation that leverages the simplicity of the free‐space Green function but in turn entails evaluation of integrals over the unit‐cell boundaries. Such integrals are here treated via the window Green function method. The windowing approximation together with a finite‐rank operator correction—used to properly impose the Rayleigh radiation condition—yield a robust second‐kind BIE that produces superalgebraically convergent solutions throughout the spectrum, including at the challenging Rayleigh–Wood anomalies. The corrected windowed BIE can be discretized by means of off‐the‐shelf Nyström and boundary element methods, and it leads to linear systems suitable for iterative linear algebra solvers as well as standard fast matrix–vector product algorithms. A variety of numerical examples demonstrate the accuracy and robustness of the proposed methodology.
Generative models for reproducible coronary calcium scoring

Abstract. Purpose: Coronary artery calcium (CAC) score, i.e., the amount of CAC quantified in CT, is a strong and independent predictor of coronary heart disease (CHD) events. However, CAC scoring suffers from limited interscan reproducibility, which is mainly due to the clinical definition requiring application of a fixed intensity level threshold for segmentation of calcifications. This limitation is especially pronounced in non-electrocardiogram-synchronized computed tomography (CT) where lesions are more impacted by cardiac motion and partial volume effects. Therefore, we propose a CAC quantification method that does not require a threshold for segmentation of CAC. Approach: Our method utilizes a generative adversarial network (GAN) where a CT with CAC is decomposed into an image without CAC and an image showing only CAC. The method, using a cycle-consistent GAN, was trained using 626 low-dose chest CTs and 514 radiotherapy treatment planning (RTP) CTs. Interscan reproducibility was compared to clinical calcium scoring in RTP CTs of 1662 patients, each having two scans. Results: A lower relative interscan difference in CAC mass was achieved by the proposed method: 47% compared to 89% manual clinical calcium scoring. The intraclass correlation coefficient of Agatston scores was 0.96 for the proposed method compared to 0.91 for automatic clinical calcium scoring. Conclusions: The increased interscan reproducibility achieved by our method may lead to increased reliability of CHD risk categorization and improved accuracy of CHD event prediction.
Stereographic Markov Chain Monte Carlo

High-dimensional distributions, especially those with heavy tails, are notoriously difficult for off-the-shelf MCMC samplers: the combination of unbounded state spaces, diminishing gradient information, and local moves results in empirically observed ``stickiness'' and poor theoretical mixing properties -- lack of geometric ergodicity. In this paper, we introduce a new class of MCMC samplers that map the original high-dimensional problem in Euclidean space onto a sphere and remedy these notorious mixing problems. In particular, we develop random-walk Metropolis type algorithms as well as versions of the Bouncy Particle Sampler that are uniformly ergodic for a large class of light and heavy-tailed distributions and also empirically exhibit rapid convergence in high dimensions. In the best scenario, the proposed samplers can enjoy the ``blessings of dimensionality'' that the convergence is faster in higher dimensions.
Optimal two-qubit gates in recurrence protocols of entanglement purification

We propose and investigate a method to optimize recurrence entanglement purification protocols. The approach is based on a numerical search in the whole set of SU(4) matrices with the aid of a quasi-Newton algorithm. Our method evaluates average concurrences where the probabilistic occurrence of mixed entangled states is also taken into account. We show for certain families of states that optimal protocols are not necessarily achieved by bilaterally applied controlled-NOT gates. As we discover several optimal solutions, the proposed method offers some flexibility in experimental implementations of entanglement purification protocols and interesting perspectives in quantum information processing.
On Bayesian mechanics: a physics of and by beliefs

