Miracle-dz/UniFAR-FTUs huggingface dataset:
Topological Quantum Gravity of the Ricci Flow.

We present a family of topological quantum gravity theories associated with the geometric theory of the Ricci flow on Riemannian manifolds. First we use BRST quantization to construct a "primitive" topological Lifshitz-type theory for only the spatial metric, with spatial diffeomorphism invariance and no gauge symmetry, associated with Hamilton's Ricci flow: Hamilton's flow equation appears as the localization equation of the primitive theory. Then we extend the primitive theory by gauging foliation-preserving spacetime symmetries. Crucially, all our theories are required to exhibit an ${\cal N}=2$ extended BRST symmetry. First, we gauge spatial diffeomorphisms, and show that this gives us access to the mathematical technique known as the DeTurck trick. Finally, we gauge foliation-preserving time reparametrizations, both with the projectable and nonprojectable lapse function. The path integral of the full theory is localized to the solutions of Ricci-type flow equations, generalizing those of Perelman. The role of Perelman's dilaton is played by the nonprojectable lapse function. Perelman's ${\cal F}$-functional appears as the superpotential of our theory. Since there is no spin-statistics theorem in nonrelativistic quantum field theory, the two supercharges of our gravity theory do not have to be interpreted as BRST charges and, after the continuation to real time, the theory can be studied as a candidate for nonrelativistic quantum gravity with propagating bosonic and fermionic degrees of freedom.
Concatenated Codes for Recovery From Multiple Reads of DNA Sequences

Decoding sequences that stem from multiple transmissions of a codeword over an insertion, deletion, and substitution channel is a critical component of efficient deoxyribonucleic acid (DNA) data storage systems. In this paper, we consider a concatenated coding scheme with an outer low-density parity-check code and either an inner convolutional code or a block code. We propose two new decoding algorithms for inference from multiple received sequences, both combining the inner code and channel to a joint hidden Markov model to infer symbolwise a posteriori probabilities (APPs). The first decoder computes the exact APPs by jointly decoding the received sequences, whereas the second decoder approximates the APPs by combining the results of separately decoded received sequences. Using the proposed algorithms, we evaluate the performance of decoding multiple received sequences by means of achievable information rates and Monte-Carlo simulations. We show significant performance gains compared to a single received sequence.
Down the bot hole: Actionable insights from a one-year analysis of bot activity on Twitter

Social media represent persuasive tools that have been progressively weaponized to affect beliefs, spread manipulative narratives, and sow conflicts along divergent factions. Software-controlled accounts (i.e., bots) are one of the main actors associated with manipulation campaigns, especially in a political context. Uncovering the strategies behind bots’ activities is of paramount importance to detect and curb such campaigns. In this paper, we present a long term (one year) analysis of bots activity on Twitter in the run-up to the 2018 U.S. midterm elections. We identify different classes of accounts based on their nature (human vs. bot) and engagement within the online discussion and we observe that hyperactive bots played a pivotal role in the dissemination of conspiratorial narratives, while dominating the political debate in the year before the election. Our analysis, in advance of the U.S. 2020 presidential election, reveals both alarming findings of human susceptibility to bots and actionable insights that can contribute to curbing coordinated campaigns.
Improving Perceptual Quality by Phone-Fortified Perceptual Loss for Speech Enhancement

Speech enhancement (SE) aims to improve speech quality and intelligibility, which are both related to a smooth transition in speech segments that may carry linguistic information, e.g. phones and syllables. In this study, we took phonetic characteristics into account in the SE training process. Hence, we designed a phone-fortified perceptual (PFP) loss, and the training of our SE model was guided by PFP loss. In PFP loss, phonetic characteristics are extracted by wav2vec, an unsupervised learning model based on the contrastive predictive coding (CPC) criterion. Different from previous deep-feature-based approaches, the proposed approach explicitly uses the phonetic information in the deep feature extraction process to guide the SE model training. To test the proposed approach, we first confirmed that the wav2vec representations carried clear phonetic information using a t-distributed stochastic neighbor embedding (t-SNE) analysis. Next, we observed that the proposed PFP loss was more strongly correlated with the perceptual evaluation metrics than point-wise and signal-level losses, thus achieving higher scores for standardized quality and intelligibility evaluation metrics in the Voice Bank--DEMAND dataset.
Assessing forest windthrow damage using single-date, post-event airborne laser scanning data

