Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Signal Processing for Common Randomness Generation over MIMO Gaussian Channels with Applications in Identification

Common randomness (CR), as a resource, is not commonly used in existing practical communication systems. In the common randomness framework, both sender and receiver aim to generate a common random variable observable to both, ideally with low error probability. The knowledge of this CR allows us to implement correlated random protocols that can lead to faster and more efficient algorithms. Previous work on CR focused on Single-Input Single-Output (SISO) channels with finite input and output alphabets. As far as we know, no results on continuous and Multiple-Input Multiple-Output (MIMO) channels have yet been established. We characterize CR over Gaussian channels for their practical relevance in many communication situations. We first derive the CR capacity for Gaussian SISO channels. This result is then used for the derivation of the CR capacity in the MIMO case. CR plays a key role in the identification scheme. In contrast to the classical transmission, the decoder is now interested in knowing \emph{whether} a specific message was sent or not, rather than knowing \emph{what} the received message is. In many new applications such as several machine-to-machine and human-to-machine systems and the tactile internet, the identification, also called the post-Shannon scheme, is more efficient than the classical transmission. It has been proved that through CR generation, the post-Shannon communication task allows us to achieve an enormous performance gain that we can take advantage of in several applications. We consider a correlation-assisted secure identification scheme and develop a lower bound on the corresponding secure identification capacity.
The Strongish Planted Clique Hypothesis and Its Consequences

We formulate a new hardness assumption, the Strongish Planted Clique Hypothesis (SPCH), which postulates that any algorithm for planted clique must run in time $n^{\Omega(\log{n})}$ (so that the state-of-the-art running time of $n^{O(\log n)}$ is optimal up to a constant in the exponent). We provide two sets of applications of the new hypothesis. First, we show that SPCH implies (nearly) tight inapproximability results for the following well-studied problems in terms of the parameter $k$: Densest $k$-Subgraph, Smallest $k$-Edge Subgraph, Densest $k$-Subhypergraph, Steiner $k$-Forest, and Directed Steiner Network with $k$ terminal pairs. For example, we show, under SPCH, that no polynomial time algorithm achieves $o(k)$-approximation for Densest $k$-Subgraph. This inapproximability ratio improves upon the previous best $k^{o(1)}$ factor from (Chalermsook et al., FOCS 2017). Furthermore, our lower bounds hold even against fixed-parameter tractable algorithms with parameter $k$. Our second application focuses on the complexity of graph pattern detection. For both induced and non-induced graph pattern detection, we prove hardness results under SPCH, which improves the running time lower bounds obtained by (Dalirrooyfard et al., STOC 2019) under the Exponential Time Hypothesis.
Detecting Social Media Manipulation in Low-Resource Languages

Social media have been deliberately used for malicious purposes, including political manipulation and disinformation. Most research focuses on high-resource languages. However, malicious actors share content across countries and languages, including low-resource ones. Here, we investigate whether and to what extent malicious actors can be detected in low-resource language settings. We discovered that a high number of accounts posting in Tagalog were suspended as part of Twitter’s crackdown on interference operations after the 2016 US Presidential election. By combining text embedding and transfer learning, our framework can detect, with promising accuracy, malicious users posting in Tagalog without any prior knowledge or training on malicious content in that language. We first learn an embedding model for each language, namely a high-resource language (English) and a low-resource one (Tagalog), independently. Then, we learn a mapping between the two latent spaces to transfer the detection model. We demonstrate that the proposed approach significantly outperforms state-of-the-art models and yields marked advantages in settings with very limited training data—the norm when dealing with detecting malicious activity in online platforms.
An Instance-Based Algorithm for Deciding the Bias of a Coin

Let $q \in (0,1)$ and $\delta \in (0,1)$ be real numbers, and let $C$ be a coin that comes up heads with an unknown probability $p$, such that $p \neq q$. We present an algorithm that, on input $C$, $q$, and $\delta$, decides, with probability at least $1-\delta$, whether $p q$. The expected number of coin flips made by this algorithm is $O \left( \frac{\log\log(1/\varepsilon) + \log(1/\delta)}{\varepsilon^2} \right)$, where $\varepsilon = |p-q|$.
Support of Closed Walks and Second Eigenvalue Multiplicity of the Normalized Adjacency Matrix