The aim of this paper is to introduce a field of study that has emerged over the last decade, called Bayesian mechanics. Bayesian mechanics is a probabilistic mechanics, comprising tools that enable us to model systems endowed with a particular partition (i.e. into particles), where the internal states (or the trajectories of internal states) of a particular system encode the parameters of beliefs about external states (or their trajectories). These tools allow us to write down mechanical theories for systems that look as if they are estimating posterior probability distributions over the causes of their sensory states. This provides a formal language for modelling the constraints, forces, potentials and other quantities determining the dynamics of such systems, especially as they entail dynamics on a space of beliefs (i.e. on a statistical manifold). Here, we will review the state of the art in the literature on the free energy principle, distinguishing between three ways in which Bayesian mechanics has been applied to particular systems (i.e. path-tracking, mode-tracking and mode-matching). We go on to examine a duality between the free energy principle and the constrained maximum entropy principle, both of which lie at the heart of Bayesian mechanics, and discuss its implications.
Weighted badly approximable complex vectors and bounded orbits of certain diagonalizable flows

We show an analogue of a theorem of An, Ghosh, Guan, and Ly on weighted badly approximable vectors for totally imaginary number fields. We show that for $G=\mathrm{SL}_2(\mathbb{C})\times\dots\times\mathrm{SL}_2(\mathbb{C})$ and $\Gamma<G$ a lattice subgroup, the points of $G/\Gamma$ with bounded orbits under a one-parameter Ad-semisimple subgroup of $G$ form a hyperplane-absolute-winning set. As an application, we also provide a generalization of a result of Esdahl-Schou and Kristensen about the set of badly approximable complex numbers.
Evaluation of Open-Source Tools for Differential Privacy

Differential privacy (DP) defines privacy protection by promising quantified indistinguishability between individuals who consent to share their privacy-sensitive information and those who do not. DP aims to deliver this promise by including well-crafted elements of random noise in the published data, and thus there is an inherent tradeoff between the degree of privacy protection and the ability to utilize the protected data. Currently, several open-source tools have been proposed for DP provision. To the best of our knowledge, there is no comprehensive study for comparing these open-source tools with respect to their ability to balance DP’s inherent tradeoff as well as the use of system resources. This work proposes an open-source evaluation framework for privacy protection solutions and offers evaluation for OpenDP Smartnoise, Google DP, PyTorch Opacus, Tensorflow Privacy, and Diffprivlib. In addition to studying their ability to balance the above tradeoff, we consider discrete and continuous attributes by quantifying their performance under different data sizes. Our results reveal several patterns that developers should have in mind when selecting tools under different application needs and criteria. This evaluation survey can be the basis for an improved selection of open-source DP tools and quicker adaptation of DP.
Lack of Fluency is Hurting Your Translation Model

Many machine translation models are trained on bilingual corpus, which consist of aligned sentence pairs from two different languages with same semantic. However, there is a qualitative discrepancy between train and test set in bilingual corpus. While the most train sentences are created via automatic techniques such as crawling and sentence-alignment methods, the test sentences are annotated with the consideration of ﬂuency by human. We suppose this discrepancy in training corpus will yield performance drop of translation model. In this work, we deﬁne ﬂuency noise to determine which parts of train sentences cause them to seem unnatural. We show that ﬂuency noise can be detected by simple gradient-based method with pre-trained classiﬁer. By remov-ing ﬂuency noise in train sentences, our ﬁnal model outperforms the baseline on WMT-14 DE → EN and RU → EN. We also show the compatibility with back-translation augmentation, which has been commonly used to improve the ﬂuency of the translation model. At last, the qualitative analysis of ﬂuency noise provides the insight of what points we should focus on.
Partial-input baselines show that NLI models can ignore context, but they don’t.