One of many possible climate change effects in temperate areas is the increase of frequency and severity of windstorms; thus, fast and cost efficient new methods are needed to evaluate wind-induced damages in forests. We present a method for assessing windstorm damages in forest landscapes based on a two-stage sampling strategy using single-date, post-event airborne laser scanning (ALS) data. ALS data are used for delineating damaged forest stands and for an initial evaluation of the volume of fallen trees. The total volume of fallen trees is then estimated using a two-stage model-assisted approach, where variables from ALS are used as auxiliary information in the difference estimator. In the first stage, a sample of the delineated forest stands is selected, and in the second stage the within-stand damages are estimated by means of line intercept sampling (LIS). The proposed method produces maps of windthrown areas, estimates of forest damages in terms of the total volume of fallen trees, and the uncertainty of the estimates. A case study is presented for a large windstorm that struck the Tuscany Region of Italy the night of the 4th and the 5th of March 2015 and caused extensive damages to trees in both forest and urban areas. The pure field-based estimates from LIS and the ALS-based estimates of stand-level fallen wood were very similar. Our positive results demonstrate the utility of the single-date approach for a fast assessment of windthrow damages in forest stands which is especially useful when pre-event ALS data are not available.
Post-selection inference with HSIC-Lasso

Detecting influential features in complex (non-linear and/or high-dimensional) datasets is key for extracting the relevant information. Most of the popular selection procedures, however, require assumptions on the underlying data - such as distributional ones -, which barely agree with empirical observations. Therefore, feature selection based on nonlinear methods, such as the model-free HSIC-Lasso, is a more relevant approach. In order to ensure valid inference among the chosen features, the selection procedure must be accounted for. In this paper, we propose selective inference with HSIC-Lasso using the framework of truncated Gaussians together with the polyhedral lemma. Based on these theoretical foundations, we develop an algorithm allowing for low computational costs and the treatment of the hyper-parameter selection issue. The relevance of our method is illustrated using artificial and real-world datasets. In particular, our empirical findings emphasise that type-I error control at the considered level can be achieved.
The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Research

Increasingly, modern Artificial Intelligence (AI) research has become more computationally intensive. However, a growing concern is that due to unequal access to computing power, only certain firms and elite universities have advantages in modern AI research. Using a novel dataset of 171394 papers from 57 prestigious computer science conferences, we document that firms, in particular, large technology firms and elite universities have increased participation in major AI conferences since deep learning's unanticipated rise in 2012. The effect is concentrated among elite universities, which are ranked 1-50 in the QS World University Rankings. Further, we find two strategies through which firms increased their presence in AI research: first, they have increased firm-only publications; and second, firms are collaborating primarily with elite universities. Consequently, this increased presence of firms and elite universities in AI research has crowded out mid-tier (QS ranked 201-300) and lower-tier (QS ranked 301-500) universities. To provide causal evidence that deep learning's unanticipated rise resulted in this divergence, we leverage the generalized synthetic control method, a data-driven counterfactual estimator. Using machine learning based text analysis methods, we provide additional evidence that the divergence between these two groups - large firms and non-elite universities - is driven by access to computing power or compute, which we term as the "compute divide". This compute divide between large firms and non-elite universities increases concerns around bias and fairness within AI technology, and presents an obstacle towards "democratizing" AI. These results suggest that a lack of access to specialized equipment such as compute can de-democratize knowledge production.
SD-access: practical experiences in designing and deploying software defined enterprise networks

Enterprise networks, over the years, have become more and more complex trying to keep up with new requirements that challenge traditional solutions. Just to mention one out of many possible examples, technologies such as Virtual LANs (VLANs) struggle to address the scalability and operational requirements introduced by Internet of Things (IoT) use cases. To keep up with these challenges we have identified four main requirements that are common across modern enterprise networks: (i) scalable mobility, (ii) endpoint segmentation, (iii) simplified administration, and (iv) resource optimization. To address these challenges we designed SDA (Software Defined Access), a solution for modern enterprise networks that leverages Software-Defined Networking (SDN) and other state of the art techniques. In this paper we present the design, implementation and evaluation of SDA. Specifically, SDA: (i) leverages a combination of an overlay approach with an event-driven protocol (LISP) to dynamically adapt to traffic and mobility patterns while preserving resources, and (ii) enforces policies to groups of endpoints for scalable segmentation with low operational burden. We present our experience with deploying SDA in two real-life scenarios: an enterprise campus, and a large warehouse with mobile robots. Our evaluation shows that SDA, when compared with traditional enterprise networks, can (i) reduce overall data plane forwarding state up to 70% thanks to a reactive protocol using a centralized routing server, and (ii) reduce by an order of magnitude the handover delays in scenarios of massive mobility with respect to other approaches. Finally, we discuss lessons learned while deploying and operating SDA, and possible optimizations regarding the use of an event-driven protocol and group-based segmentation.
Micromagnetics of thin films in the presence of Dzyaloshinskii-Moriya interaction

