Miracle-dz/UniFAR-FTUs huggingface dataset:
Quality of Service Guarantees for Physical Unclonable Functions

We consider a secret key agreement problem in which noisy physical unclonable function (PUF) outputs facilitate reliable, secure, and private key agreement with the help of public, noiseless, and authenticated storage. PUF outputs are highly correlated, so transform coding methods have been combined with scalar quantizers to extract uncorrelated bit sequences with reliability guarantees. For PUF circuits with continuous-valued outputs, the models for transformed outputs are made more realistic by replacing the fitted distributions with corresponding truncated ones. The state-of-the-art PUF methods that provide reliability guarantees to each extracted bit are shown to be inadequate to guarantee the same reliability level for all PUF outputs. Thus, a quality of service parameter is introduced to control the percentage of PUF outputs for which a target reliability level can be guaranteed. A public ring oscillator (RO) output dataset is used to illustrate that a truncated Gaussian distribution can be fitted to transformed RO outputs that are inputs to uniform scalar quantizers such that reliability guarantees can be provided for each bit extracted from any PUF device under additive Gaussian noise components by eliminating a small subset of PUF outputs. Furthermore, we conversely show that it is not possible to provide such reliability guarantees without eliminating any PUF output if no extra secrecy and privacy leakage is allowed.
SRG/ART-XC all-sky X-ray survey: catalog of sources detected during the first year

We present a first catalog of sources detected by the Mikhail Pavlinsky ART-XC telescope on board the SRG observatory in the 4–12 keV energy band during its ongoing all-sky survey. The catalog comprises 867 sources detected on the combined map of the first two 6-month scans of the sky (December 2019–December 2020), ART-XC sky surveys 1 and 2, or ARTSS12. The achieved sensitivity to point sources varies between ∼4 × 10−12 erg s−1 cm−2 near the ecliptic plane and ∼8 × 10−13 erg s−1 cm−2 (4–12 keV) near the ecliptic poles, and the typical localization accuracy is ∼15′′. Of the 750 sources of known or suspected origin in the catalog, 56% are extragalactic (mostly active galactic nuclei, AGN; and clusters of galaxies) and the rest are Galactic (mostly cataclysmic variables, CVs; and lowand high-mass X-ray binaries). For 114 sources, ART-XC has detected X-rays for the first time. Although the majority of these (∼80) are expected to be spurious (given the adopted detection threshold), there can be a significant number of newly discovered astrophysical objects. We have started a program of optical follow-up observations of the new and previously unidentified X-ray sources, which has already led to the identification of several AGN and CVs. With the SRG all-sky survey planned to continue for a total of four years, we can expect the ART-XC survey in the 4–12 keV band to significantly surpass previous surveys that were carried out in similar (medium X-ray) energy bands in terms of the combination of angular resolution, sensitivity, and sky coverage.
Efficient Set of Vectors Search

We consider a similarity measure between two sets 𝐴 and 𝐵 of vectors, that balances the average and maximum cosine distance be-tween pairs of vectors, one from set 𝐴 and one from set 𝐵 . As a motivation for this measure, we present lineage tracking in a data-base. To practically realize this measure, we need an approximate search algorithm that given a set of vectors 𝐴 and sets of vectors 𝐵 1 , . , 𝐵 𝑛 , the algorithm quickly locates the set 𝐵 𝑖 that maximizes the similarity measure. For the case where all sets are singleton sets, essentially each is a single vector, there are known eﬃcient approximate search algorithms, e.g., approximated versions of tree search algorithms, locality-sensitive hashing (LSH), vector quantization (VQ) and proximity graph algorithms. In this work, we present approximate search algorithms for the general case. The underlying idea in these algorithms is encoding a set of vectors via a “long” single vector.
Automatic Label Correction for the Accurate Edge Detection of Overlapping Cervical Cells