We show that the multiplicity of the second normalized adjacency matrix eigenvalue of any connected graph of maximum degree $\Delta$ is bounded by $O(n \Delta^{7/5}/\log^{1/5-o(1)}n)$ for any $\Delta$, and by $O(n\log^{1/2}d/\log^{1/4-o(1)}n)$ for simple $d$-regular graphs when $d\ge \log^{1/4}n$. In fact, the same bounds hold for the number of eigenvalues in any interval of width $\lambda_2/\log_\Delta^{1-o(1)}n$ containing the second eigenvalue $\lambda_2$. The main ingredient in the proof is a polynomial (in $k$) lower bound on the typical support of a closed random walk of length $2k$ in any connected graph, which in turn relies on new lower bounds for the entries of the Perron eigenvector of submatrices of the normalized adjacency matrix.
Convergence of a finite element method on a Bakhvalov‐type mesh for a singularly perturbed convection–diffusion equation in 2D

A finite element method of any order is applied on a Bakhvalov‐type mesh to solve a singularly perturbed convection–diffusion equation in 2D, whose solution exhibits exponential boundary layers. A uniform convergence of (almost) optimal order is proved by means of a carefully defined interpolant.
Ordinally Consensus Subset over Multiple Metrics

In this paper, we propose to study the following maximum ordinal consensus problem: Suppose we are given a metric system (M, X), which contains k metrics M = {\rho_1,. , \rho_k} defined on the same point set X. We aim to find a maximum subset X' of X such that all metrics in M are "consistent" when restricted on the subset X'. In particular, our definition of consistency will rely only on the ordering between pairwise distances, and thus we call a "consistent" subset an ordinal consensus of X w.r. t. M. We will introduce two concepts of "consistency" in the ordinal sense: a strong one and a weak one. Specifically, a subset X' is strongly consistent means that the ordering of their pairwise distances is the same under each of the input metric \rho_i from M. The weak consistency, on the other hand, relaxes this exact ordering condition, and intuitively allows us to take the plurality of ordering relation between two pairwise distances. We show in this paper that the maximum consensus problems over both the strong and the weak consistency notions are NP-complete, even when there are only 2 or 3 simple metrics, such as line metrics and ultrametrics. We also develop constant-factor approximation algorithms for the dual version, the minimum inconsistent subset problem of a metric system (M, P), - note that optimizing these two dual problems are equivalent.
On Performance of Sparse Fast Fourier Transform Algorithms Using the Aliasing Filter.

Computing the Sparse Fast Fourier Transform(sFFT) of a K-sparse signal of size N has emerged as a critical topic for a long time. There are mainly two stages in the sFFT: frequency bucketization and spectrum reconstruction. Frequency bucketization is equivalent to hashing the frequency coefficients into B buckets through one of these filters: Dirichlet kernel filter, flat filter, aliasing filter, etc. The spectrum reconstruction is equivalent to identifying frequencies that are isolated in their buckets. More than forty different sFFT algorithms compute Discrete Fourier Transform(DFT) by their unique methods so far. In order to use them properly, the urgent topic of great concern is how to analyze and evaluate the performance of these algorithms in theory and practice. The paper mainly discusses the sFFT Algorithms using the aliasing filter. In the first part, the paper introduces the technique of three frameworks: the one-shot framework based on the compressed sensing(CS) solver, the peeling framework based on the bipartite graph and the iterative framework based on the binary tree search. Then, we get the conclusion of the performance of six corresponding algorithms: sFFT-DT1.0, sFFT-DT2.0, sFFT-DT3.0, FFAST, R-FFAST and DSFFT algorithm in theory. In the second part, we make two categories of experiments for computing the signals of different SNR, different N, different K by a standard testing platform and record the run time, percentage of the signal sampled and L0, L1, L2 error both in the exactly sparse case and general sparse case. The result of experiments satisfies the inferences obtained in theory.
Learning GPLVM with arbitrary kernels using the unscented transformation