In this paper, we study the thin-film limit of the micromagnetic energy functional in the presence of bulk Dzyaloshinskii–Moriya interaction (DMI). Our analysis includes both a stationary [Formula: see text]-convergence result for the micromagnetic energy, as well as the identification of the asymptotic behavior of the associated Landau–Lifshitz–Gilbert equation. In particular, we prove that, in the limiting model, part of the DMI term behaves like the projection of the magnetic moment onto the normal to the film, contributing this way to an increase in the shape anisotropy arising from the magnetostatic self-energy. Finally, we discuss a convergent finite element approach for the approximation of the time-dependent case and use it to numerically compare the original three-dimensional (3D) model with the 2D thin-film limit.
Relationship between the morphological features of adductor muscles and sprinting performance in sprinters

The relationship between the thigh muscle composition and the sprinting performance was inves ・ tigated in llmale adult sp 践 nters （ age ／ 20 ． 8 ± 0 ． 9 yrs ， 100 m sprint time ／ 11 ． 20 ± 0 ， 33 sec ） ． Axial im ・ ages of the thigh muscle were taken by 皿 agnetic reso 皿 ance imaging （ MRI ） at upPer （ 70 ％ ） and mid − dle （ 50 ％ ） position in femur ． From these i 皿 ages ， cross − sectional areas （ CSA ） of the quadriceps femoris ， し he hamstring and the adductor muscles were measured ． The results of the regression ana 正 y − s 置 sshowed sigriificant correlatiens between 100 m sp 血 t time and both CSA of adductor and ham − string muscles at 70 ％ 1 ） osition （ r ＝− 0 ． 72 and r ＝− 0 ． 67 ， respectively ） ． There were no sigriificant cor − relations between 100 m spri 且 t time and CSA of adductor or hamst 血 g muscles at 50 ％ positioI1 ， and neither quadriceps femoris mulicles at 70 nor 50 ％ positions ． These results suggest that greater mus ・ cle ef hamstring and at upper
Large deviations and a phase transition in the Block Spin Potts models.

We introduce and analyze a generalization of the blocks spin Ising (Curie-Weiss) models that were discussed in a number of recent articles. In these block spin models each spin in one of $s$ blocks can take one of a finite number of $q \ge 3$ values, hence the name block spin Potts model. The values a spin can take are called colors. We prove a large deviation principle for the percentage of spins of a certain color in a certain block. These values are represented in an $s \times q$ matrix. We show that for uniform block sizes and appropriately chosen interaction strength there is a phase transition. In some regime the only equilibrium is the uniform distribution of all colors in all blocks, while in other parameter regimes there is one predominant color, and this is the same color with the same frequency for all blocks. Finally, we establish log-Sobolev-type inequalities for the block spin Potts model.
Modeling biomedical breathing signals with convolutional deep probabilistic autoencoders

One of the main problems with biomedical signals is the limited amount of patient-specific data and the significant amount of time needed to record a sufficient number of samples for diagnostic and treatment purposes. We explore the use of Variational Autoencoder (VAE) and Adversarial Autoencoder (AAE) algorithms based on one-dimensional convolutional neural networks in order to build generative models able to capture and represent the variability of a set of unlabeled quasi-periodic signals using as few as 10 parameters. Furthermore, we introduce a modified AAE architecture that allows simultaneous semi-supervised classification and generation of different types of signals. Our study is based on physical breathing signals, i.e. time series describing the position of chest markers, generally used to describe respiratory motion. The time series are discretized into a vector of periods, with each period containing 6 time and position values. These vectors can be transformed back into time series through an additional reconstruction neural network and allow to generate extended signals while simplifying the modeling task. The obtained models can be used to generate realistic breathing realizations from patient or population data and to classify new recordings. We show that by incorporating the labels from around 10-15\% of the dataset during training, the model can be guided to group data according to the patient it belongs to, or based on the presence of different types of breathing irregularities such as baseline shifts. Our specific motivation is to model breathing motion during radiotherapy lung cancer treatments, for which the developed model serves as an efficient tool to robustify plans against breathing uncertainties. However, the same methodology can in principle be applied to any other kind of quasi-periodic biomedical signal, representing a generically applicable tool.
Non-thermal neutrinos created by shock acceleration in successful and failed core-collapse supernova

We present a comprehensive study of neutrino shock acceleration in core-collapse supernova (CCSN). The leading players are heavy leptonic neutrinos, $\nu_{\mu}$ and $\nu_{\tau}$; the former and latter potentially gain the energy up to $\sim 100$ MeV and $\sim 200$ MeV, respectively, through the shock acceleration. Demonstrating the neutrino shock acceleration by Monte Carlo neutrino transport, we make a statement that it commonly occurs in the early post bounce phase ($\lesssim 50$ ms after bounce) for all massive stellar collapse experiencing nuclear bounce and would reoccur in the late phase ($\gtrsim 100$ ms) for failed CCSNe. This opens up a new possibility to detect high energy neutrinos by terrestrial detectors from Galactic CCSNe; hence, we estimate the event counts for Hyper(Super)-Kamiokande, DUNE, and JUNO. We find that the event count with the energy of $\gtrsim 80$ MeV is a few orders of magnitude higher than that of the thermal neutrinos regardless of the detectors, and muon production may also happen in these detectors by $\nu_{\mu}$ with the energy of $\gtrsim 100$ MeV. The neutrino signals provide a precious information on deciphering the inner dynamics of CCSN and placing a constraint on the physics of neutrino oscillation; indeed, the detection of the high energy neutrinos through charged current reaction channels will be a smoking gun evidence of neutrino flavor conversion.
Group-Harmonic and Group-Closeness Maximization - Approximation and Engineering