—Accurate labeling is essential for supervised deep learning methods. In this paper, to accurately segment images of multiple overlapping cervical cells with deep learning models, we propose an automatic label correction algorithm to improve the edge positioning accuracy of overlapping cervical cells in manual labeling. Our algorithm is designed based on gradient guidance, and can automatically correct edge positions for overlapping cervical cells and differences among manual labeling with different annotators. Using the proposed algorithm, we constructed an open cervical cell edge detection dataset (CCEDD) with high labeling accuracy. The experiments on the dataset for training show that our automatic label correction algorithm can improve the accuracy of manual labels and further improve the positioning accuracy of overlapping cells with deep learning models. We have released the dataset and code at https://github.com/nachifur/automatic-label-correction-CCEDD.
A Diffuse Interface Model for Cell Blebbing Including Membrane-Cortex Coupling with Linker Dynamics

The aim of this paper is to develop suitable models for the phenomenon of cell blebbing, which allow for computational predictions of mechanical effects including the crucial interaction of the cell membrane and the actin cortex. For this sake we resort to a two phase-field model that uses diffuse descriptions of both the membrane and the cortex, which in particular allows for a suitable description of the interaction via linker protein densities. Besides the detailed modelling we discuss some energetic aspects of the models and present a numerical scheme, which allows to carry out several computational studies. In those we demonstrate that several effects found in experiments can be reproduced, in particular bleb formation by cortex rupture, which was not possible by previous models without the linker dynamics.
The Detailed Light-curve Evolution of V1674 Her (Nova Her 2021)

We report high-cadence photometry of the ultra-fast (t 2 ∼ 1.2 days) nova V1674 Her during its rise to maximum light (V ∼ 6.3) and the beginning of its subsequent decline. These observations from Evryscope and the Mount Laguna Observatory All-Sky Camera reveal a plateau in the pre-maximum light curve at g ∼ 14 (∼8 mag below peak) that lasted for at least three hours. Similar features (so-called pre-maximum halts) have been observed in some novae near maximum light, but to our knowledge the detection of a plateau in the light curve ∼8 mag below peak is unprecedented.
Multi-Label Generalized Zero Shot Learning for the Classification of Disease in Chest Radiographs

Despite the success of deep neural networks in chest X-ray (CXR) diagnosis, supervised learning only allows the prediction of disease classes that were seen during training. At inference, these networks cannot predict an unseen disease class. Incorporating a new class requires the collection of labeled data, which is not a trivial task, especially for less frequently-occurring diseases. As a result, it becomes inconceivable to build a model that can diagnose all possible disease classes. Here, we propose a multi-label generalized zero shot learning (CXR-ML-GZSL) network that can simultaneously predict multiple seen and unseen diseases in CXR images. Given an input image, CXR-ML-GZSL learns a visual representation guided by the input's corresponding semantics extracted from a rich medical text corpus. Towards this ambitious goal, we propose to map both visual and semantic modalities to a latent feature space using a novel learning objective. The objective ensures that (i) the most relevant labels for the query image are ranked higher than irrelevant labels, (ii) the network learns a visual representation that is aligned with its semantics in the latent feature space, and (iii) the mapped semantics preserve their original inter-class representation. The network is end-to-end trainable and requires no independent pre-training for the offline feature extractor. Experiments on the NIH Chest X-ray dataset show that our network outperforms two strong baselines in terms of recall, precision, f1 score, and area under the receiver operating characteristic curve. Our code is publicly available at: https://github.com/nyuad-cai/CXR-ML-GZSL.git
Noisy-MAPPO: Noisy Credit Assignment for Cooperative Multi-agent Actor-Critic methods

