Miracle-dz/UniFAR-FTUs huggingface dataset:
Designing Perceptual Puzzles by Differentiating Probabilistic Programs

We design new visual illusions by finding “adversarial examples” for principled models of human perception — specifically, for probabilistic models, which treat vision as Bayesian inference. To perform this search efficiently, we design a differentiable probabilistic programming language, whose API exposes MCMC inference as a first-class differentiable function. We demonstrate our method by automatically creating illusions for three features of human vision: color constancy, size constancy, and face perception.
Real or Virtual: A Video Conferencing Background Manipulation-Detection System

Recently, the popularity and wide use of the last-generation video conferencing technologies created an exponential growth in its market size. Such technology allows participants in diﬀerent geographic regions to have a virtual face-to-face meeting. Additionally, it enables users to employ a virtual background to conceal their own environment due to privacy concerns or to reduce distractions, particularly in professional settings. Nevertheless, in scenarios where the users should not hide their actual locations, they may mislead other participants by claiming their virtual background as a real one. Therefore, it is crucial to develop tools and strategies to detect the authenticity of the considered virtual background. In this paper, we present a detection strategy to distinguish between real and virtual video conferencing user backgrounds. We demonstrate that our detector is robust against two attack scenarios. The ﬁrst scenario con-siders the case where the detector is unaware about the attacks (i.e., the forensically-edited frames are not part of the training set). In the second scenario, we make the detector aware of the adversarial attacks, which we refer to Adversarial Multimedia Forensics (i.e, the forensically-edited frames are included in the training set). Given the lack of publicly available dataset of virtual and real backgrounds for video conferencing, we created our own dataset and made them publicly available [1]. Then, we demonstrate the robustness of our detector against diﬀerent adversarial attacks that the adversary considers. Ultimately, our detector’s performance is signiﬁcant against the CRSPAM1372 [2] features, and post-processing operations such as geometric transformations with diﬀerent quality factors that the attacker may choose. Moreover, our performance results shows that we can perfectly identify a real from a virtual background with an accuracy of 99.80%.
Direct 3D Printing of Soft Fluidic Actuators with Graded Porosity

New additive manufacturing methods are needed to realize more complex soft robots. One example is soft fluidic robotics, which exploits fluidic power and stiffness gradients. Porous structures are an interesting type for this approach, as they are flexible and allow for fluid transport. In this work, the infill foam (InFoam) method is proposed to print structures with graded porosity by liquid rope coiling (LRC). By exploiting LRC, the InFoam method could exploit the repeatable coiling patterns to print structures. To this end, only the characterization of the relationship between nozzle height and coil radius and the extruded length was necessary (at a fixed temperature). Then by adjusting the nozzle height and/or extrusion speed the porosity of the printed structure could be set. The InFoam method was demonstrated by printing porous structures using styrene-ethylene-butylene-styrene (SEBS) with porosities ranging from 46% to 89%. In compression tests, the cubes showed large changes in compression modulus (more than 200 times), density (-89% compared to bulk), and energy dissipation. The InFoam method combined coiling and normal plotting to realize a large range of porosity gradients. This grading was exploited to realize rectangular structures with varying deformation patterns, which included twisting, contraction, and bending. Furthermore, the InFoam method was shown to be capable of programming the behavior of bending actuators by varying the porosity. Both the output force and stroke showed correlations similar to those of the cubes. Thus, the InFoam method can fabricate and program the mechanical behavior of a soft fluidic (porous) actuator by grading porosity.
ISTRBoost: Importance Sampling Transfer Regression using Boosting

Current Instance Transfer Learning (ITL) methodologies use domain adaptation and sub-space transformation to achieve successful transfer learning. However, these methodologies, in their processes, sometimes overfit on the target dataset or suffer from negative transfer if the test dataset has a high variance. Boosting methodologies have been shown to reduce the risk of overfitting by iteratively re-weighing instances with high-residual. However, this balance is usually achieved with parameter optimization, as well as reducing the skewness in weights produced due to the size of the source dataset. While the former can be achieved, the latter is more challenging and can lead to negative transfer. We introduce a simpler and more robust fix to this problem by building upon the popular boosting ITL regression methodology, two-stage TrAdaBoost.R2. Our methodology,~\us{}, is a boosting and random-forest based ensemble methodology that utilizes importance sampling to reduce the skewness due to the source dataset. We show that~\us{}~performs better than competitive transfer learning methodologies $63\%$ of the time. It also displays consistency in its performance over diverse datasets with varying complexities, as opposed to the sporadic results observed for other transfer learning methodologies.
Evaluating Automatic Difficulty Estimation of Logic Formalization Exercises