Centrality measures characterize important nodes in networks. Efficiently computing such nodes has received a lot of attention. When considering the generalization of computing central groups of nodes, challenging optimization problems occur. In this work, we study two such problems, group-harmonic maximization and group-closeness maximization both from a theoretical and from an algorithm engineering perspective. On the theoretical side, we obtain the following results. For group-harmonic maximization, unless $P=NP$, there is no polynomial-time algorithm that achieves an approximation factor better than $1-1/e$ (directed) and $1-1/(4e)$ (undirected), even for unweighted graphs. On the positive side, we show that a greedy algorithm achieves an approximation factor of $\lambda(1-2/e)$ (directed) and $\lambda(1-1/e)/2$ (undirected), where $\lambda$ is the ratio of minimal and maximal edge weights. For group-closeness maximization, the undirected case is $NP$-hard to be approximated to within a factor better than $1-1/(e+1)$ and a constant approximation factor is achieved by a local-search algorithm. For the directed case, however, we show that, for any $\epsilon<1/2$, the problem is $NP$-hard to be approximated within a factor of $4|V|^{-\epsilon}$. From the algorithm engineering perspective, we provide efficient implementations of the above greedy and local search algorithms. In our experimental study we show that, on small instances where an optimum solution can be computed in reasonable time, the quality of both the greedy and the local search algorithms come very close to the optimum. On larger instances, our local search algorithms yield results with superior quality compared to existing greedy and local search solutions, at the cost of additional running time. We thus advocate local search for scenarios where solution quality is of highest concern.
Deep DA for Ordinal Regression of Pain Intensity Estimation Using Weakly-Labeled Videos

Automatic estimation of pain intensity from facial expressions in videos has an immense potential in health care applications. However, domain adaptation (DA) is needed to alleviate the problem of domain shifts that typically occurs between video data captured in source and target do-mains. Given the laborious task of collecting and annotating videos, and the subjective bias due to ambiguity among adjacent intensity levels, weakly-supervised learning (WSL)is gaining attention in such applications. Yet, most state-of-the-art WSL models are typically formulated as regression problems, and do not leverage the ordinal relation between intensity levels, nor the temporal coherence of multiple consecutive frames. This paper introduces a new deep learn-ing model for weakly-supervised DA with ordinal regression(WSDA-OR), where videos in target domain have coarse la-bels provided on a periodic basis. The WSDA-OR model enforces ordinal relationships among the intensity levels as-signed to the target sequences, and associates multiple relevant frames to sequence-level labels (instead of a single frame). In particular, it learns discriminant and domain-invariant feature representations by integrating multiple in-stance learning with deep adversarial DA, where soft Gaussian labels are used to efficiently represent the weak ordinal sequence-level labels from the target domain. The proposed approach was validated on the RECOLA video dataset as fully-labeled source domain, and UNBC-McMaster video data as weakly-labeled target domain. We have also validated WSDA-OR on BIOVID and Fatigue (private) datasets for sequence level estimation. Experimental results indicate that our approach can provide a significant improvement over the state-of-the-art models, allowing to achieve a greater localization accuracy.
Competition between fractional quantum Hall liquid and Wigner solid at small fillings: Role of layer thickness and Landau level mixing

What is the fate of the ground state of a two-dimensional electron system (2DES) at very low Landau level filling factors ($\nu$) where interaction reigns supreme? An ordered array of electrons, the so-called Wigner crystal, has long been believed to be the answer. It was in fact the search for the elusive Wigner crystal that led to the discovery of an unexpected, incompressible liquid state, namely the fractional quantum Hall state at $\nu=1/3$. Understanding the competition between the liquid and solid ground states has since remained an active field of fundamental research. Here we report experimental data for a new two-dimensional system where the electrons are confined to an AlAs quantum well. The exceptionally high quality of the samples and the large electron effective mass allow us to determine the liquid-solid phase diagram for the two-dimensional electrons in a large range of filling factors near $\simeq 1/3$ and $\simeq 1/5$. The data and their comparison with an available theoretical phase diagram reveal the crucial role of Landau level mixing and finite electron layer thickness in determining the prevailing ground states.