Multi-Agent Reinforcement Learning (MARL) has seen revolutionary breakthroughs with its successful application to multi-agent co-operative tasks such as robot swarms control, autonomous vehicle coordination, and computer games. Recent works have applied the Proximal Policy Optimization (PPO) to the multi-agent tasks, called Multi-agent PPO (MAPPO). However, the MAPPO in current works lacks a theory to guarantee its convergence; and requires artificial agent-specific features, called MAPPO-agent-specific (MAPPO-AS). In addition, the performance of MAPPO-AS is still lower than the finetuned QMIX on the popular benchmark environment StarCraft Multi-agent Challenge (SMAC). In this paper, we firstly theoretically generalize PPO to MAPPO by a approximate lower bound of Trust Region Policy Optimization (TRPO), which guarantees its convergence. Secondly, since the centralized advantage value function in vanilla MAPPO may mislead the learning of some agents, which are not related to these advantage values, called The Policies Overfitting in Multi-agent Cooperation(POMAC) problem. We propose the noisy credit assignment methods (Noisy-MAPPO and Advantage-Noisy-MAPPO) to solve it. The experimental results show that the average performance of Noisy-MAPPO is better than that of finetuned QMIX; Noisy-MAPPO is the first algorithm that achieves more than 90% winning rates in all SMAC scenarios. We open-source the code at https://github.com/hijkzzz/noisy-mappo.
Emotion Recognition for Healthcare Surveillance Systems Using Neural Networks: A Survey

Recognizing the patient's emotions using deep learning techniques has attracted significant attention recently due to technological advancements. Automatically identifying the emotions can help build smart healthcare centers that can detect depression and stress among the patients in order to start the medication early. Using advanced technology to identify emotions is one of the most exciting topics as it defines the relationships between humans and machines. Machines learned how to predict emotions by adopting various methods. In this survey, we present recent research in the field of using neural networks to recognize emotions. We focus on studying emotions' recognition from speech, facial expressions, and audio-visual input and show the different techniques of deploying these algorithms in the real world. These three emotion recognition techniques can be used as a surveillance system in healthcare centers to monitor patients. We conclude the survey with a presentation of the challenges and the related future work to provide an insight into the applications of using emotion recognition.
Using BERT Encoding to Tackle the Mad-lib Attack in SMS Spam Detection

One of the stratagems used to deceive spam filters is to substitute vocables with synonyms or similar words that turn the message unrecognisable by the detection algorithms. In this paper we investigate whether the recent development of language models sensitive to the semantics and context of words, such as Google's BERT, may be useful to overcome this adversarial attack (called"Mad-lib"as per the word substitution game). Using a dataset of 5572 SMS spam messages, we first established a baseline of detection performance using widely known document representation models (BoW and TFIDF) and the novel BERT model, coupled with a variety of classification algorithms (Decision Tree, kNN, SVM, Logistic Regression, Naive Bayes, Multilayer Perceptron). Then, we built a thesaurus of the vocabulary contained in these messages, and set up a Mad-lib attack experiment in which we modified each message of a held out subset of data (not used in the baseline experiment) with different rates of substitution of original words with synonyms from the thesaurus. Lastly, we evaluated the detection performance of the three representation models (BoW, TFIDF and BERT) coupled with the best classifier from the baseline experiment (SVM). We found that the classic models achieved a 94% Balanced Accuracy (BA) in the original dataset, whereas the BERT model obtained 96%. On the other hand, the Mad-lib attack experiment showed that BERT encodings manage to maintain a similar BA performance of 96% with an average substitution rate of 1.82 words per message, and 95% with 3.34 words substituted per message. In contrast, the BA performance of the BoW and TFIDF encoders dropped to chance. These results hint at the potential advantage of BERT models to combat these type of ingenious attacks, offsetting to some extent for the inappropriate use of semantic relationships in language.
An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions

Since 2019, most ad exchanges and sell-side platforms (SSPs), in the online advertising industry, shifted from second to first price auctions. Due to the fundamental difference between these auctions, demand-side platforms (DSPs) have had to update their bidding strategies to avoid bidding unnecessarily high and hence overpaying. Bid shading was proposed to adjust the bid price intended for second-price auctions, in order to balance cost and winning probability in a first-price auction setup. In this study, we introduce a novel deep distribution network for optimal bidding in both open (non-censored) and closed (censored) online first-price auctions. Offline and online A/B testing results show that our algorithm outperforms previous state-of-art algorithms in terms of both surplus and effective cost per action (eCPX) metrics. Furthermore, the algorithm is optimized in run-time and has been deployed into VerizonMedia DSP as production algorithm, serving hundreds of billions of bid requests per day. Online A/B test shows that advertiser's ROI are improved by +2.4%, +2.4%, and +8.6% for impression based (CPM), click based (CPC), and conversion based (CPA) campaigns respectively.
Large-eddy simulations of marine boundary-layer clouds associated with cold air outbreaks during the ACTIVATE campaign– part 1: Case setup and sensitivities to large-scale forcings