Teaching logic effectively requires an understanding of the factors which cause logic students to struggle. Formalization exercises, which require the student to produce a formula corresponding to the natural language sentence, are a good candidate for scrutiny since they tap into the students' understanding of various aspects of logic. We correlate the difficulty of formalization exercises predicted by a previously proposed difficulty estimation algorithm with two empirical difficulty measures on the Grade Grinder corpus, which contains student solutions to FOL exercises. We obtain a moderate correlation with both measures, suggesting that the said algorithm indeed taps into important sources of difficulty but leaves a fair amount of variance uncaptured. We conduct an error analysis, closely examining exercises which were misclassified, with the aim of identifying additional sources of difficulty. We identify three additional factors which emerge from the difficulty analysis, namely predicate complexity, pragmatic factors and typicality of the exercises, and discuss the implications of automated difficulty estimation for logic teaching and explainable AI.
Angular Quantization in CFT

The most common quantization scheme in which to study a conformal field theory is radial quantization, wherein a Hilbert space of states is defined on a sphere, whose Hamiltonian when mapped to the plane is the dilatation generator and which boasts a state/operator correspondence. In this paper, we consider an alternative quantization scheme for 2d CFTs in which the plane is foliated by constant-angle slices, as opposed to concentric circles, whose Hamiltonian is the rotation generator. In this angular quantization, there is no state/operator correspondence but instead an “asymptotics/operator correspondence”. A central feature is that the quantization slice ends on two operators, and a regulator must be chosen by excising holes around each operator and imposing suitable boundary conditions such that the holes shrink to the desired local operators. This angular quantization may be viewed as constructing CFTs in Minkowski space, or separately as studying non-conformal (but local) boundary conditions in CFTs. We provide explicit Fock space constructions for various free 2d CFTs. In addition to the motivation of applying angular quantization in string theory situations where traditional radial quantization is insufficient, we comment on its relation to modular Hamiltonian approaches to entanglement entropy.
Pretraining Chinese BERT for Detecting Word Insertion and Deletion Errors

Chinese BERT models achieve remarkable progress in dealing with grammatical errors of word substitution. However, they fail to handle word insertion and deletion because BERT assumes the existence of a word at each position. To address this, we present a simple and effective Chinese pretrained model. The basic idea is to enable the model to determine whether a word exists at a particular position. We achieve this by introducing a special token \texttt{[null]}, the prediction of which stands for the non-existence of a word. In the training stage, we design pretraining tasks such that the model learns to predict \texttt{[null]} and real words jointly given the surrounding context. In the inference stage, the model readily detects whether a word should be inserted or deleted with the standard masked language modeling function. We further create an evaluation dataset to foster research on word insertion and deletion. It includes human-annotated corrections for 7,726 erroneous sentences. Results show that existing Chinese BERT performs poorly on detecting insertion and deletion errors. Our approach significantly improves the F1 scores from 24.1\% to 78.1\% for word insertion and from 26.5\% to 68.5\% for word deletion, respectively.
Beyond Lipschitz: Sharp Generalization and Excess Risk Bounds for Full-Batch GD

We provide sharp path-dependent generalization and excess risk guarantees for the full-batch Gradient Descent (GD) algorithm on smooth losses (possibly non-Lipschitz, possibly nonconvex). At the heart of our analysis is an upper bound on the generalization error, which implies that average output stability and a bounded expected optimization error at termination lead to generalization. This result shows that a small generalization error occurs along the optimization path, and allows us to bypass Lipschitz or sub-Gaussian assumptions on the loss prevalent in previous works. For nonconvex, convex, and strongly convex losses, we show the explicit dependence of the generalization error in terms of the accumulated path-dependent optimization error, terminal optimization error, number of samples, and number of iterations. For nonconvex smooth losses, we prove that full-batch GD efficiently generalizes close to any stationary point at termination, and recovers the generalization error guarantees of stochastic algorithms with fewer assumptions. For smooth convex losses, we show that the generalization error is tighter than existing bounds for SGD (up to one order of error magnitude). Consequently the excess risk matches that of SGD for quadratically less iterations. Lastly, for strongly convex smooth losses, we show that full-batch GD achieves essentially the same excess risk rate as compared with the state of the art on SGD, but with an exponentially smaller number of iterations (logarithmic in the dataset size).
Representation-Aware Experimentation: Group Inequality Analysis for A/B Testing and Alerting