Gaussian Process Latent Variable Model (GPLVM) is a flexible framework to handle uncertain inputs in Gaussian Processes (GPs) and incorporate GPs as components of larger graphical models. Nonetheless, the standard GPLVM variational inference approach is tractable only for a narrow family of kernel functions. The most popular implementations of GPLVM circumvent this limitation using quadrature methods, which may become a computational bottleneck even for relatively low dimensions. For instance, the widely employed Gauss-Hermite quadrature has exponential complexity on the number of dimensions. In this work, we propose using the unscented transformation instead. Overall, this method presents comparable, if not better, performance than offthe-shelf solutions to GPLVM and its computational complexity scales only linearly on dimension. In contrast to Monte Carlo methods, our approach is deterministic and works well with quasi-Newton methods, such as the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. We illustrate the applicability of our method with experiments on dimensionality reduction and multistep-ahead prediction with uncertainty propagation.
Wayback Machine: Capturing the evolutionary behaviour of the bug dependency graph in open-source software systems

The issue tracking system (ITS) is a rich data source for data-driven decision making. Different characteristics of bugs, such as severity, priority, and time to fix may be misleading. Similarly, these values may be subjective, e.g., severity and priority values are assigned based on the intuition of a user or a developer rather than a structured and well-defined procedure. Hence, we explore the dependency graph of the bugs and its complexity as an alternative to show the actual project evolution. In this work, we aim to overcome uncertainty in decision making by tracking the complexity of the bug dependency graph (BDG) to come up with a bug resolution policy that balances different considerations such as bug dependency, severity, and fixing time for the bug triaging. We model the evolution of BDG by mining issue tracking systems of three open-source projects for the past ten years. We first design a Wayback machine to examine the current bug fixing strategies, and then we define eight rule-based bug prioritization policies and compare their performances using ten distinct internal and external indices. We simulate the behavior of the ITS and trace back the effect of each policy across the history of the ITS. Considering the strategies related to the topology of the BDG, we are able to address bug prioritization problems under different scenarios. Our findings show that the network-related approaches are superior to the actual prioritization task in most cases. Among the selected open-source projects, LibreOffice triagers are the only ones who disregard the importance of the BDG, and that project is faced with a very dense BDG. Although we found that there is no single remedy that satisfies all the expectations of developers, the graph-related policies are found to be robust and deemed to be more suitable for bug triaging.
Electrically-Reconfigurable Passive and Active Circuits in a Single Plasmonic Architecture

Guided-wave plasmonic circuits are promising platforms for sensing, interconnection, and quantum applications in the sub-diffraction regime. Nonetheless, the loss-confinement trade-off remains a collective bottleneck for plasmonic-enhanced optical processes. Here, we report a unique plasmonic waveguide that can alleviate such trade-off and improve the efficiencies of plasmonic-based emission, light-matter-interaction, and detection simultaneously. Through different bias configurations, record experimental attributes such as normalized Purcell factor approaching 10^4, 10-dB amplitude modulation with <1 dB insertion loss and fJ-level switching energy, and photodetection sensitivity and internal quantum efficiency of -54 dBm and 6.4 % respectively can be realized within the same amorphous-based plasmonic structure. The ability to support multiple optoelectronic phenomena while providing performance gains over existing plasmonic and dielectric counterparts offers a clear path towards reconfigurable, monolithic plasmonic circuits.
Using GANs to Synthesise Minimum Training Data for Deepfake Generation