Large-eddy simulation (LES) is able to capture key boundary-layer (BL) turbulence and cloud processes. Yet, large-scale forcing and surface turbulent fluxes of sensible and latent heat are often poorly prescribed for LES simulations. We derive these quantities from measurements and reanalysis obtained for two cold air outbreak (CAO) events during Phase I of the Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) in February-March 2020. We study the two contrasting CAO cases by performing LES and test the sensitivity of BL structure and clouds to large-scale forcings and turbulent heat fluxes. Profiles of atmospheric state and large-scale divergence and surface turbulent heat fluxes obtained from the reanalysis data ERA5 agree reasonablywell with those derived fromACTIVATE field measurements for both cases at the sampling time and location. Therefore, we adopt the time evolving heat fluxes, wind and advective tendencies profiles from ERA5 reanalysis data to drive the LES.We find that large-scale thermodynamic advective tendencies and wind relaxations are important for the LES to capture the evolving observed BL meteorological states characterized by the hourly ERA5 reanalysis data and validated by the observations. We show that the divergence (or vertical velocity) is important in regulating the BL growth driven by surface heat fluxes in LES simulations. The evolution of liquid water path is largely affected by the evolution of surface heat fluxes. The liquid water path simulated in LES agrees reasonably well with the ACTIVATE measurements. This study paves the path to investigate aerosol-cloud-meteorology interactions using LES informed and evaluated by ACTIVATE field measurements.
Heegner cycles in Griffiths groups of Kuga-Sato varieties

The aim of this article is to prove, using complex Abel–Jacobi maps, that the subgroup generated by Heegner cycles associated with a ﬁxed imaginary quadratic ﬁeld in the Griﬃths group of a Kuga–Sato variety over a modular curve has inﬁnite rank. This generalises a classical result of Chad Schoen for the Kuga–Sato threefold, and complements work of Amnon Besser on complex multiplication cycles over Shimura curves. The proof relies on a formula for the images of Heegner cycles under the complex Abel–Jacobi map given in terms of explicit line integrals of even weight cusp forms on the complex upper half-plane. The latter is deduced from previous joint work of the author with Massimo Bertolini, Henri Darmon, and Kartik Prasanna by exploiting connections with generalised Heegner cycles. As a corollary, it is proved that the Griﬃths group of the product of a Kuga–Sato variety with powers of an elliptic curve with complex multiplication has inﬁnite rank. This recovers results of Ashay Burungale by a diﬀerent and more direct approach.
Asynchronous games on Petri nets and ATL

We define a game on distributed Petri nets, where several players interact with each other, and with an environment. The players, or users, have perfect knowledge of the current state, and pursue a common goal. Such goal is expressed by Alternating-time Temporal Logic (ATL). The users have a winning strategy if they can cooperate to reach their goal, no matter how the environment behaves. We show that such a game can be translated into a game on concurrent game structures (introduced in order to give a semantics to ATL). We compare our game with the game on concurrent game structures and discuss the differences between the two approaches. Finally, we show that, when we consider memoryless strategies and a fragment of ATL, we can construct a concurrent game structure from the Petri net, such that an ATL formula is verified on the net if, and only if, it is verified on the game structure.
OSSOS Finds an Exponential Cutoff in the Size Distribution of the Cold Classical Kuiper Belt