As companies adopt increasingly experimentation-driven cultures, it is crucial to develop methods for understanding any potential unintended consequences of those experiments. We might have speciﬁc questions about those consequences (did a change increase or decrease gender representation equality among content creators? ); we might also wonder whether if we have not yet considered the right question (that is, we don’t know what we don’t know). Hence we address the problem of unintended consequences in experimentation from two perspectives: namely, pre-speciﬁed vs. data-driven selection, of dimensions of interest. For a speciﬁed dimen-sion, we introduce a statistic to measure deviation from equal representation (DER statistic), give its asymptotic distribution, and evaluate ﬁnite-sample performance. We explain how to use this statistic to search across large-scale experimentation systems to alert us to any extreme unintended consequences on group representation. We complement this methodology by discussing a search for heterogeneous treatment eﬀects along a set of dimensions with causal trees, modiﬁed slightly for practicalities in our ecosystem, and used here as a way to dive deeper into experiments ﬂagged by the DER statistic alerts. We introduce a method for simulating data that closely mimics observed data at LinkedIn, and evaluate the performance of DER statistics in simulations. Last, we give a case study from LinkedIn, and show how these methodologies empowered us to discover surprising and important insights about group representation. Code for replication is available in an appendix.
The Diophantine equation $f(x)=g(y)$ for polynomials with simple rational roots

In this paper we consider Diophantine equations of the form f ( x ) = g ( y ) where f has simple rational roots and g has rational coeﬃcients. We give strict conditions for the cases where the equation has inﬁnitely many solutions in rationals with a bounded denominator. We give examples illustrating that the given conditions are necessary. It turns out that such equations with inﬁnitely many solutions are strongly related to Prouhet-Tarry-Escott tuples. In the special, but important case when g has only simple rational roots as well, we can give a simpler statement. Also we provide an application to equal products with terms belonging to blocks of consecutive integers of bounded length. The latter theorem is related to problems and results of Erd˝os and Turk, and of Erd˝os and Graham.
Thompson Sampling for Bandit Learning in Matching Markets

The problem of two-sided matching markets has a wide range of real-world applications and has been extensively studied in the literature. A line of recent works have focused on the problem setting where the preferences of one-side market participants are unknown a priori and are learned by iteratively interacting with the other side of participants. All these works are based on explore-then-commit (ETC) and upper confidence bound (UCB) algorithms, two common strategies in multi-armed bandits (MAB). Thompson sampling (TS) is another popular approach, which attracts lots of attention due to its easier implementation and better empirical performances. In many problems, even when UCB and ETC-type algorithms have already been analyzed, researchers are still trying to study TS for its benefits. However, the convergence analysis of TS is much more challenging and remains open in many problem settings. In this paper, we provide the first regret analysis for TS in the new setting of iterative matching markets. Extensive experiments demonstrate the practical advantages of the TS-type algorithm over the ETC and UCB-type baselines.
Reconstruction of decays to merged photons using end-to-end deep learning with domain continuation in the CMS detector

A novel technique based on machine learning is introduced to reconstruct the decays of highly Lorentz-boosted particles. Using an end-to-end deep learning strategy, the technique bypasses existing rule-based particle reconstruction methods typically used in high energy physics analyses. It uses minimally processed detector data as input and directly outputs particle properties of interest. The new technique is demonstrated for the reconstruction of the invariant mass of particles decaying in the CMS detector. The decay of a hypothetical scalar particle $\mathcal{A}$ into two photons, $\mathcal{A}$ $\to$ $\gamma\gamma$, is chosen as a benchmark decay. Lorentz boosts $\gamma_\mathrm{L}$ = 60-600 are considered, ranging from regimes where both photons are resolved to those where the photons are closely merged as one object. A training method using domain continuation is introduced, enabling the invariant mass reconstruction of unresolved photon pairs in a novel way. The new technique is validated using $\pi^0$ $\to$ $\gamma \gamma$ decays in LHC collision data.
Bias-Variance Decompositions for Margin Losses

We introduce a novel bias-variance decomposition for a range of strictly convex margin losses, including the logistic loss (minimized by the classic LogitBoost algorithm), as well as the squared margin loss and canonical boosting loss. Furthermore, we show that, for all strictly convex margin losses, the expected risk decomposes into the risk of a"central"model and a term quantifying variation in the functional margin with respect to variations in the training data. These decompositions provide a diagnostic tool for practitioners to understand model overfitting/underfitting, and have implications for additive ensemble models -- for example, when our bias-variance decomposition holds, there is a corresponding"ambiguity"decomposition, which can be used to quantify model diversity.
Approach to Predicting News - A Precise Multi-LSTM Network With BERT