When strong partial-input baselines reveal artifacts in crowdsourced NLI datasets, the performance of full-input models trained on such datasets is often dismissed as reliance on spurious correlations. We investigate whether state-of-the-art NLI models are capable of overriding default inferences made by a partial-input baseline. We introduce an evaluation set of 600 examples consisting of perturbed premises to examine a RoBERTa model’s sensitivity to edited contexts. Our results indicate that NLI models are still capable of learning to condition on context—a necessary component of inferential reasoning—despite being trained on artifact-ridden datasets.
Asymptotic Analysis of RLS-Based Digital Precoder With Limited PAPR in Massive MIMO

This paper focuses on the performance analysis of a class of limited peak-to-average power ratio (PAPR) precoders for downlink multi-user massive multiple-input multiple-output (MIMO) systems. Contrary to conventional precoding approaches based on simple linear precoders such as maximum ratio transmission (MRT) and regularized zero-forcing (RZF), the precoders in this paper are obtained by solving a convex optimization problem. To be specific, these precoders are designed so that the power of each precoded symbol entry is restricted, and the PAPR at each antenna is tunable. By using the Convex Gaussian Min-max Theorem (CGMT), we analytically characterize the empirical distribution of the precoded vector and the joint empirical distribution between the distortion and the intended symbol vector. This allows us to study the performance of these precoders in terms of per-antenna power, per-user distortion power, signal-to-noise and distortion ratio (SINAD), and bit error probability. We show that for this class of precoders, there is an optimal transmit per-antenna power that maximizes the system performance in terms of SINAD and bit error probability.
Changing Learning Environments Through Changing Faculty

 Faculty members play a significant role in determining the learning and working environments at institutions of higher education; therefore, changes in these environments will occur when faculty members see the need for changes, see what types of changes are necessary, and decide to implement the changes. The National Science Foundation demonstration project “Changing Faculty through Learning Communities” facilitates progress toward needed changes by focusing on four strategic disciplines: development and invitation, mental models, personal vision, and personal commitment. The project has developed four strategies to address these strategic disciplines: present speakers to address relevant research, offer workshops and learning communities to facilitate conversations among faculty, provide support for women in undergraduate research to encourage invitation, and provide travel support for women students to encourage invitation. The paper will discuss experiential knowledge gained to date as well as the effect of this knowledge on the planning and implementation of future endeavors.
On the minimal dimension of the orbits of a $\mathbb R^n$-action

Consider a smooth action of R n on a connected manifold M , not necessarily compact, of dimension m and rank k . Assume that M is not a cylinder. Then there exists an orbit of the action of dimension < ( m + k ) / 2. As a consequence, one shows that if there is a non-zero element of the ring of Pontrjagin classes of M of degree 4 ℓ ≥ 4, then there exists an orbit of the action of dimension ≤ m − ℓ − 1.
It's not easy to relax: liveness in chained BFT protocols

Modern chained Byzantine Fault Tolerant (BFT) protocols leverage a combination of pipelining and leader rotation to maximize both efficiency and fairness. Unfortunately, this approach compromises liveness. We observe that even simple leader failures such as crashes can prevent the system from making progress, both theoretically, and practically. The root cause is simple: these protocols require a sequence of three or four consecutive honest leaders to commit operations. This paper makes two contributions: first, we show that, in the presence of arbitrary failures, consecutive honest leaders are necessary . When nodes fail by omission however, one can do better. As second contribution, we thus propose Siesta, a novel chained BFT protocol that successfully commit blocks that span multiple non-consecutive leaders. Siesta reduces the expected commit latency of Hotstuff by a factor of three under failures, and the worst-case latency by a factor of eight.
The monopole-dimer model on Cartesian products of plane graphs

The monopole-dimer model is a signed variant of the monomer-dimer model which has determinantal structure. We extend the monopole-dimer model for planar graphs introduced by the second author (Math. Phys. Anal. Geom., 2015) to Cartesian products thereof and show that the partition function of this model can be expressed as a determinant of a generalised signed adjacency matrix. We then show that the partition function is independent of the orientations of the planar graphs so long as the orientations are Pfaffian. When these planar graphs are bipartite, we show that the computatation of the partition function becomes especially simple. We then give an explicit product formula for the partition function of three-dimensional grid graphs a la Kasteleyn and Temperley--Fischer, which turns out to be fourth power of a polynomial when all grid lengths are even. Finally, we generalise this product formula to $d$ dimensions, again obtaining an explicit product formula. We conclude with a discussion on asymptotic formulas for the free energy and monopole densities.