Miracle-dz/UniFAR-FTUs huggingface dataset:
Feasibility of oral HIV self-testing in female sex workers in Gaborone, Botswana

Background Oral HIV self-testing (HIVST) may be useful for increasing testing in persons at elevated risk of acquiring HIV. Methods We conducted a pilot study to evaluate the feasibility (defined by uptake) of HIVST among FSW in Gaborone, Botswana. FSW age 18 years and above were recruited through a non-governmental organization serving FSW. FSW with unknown or negative HIV status at screening performed HIVST in the study clinic following brief training. FSW testing HIV-negative were each given two test kits to take home: one kit to perform unassisted HIVST and another to share with others. Feasibility (use) of HIVST (and sharing of test kits with others) was assessed in these women at a study visit four months later. Results Two hundred FSW were screened. Their average age was 34 years (range 18–59), and 115 (58%) were HIV-positive. Eighty-five (42%) tested HIV-negative at entry and were eligible to take part in the HIVST pilot study. All 85 (100%) agreed to take home HIVST kits. Sixty-nine (81%) of these 85 participants had a follow-up visit, 56 (81%) of whom reported performing HIVST at a mean of three and half months after the initial visit. All 56 participants who performed HIVST reported negative HIVST results. Fifty (73%) of the 69 participants who took HIVST kits home shared them with others. Of the 50 women sharing HIVST kits, 25 (50%) shared with their non-client partners, 15 with a family member, 8 with friends, and 3 with a client. One participant did not test herself but shared both her test kits. Most participants 53/56 (95%) found oral HIVST very easy to use whilst 3/56 (5%) felt it was fairly easy. Conclusion Oral HIVST is feasible among FSW in Gaborone, Botswana. The majority of FSW used the HIVST kits themselves and also shared extra HIVST kits with other individuals.
PopNet: a Pop Culture Knowledge Association Network for Supporting Creative Connections

Pop culture is a pervasive and important aspect of communication and self-expression. When people wish to communicate using pop culture references, they need to find connections between their message and the things, people, location and actions of a movie, tv series, or other pop culture domain. However, finding an appropriate match from memory is challenging and search engines are not specific enough to the task. Often domain-specific knowledge graphs provide the structure, specificity and search capabilities that people need. We introduce PopNet - a Pop Culture Knowledge Association Network automatically created from plain text using state-of-the art NLP methods to extract entities and actions from text summaries of movies and tv shows. The interface allows people to browse and search the entries to find connections. We conduct a study showing that this system is accurate and helpful for finding multiple connections between a message and a pop culture domain.
Observation of Emergent Z2 Gauge Invariance in a Superconducting Circuit

Z2 Gauge Invariance in a Superconducting Circuit Zhan Wang,1, 2, ∗ Zi-Yong Ge,1, 2, ∗ Zhongcheng Xiang,1, ∗ Xiaohui Song,1 Rui-Zhen Huang,3 Pengtao Song,1, 2 Xue-Yi Guo,1 Luhong Su,1, 2 Kai Xu,1, 4, 5 Dongning Zheng,1, 2, 4, 5, † and Heng Fan1, 2, 4, 5, ‡ Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100190, China Kavli Institute for Theoretical Sciences, University of Chinese Academy of Sciences, Beijing 100190, China Songshan Lake Materials Laboratory, Dongguan 523808, Guangdong, China CAS Center for Excellence in Topological Quantum Computation, UCAS, Beijing 100190, China
Locally testable codes with constant rate, distance, and locality

A locally testable code (LTC) is an error correcting code that has a property-tester. The tester reads q bits that are randomly chosen, and rejects words with probability proportional to their distance from the code. The parameter q is called the locality of the tester. LTCs were initially studied as important components of probabilistically checkable proofs (PCP), and since then the topic has evolved on its own. High rate LTCs could be useful in practice: before attempting to decode a received word, one can save time by first quickly testing if it is close to the code. An outstanding open question has been whether there exist “c3-LTCs”, namely LTCs with constant rate, constant distance, and constant locality. In this work we construct such codes based on a new two-dimensional complex which we call a left-right Cayley complex. This is essentially a graph which, in addition to vertices and edges, also has squares. Our codes can be viewed as a two-dimensional version of (the one-dimensional) expander codes, where the codewords are functions on the squares rather than on the edges.
Deep Fisher Vector Coding For Whole Slide Image Classification

Adopting machine learning methods for histological sections is a challenging task given the generated huge size of whole slide images (WSIs) especially using high power resolution. In this paper we propose a novel WSI classification method which efficiently predicts a WSI’s label. The proposed method considers each WSI as a population of patches and computes a statistic by having some samples from the population. This statistic can be computed efficiently, and our test time on a WSI is about one tenth of that of the existing methods. Moreover, our pooling strategy on the WSI is more general than that of previous works. Further, the assumptions of our method are quite general, and therefore, it is applicable to any WSI classification task. The experiments show that the performance of our method is competitive in two different tasks, while, unlike some of the competing methods, it does not consider any prior clinical knowledge about the label to be predicted.
A binary operation on irreducible components of Lusztig's nilpotent varieties {II}: applications and conjectures for representations of $GL_n$ over a non-archimedean local field