Varieties of Democracy (V-Dem) 1 is a new approach to conceptualizing and measuring democracy and politics. It has information for 200 countries and is one of the biggest databases for political science. According to the V-Dem annual democracy report 2019, Taiwan is one of the two countries that got disseminated false information from foreign governments the most. It also shows that the ‘‘made-up news’’ has caused a great deal of confusion among Taiwanese society and serious impacts on global stability. Although there are several applications helping distinguish the false information, we found out that the pre-processing of categorizing the news is still done by human labor. However, unlike the machine, human labor may cause some mistakes and cannot work for a long time. The growing demands on automatic machines in the near decades show that while the machine can do as good as humans or even better, using machines can reduce humans’ burden and cut down cost. Therefore, in this work, we build a predictive model to classify the category of the news. The corpora we used contains 28358 news and 200 news which are scraped from the online newspaper L iberty T imes N et (LTN) 2 website and includes 8 categories: Technology, Entertainment, Fashion, Politics, Sports, International, Finance, and Health. At first, we use B idirectional E ncoder R epresentations from T ransformers (BERT) 3 for word embeddings which transform each Chinese character into a (1,768) vector. Then, we use a L ong S hort-T erm M emory (LSTM) layer to transform word embeddings into sentence embeddings and add another LSTM layer to transform them into document embeddings. Each document embedding is an input for the final predicting model, which contains two Dense layers and one Activation layer. And each document embedding is transformed into 1 vector with 8 real numbers, then the highest one will correspond to the 8 categories, and it is the predicted category of the news. The implementation of our network can be found on GitHub (https://github.com/LanaChen0/Predict_News).
Open or not open: Are conventional radio access networks more secure and trustworthy than Open-RAN?

The Open RAN architecture is a promising and future-oriented architecture. It is intended to open up the radio access network (RAN) and enable more innovation and competition in the market. This will lead to RANs for current 5G networks, but especially for future 6G networks, evolving from the current highly integrated, vendor-specific RAN architecture towards disaggregated architectures with open interfaces that will enable to better tailor RAN solutions to the requirements of 5G and 6G applications. However, the introduction of such an open architecture substantially broadens the attack possibilities when compared to conventional RANs. In the past, this has often led to negative headlines that in summary have associated Open RAN with faulty or inadequate security. In this paper, we analyze what components are involved in an Open RAN deployment, how to assess the current state of security, and what measures need to be taken to ensure secure operation.
Design and Experimental Evaluation of a Bluetooth 5.1 Antenna Array for Angle-of-Arrival Estimation

One the of the applications in the realm of the Internet-of-Things (IoT) is real-time localization of assets in specific application environments where satellite based global positioning is unviable. Numerous approaches for localization relying on wireless sensor mesh systems have been evaluated, but the recent Bluetooth Low Energy (BLE) 5.1 direction finding features based on Angle-of-Arrival (AoA) promise a low-cost solution for this application. In this paper, we present an implementation of a BLE 5.1 based circular antenna array, and perform two experimental evaluations over the quality of the retrieved data sampled from the array. Specifically, we retrieve samples of the phase value of the Constant Tone Extension which enables the direction finding functionalities through calculation of phase differences between antenna pairs. We evaluate the quality of the sampled phase data in an anechoic chamber, and in a real-world environment using a setup composed of four BLE beacons.
Convergence of neural networks to Gaussian mixture distribution

We give a proof that, under relatively mild conditions, fully-connected feed-forward deep random neural networks converge to a Gaussian mixture distribution as only the width of the last hidden layer goes to infinity. We conducted experiments for a simple model which supports our result. Moreover, it gives a detailed description of the convergence, namely, the growth of the last hidden layer gets the distribution closer to the Gaussian mixture, and the other layer successively get the Gaussian mixture closer to the normal distribution.
Mutually Unbiased Measurements, Hadamard Matrices, and Superdense Coding

Mutually unbiased bases (MUBs) are highly symmetric bases on complex Hilbert spaces, and the corresponding rank-1 projective measurements are ubiquitous in quantum information theory. In this work, we study a recently introduced generalisation of MUBs called mutually unbiased measurements (MUMs). These measurements inherit the essential property of complementarity from MUBs, but the Hilbert space dimension is no longer required to match the number of outcomes. This operational complementarity property renders MUMs highly useful for device-independent quantum information processing. It has been shown that MUMs are strictly more general than MUBs. In this work we provide a complete proof of the characterisation of MUMs that are direct sums of MUBs. We then construct new examples of MUMs that are not direct sums of MUBs. A crucial technical tool for this construction is a correspondence with quaternionic Hadamard matrices, which allows us to map known examples of such matrices to MUMs that are not direct sums of MUBs. Furthermore, we show that—in stark contrast with MUBs—the number of MUMs for a fixed outcome number is unbounded. Next, we focus on the use of MUMs in quantum communication. We demonstrate how any pair of MUMs with $d$ outcomes defines a $d$ -dimensional superdense coding protocol. Using MUMs that are not direct sums of MUBs, we disprove a recent conjecture due to Nayak and Yuen on the rigidity of superdense coding, for infinitely many dimensions. The superdense coding protocols arising in the refutation reveal how shared entanglement may be used in a manner heretofore unknown.