The cold main classical Kuiper Belt consists of the nonresonant small solar system bodies with low orbital inclinations and orbital semimajor axes between 42.4 and 47.7 au. These objects likely formed in situ, and the population has experienced minimal collisional modification since formation. Using the Outer Solar System Origins Survey ensemble sample and characterization, combined with constraints from deeper surveys and supported by evidence from the Minor Planet Center catalog and the Deep Ecliptic Survey, we determine the absolute magnitude H r distribution of the cold classical belt from H r ≃ 5 to 12 (roughly diameters of 400–20 km). We conclude that the cold population’s H r distribution exhibits an exponential cutoff at large sizes. Exponential cutoffs at large sizes are not a natural outcome of pairwise particle accretion, but exponentially tapered power-law size distributions are a feature of numerical simulations of planetesimal formation via a streaming instability. Our observation of an exponential cutoff agrees with previous observational inferences that no large objects (D ≳ 400 km) exist in the cold population. We note that the asymptotic slope of the H r distribution is consistent with α ∼ 0.4. This asymptotic slope is also found in streaming instability modeling of planetesimal formation and is thus not necessarily associated with achieving collisional equilibrium. Studies of the trans-Neptunian region are providing the parameters that will enable future streaming-instability studies to determine the initial conditions of planetesimal formation in the ≈45 au region of the Sun’s protoplanetary disk.
Approximate trace reconstruction of random strings from a constant number of traces

In the trace reconstruction problem, the goal is to reconstruct an unknown string $x$ of length $n$ from multiple traces obtained by passing $x$ through the deletion channel. In the relaxed problem of $approximate$ trace reconstruction, the goal is to reconstruct an approximation $\widehat{x}$ of $x$ which is close (within $\epsilon n$) to $x$ in edit distance. We show that for most strings $x$, this is possible with high probability using only a constant number of traces. Crucially, this constant does not grow with $n$, and only depends on the deletion probability and $\epsilon$.
k-Gauss-Bonnet inflation

We propose a novel k-Gauss-Bonnet inflationary model, in which a kinetic term of scalar field is allowed to non-minimally couple to the Gauss-Bonnet topological invariant in the absence of a potential of scalar field. As a result, this model is shown to admit an isotropic power-law inflation provided that the scalar field is phantom. Furthermore, stability analysis based on the dynamical system method is performed to indicate that this inflation solution is indeed stable and attractive. More interestingly, a gradient instability in tensor perturbations is shown to disappear in this model.
PyMatching: A Python Package for Decoding Quantum Codes with Minimum-Weight Perfect Matching

This article introduces PyMatching, a fast open-source Python package for decoding quantum error-correcting codes with the minimum-weight perfect matching (MWPM) algorithm. PyMatching includes the standard MWPM decoder as well as a variant, which we call local matching, that restricts each syndrome defect to be matched to another defect within a local neighborhood. The decoding performance of local matching is almost identical to that of the standard MWPM decoder in practice, while reducing the computational complexity. We benchmark the performance of PyMatching, showing that local matching is several orders of magnitude faster than implementations of the full MWPM algorithm using NetworkX or Blossom V for problem sizes typically considered in error correction simulations. PyMatching and its dependencies are open-source, and it can be used to decode any quantum code for which syndrome defects come in pairs using a simple Python interface. PyMatching supports the use of weighted edges, hook errors, boundaries and measurement errors, enabling fast decoding, and simulation of fault-tolerant quantum computing.
Game Semantics of Martin-Löf Type Theory

This work presents game semantics of Martin-Löf type theory (MLTT) equipped with the One, the Zero, the N, Pi, Sigma and Id types. Game semantics interprets a wide range of logic and computation, even the polymorphic $\lambda$ -calculus; however, it has remained a well-known challenge in the past 25 years to achieve game semantics of dependent type theories such as MLTT, and past attempts lack directness or generality. For instance, the approach taken by Abramsky et al. interprets Sigma types indirectly by formal lists, not by games, making an interpretation of universes hopeless, and it is limited to a very specific class of dependent types. The difficulty of this challenge comes from a conflict between the extensionality of dependent types and the intensionality of game semantics. We overcome the challenge by inventing a novel variant of games, while we keep strategies unchanged, in such a way that this variant inherits the strong points of conventional game semantics. Also, our method enables an interpretation of subtyping on dependent types for the first time as game semantics. We finally give a new, game-semantic proof of the independence of Markov’s principle from MLTT. This proof illustrates an advantage of our intensional model over extensional ones such as Hyland’s effective topos.