There are many applications of Generative Adversarial Networks (GANs) in fields like computer vision, natural language processing, speech synthesis, and more. Undoubtedly the most notable results have been in the area of image synthesis and in particular in the generation of deepfake videos. While deepfakes have received much negative media coverage, they can be a useful technology in applications like entertainment, customer relations, or even assistive care. One problem with generating deepfakes is the requirement for a lot of image training data of the subject which is not an issue if the subject is a celebrity for whom many images already exist. If there are only a small number of training images then the quality of the deepfake will be poor. Some media reports have indicated that a good deepfake can be produced with as few as 500 images but in practice, quality deepfakes require many thousands of images, one of the reasons why deepfakes of celebrities and politicians have become so popular. In this study, we exploit the property of a GAN to produce images of an individual with variable facial expressions which we then use to generate a deepfake. We observe that with such variability in facial expressions of synthetic GAN-generated training images and a reduced quantity of them, we can produce a near-realistic deepfake videos.
Challenges of Applying Deep Reinforcement Learning in Dynamic Dispatching

Dynamic dispatching aims to smartly allocate the right resources to the right place at the right time. Dynamic dispatching is one of the core problems for operations optimization in the mining industry. Theoretically, deep reinforcement learning (RL) should be a natural fit to solve this problem. However, the industry relies on heuristics or even human intuitions, which are often short-sighted and sub-optimal solutions. In this paper, we review the main challenges in using deep RL to address the dynamic dispatching problem in the mining industry.
The Photospheric Temperatures of Betelgeuse during the Great Dimming of 2019/2020: No New Dust Required

The processes that shape the extended atmospheres of red supergiants, heat their chromospheres, create molecular reservoirs, drive mass loss, and create dust remain poorly understood. Betelgeuse’s V-band “Great Dimming” event of 2019 September/2020 February and its subsequent rapid brightening provides a rare opportunity to study these phenomena. Two different explanations have emerged to explain the dimming; new dust appeared in our line of sight attenuating the photospheric light, or a large portion of the photosphere had cooled. Here we present five years of Wing three-filter (A, B, and C band) TiO and near-IR photometry obtained at the Wasatonic Observatory. These reveal that parts of the photosphere had a mean effective temperature (Teff) significantly lower than that found by Levesque & Massey. Synthetic photometry from MARCS-model photospheres and spectra reveal that the V band, TiO index, and C-band photometry, and previously reported 4000–6800 Å spectra can be quantitatively reproduced if there are multiple photospheric components, as hinted at by Very Large Telescope (VLT)-SPHERE images in Montargès et al. If the cooler component has ΔTeff ≥ 250 K cooler than 3650 K, then no new dust is required to explain the available empirical constraints. A coincidence of the dominant short- (∼430 days) and long-period (∼5.8 yr) V-band variations occurred near the time of deep minimum (Guinan et al. ). This is in tandem with the strong correlation of V mag and photospheric radial velocities, recently reported by Dupree et al. (). These suggest that the cooling of a large fraction of the visible star has a dynamic origin related to the photospheric motions, perhaps arising from pulsation or large-scale convective motions.
Galaxy formation with L-GALAXIES: modelling the environmental dependency of galaxy evolution and comparing with observations

We present a variation of the recently updated Munich semi-analytical galaxy formation model, L-Galaxies, with a new gas stripping method. Extending earlier work, we directly measure the local environmental properties of galaxies to formulate a more accurate treatment of ram-pressure stripping for all galaxies. We fully re-calibrate the modified L-Galaxies model using a Markov Chain Monte Carlo (MCMC) method with the stellar mass function and quenched fraction of galaxies at $0\leq z\leq2$ as constraints. Due to this re-calibration, global galaxy population relations, including the stellar mass function, quenched fractions versus galaxy mass and HI mass function are all largely unchanged and remain consistent with observations. By comparing to data on galaxy properties in different environments from the SDSS and HSC surveys, we demonstrate that our modified model improves the agreement with the quenched fractions and star formation rates of galaxies as a function of environment, stellar mass, and redshift. Overall, in the vicinity of haloes with total mass $10^{12}$ to $10^{15}\rm M_{\odot}$ at $z=0$, our new model produces higher quenched fractions and stronger environmental dependencies, better recovering observed trends with halocentric distance up to several virial radii. By analysing the actual amount of gas stripped from galaxies in our model, we show that those in the vicinity of massive haloes lose a large fraction of their hot halo gas before they become satellites. We demonstrate that this affects galaxy quenching both within and beyond the halo boundary. This is likely to influence the correlations between galaxies up to tens of megaparsecs.
Mass segregation and sequential star formation in NGC 2264 revealed by Herschel

Context. The mass segregation of stellar clusters could be primordial rather than dynamical. Despite the abundance of studies of mass segregation for stellar clusters, those for stellar progenitors are still scarce, so the question concerning the origin and evolution of mass segregation is still open. Aims. Our goal is to characterize the structure of the NGC 2264 molecular cloud and compare the populations of clumps and young stellar objects (YSOs) in this region whose rich YSO population has shown evidence of sequential star formation. Methods. We separated the Herschel column density map of NGC 2264 into three subregions and compared their cloud power spectra using a multiscale segmentation technique. We extracted compact cloud fragments from the column density image, measured their basic properties, and studied their spatial and mass distributions. Results. In the whole NGC 2264 cloud, we identified a population of 256 clumps with typical sizes of ~0.1 pc and masses ranging from 0.08 M⊙ to 53 M⊙. Although clumps have been detected all over the cloud, most of the massive, bound clumps are concentrated in the central subregion of NGC 2264. The local surface density and the mass segregation ratio indicate a strong degree of mass segregation for the 15 most massive clumps, with a median Σ6 three times that of the whole clumps population and ΛMSR ≃ 8. We show that this cluster of massive clumps is forming within a high-density cloud ridge, which is formed and probably still fed by the high concentration of gas observed on larger scales in the central subregion. The time sequence obtained from the combined study of the clump and YSO populations in NGC 2264 suggests that the star formation started in the northern subregion, that it is now actively developing at the center, and will soon start in the southern subregion. Conclusions. Taken together, the cloud structure and the clump and YSO populations in NGC 2264 argue for a dynamical scenario of star formation. The cloud could first undergo global collapse, driving most clumps to centrally concentrated ridges. After their main accretion phase, some YSOs, and probably the most massive, would stay clustered while others would be dispersed from their birth sites. We propose that the mass segregation observed in some star clusters is inherited from that of clumps, originating from the mass assembly phase of molecular clouds.
Personal-ITY: A Novel YouTube-based Corpus for Personality Prediction in Italian

We present a novel corpus for personality prediction in Italian, containing a larger number of authors and a different genre compared to previously available resources. The corpus is built exploiting Distant Supervision, assigning Myers-Briggs Type Indicator (MBTI) labels to YouTube comments, and can lend itself to a variety of experiments. We report on preliminary experiments on Personal-ITY, which can serve as a baseline for future work, showing that some types are easier to predict than others, and discussing the perks of cross-dataset prediction.
Deep Time Delay Neural Network for Speech Enhancement with Full Data Learning

Recurrent neural networks (RNNs) have shown significant improvements in recent years for speech enhancement. However, the model complexity and inference time cost of RNNs are much higher than deep feed-forward neural networks (DNNs). Therefore, these limit the applications of speech enhancement. This paper proposes a deep time delay neural network (TDNN) for speech enhancement with full data learning. The TDNN has excellent potential for capturing long range temporal contexts, which utilizes a modular and incremental design. Besides, the TDNN preserves the feed-forward structure so that its inference cost is comparable to standard DNN. To make full use of the training data, we propose a full data learning method for speech enhancement. More specifically, we not only use the noisy-to-clean (input-to-target) to train the enhanced model, but also the clean-to-clean and noise-to-silence data. Therefore, all of the training data can be used to train the enhanced model. Our experiments are conducted on TIMIT dataset. Experimental results show that our proposed method could achieve a better performance than DNN and comparable even better performance than BLSTM. Meanwhile, compared with the BLSTM, the proposed method drastically reduce the inference time.