In the first part of the paper we defined and studied a binary operation on the set of irreducible components of Lusztig’s nilpotent varieties of a quiver. For type A we conjecture, following Geiss and Schröer, that this operation is compatible with the socle of parabolic induction of representations of general linear groups over a local nonarchimedean field, at least when one of the irreducible components is rigid. We verify this conjecture in special cases.
Impact of new advances in seismology and geotechnical engineering on structural design since the Northridge earthquake

The Northridge earthquake has brought a decade of change in the practice of engineering for all structures, and for tall buildings in particular. With the earlier Loma Prieta earthquake in Northern California, there was already some impetus and momentum to improve seismic safety and reduce risks from seismic hazards. However, the extensive damage and economic losses from the Northridge earthquake fueled greater public will to meet the challenges and provide programs for the reduction of losses and human casualties. In the geotechnical engineering discipline, many of the advances involved a more consistent application of seismic risk across the United States in dealing with strong ground motions. Initiatives were set in place to develop seismic hazard models of the possible earthquake sources that affect the country and to use these models in developing design criteria for seismic design in all regions of the United States, not just in California. These ground motion estimates are now accounted for in the latest design guidelines and building codes; however, the treatments in these documents is simplified and not always tied to reality. Ground motions are complex and advances are being made to understand the complexities and explain the variations. Great advances in seismology have occurred in the 10 years since Northridge, and these advances will be incorporated into building design and analysis with time. Other advances have been made in the area of defining seismic hazards that result from ground failures in California. The Northridge and earlier earthquakes demonstrated that ground failures (especially related to soil liquefaction and landsliding) can present a significant risk and result in large losses. In the decade since the Northridge earthquake, structural analysis has advanced with leaps and bounds due to advances in computer software and hardware. Complex three‐dimensional analyses in the time domain once relegated to only large mainframe computers can now be performed on desktop computers in most structural engineering offices. To model the seismic behavior of structures more accurately, better modeling of the soil–structure interface as well as the soil has become necessary. Models incorporating the appropriate soil behavior provide for better understanding of the forces and displacements of the structures under seismic loading and possibly avoid overconservatism by not accounting for the soil behavior. Copyright © 2004 John Wiley & Sons, Ltd.
Convolutional neural networks with radio-frequency spintronic nano-devices

Convolutional neural networks (LeCun and Bengio 1998 The Handbook of Brain Theory and Neural Networks 255–58; LeCun, Bengio and Hinton 2015 Nature 521 436–44) are state-of-the-art and ubiquitous in modern signal processing and machine vision. Nowadays, hardware solutions based on emerging nanodevices are designed to reduce the power consumption of these networks. This is done either by using devices that implement convolutional filters and sequentially multiply consecutive subsets of the input, or by using different sets of devices to perform the different multiplications in parallel to avoid storing intermediate computational steps in memory. Spintronics devices are promising for information processing because of the various neural and synaptic functionalities they offer. However, due to their low OFF/ON ratio, performing all the multiplications required for convolutions in a single step with a crossbar array of spintronic memories would cause sneak-path currents. Here we present an architecture where synaptic communications are based on a resonance effect. These synaptic communications thus have a frequency selectivity that prevents crosstalk caused by sneak-path currents. We first demonstrate how a chain of spintronic resonators can function as synapses and make convolutions by sequentially rectifying radio-frequency signals encoding consecutive sets of inputs. We show that a parallel implementation is possible with multiple chains of spintronic resonators. We propose two different spatial arrangements for these chains. For each of them, we explain how to tune many artificial synapses simultaneously, exploiting the synaptic weight sharing specific to convolutions. We show how information can be transmitted between convolutional layers by using spintronic oscillators as artificial microwave neurons. Finally, we simulate a network of these radio-frequency resonators and spintronic oscillators to solve the MNIST handwritten digits dataset, and obtain results comparable to software convolutional neural networks. Since it can run convolutional neural networks fully in parallel in a single step with nano devices, the architecture proposed in this paper is promising for embedded applications requiring machine vision, such as autonomous driving.
Lattice Lindblad simulation

We perform the real-time lattice simulation of an open quantum system, which is based on the Schwinger-Keldysh path integral representation of the Lindblad formalism. Although the real-time simulation generally suffers from the sign problem, there exist a few exceptional cases. We focus on a sign-problem-free system of a non-relativistic spinless fermion and analyze time evolution under driving and dissipation.
FPM: A Collection of Large-scale Foundation Pre-trained Language Models

Recent work in language modeling has shown that train- ing large-scale Transformer models has promoted the lat- est developments in natural language processing applica- tions. However, there is very little work to unify the cur- rent effective models. In this work, we use the current ef- fective model structure to launch a model set through the current most mainstream technology. We think this will become the basic model in the future. For Chinese, us- ing the GPT-2[9] model, a 10.3 billion parameter language model was trained on the Chinese dataset, and, in particu- lar, a 2.9 billion parameter language model based on dia- logue data was trained; the BERT model was trained on the Chinese dataset with 495 million parameters; the Trans- former model has trained a language model with 5.6 bil- lion parameters on the Chinese dataset. In English, cor- responding training work has also been done. Using the GPT-2 model, a language model with 6.4 billion param- eters was trained on the English dataset; the BERT[3] model trained a language model with 1.24 billion param- eters on the English dataset, and in particular, it trained a 688 million parameter based on single card training tech- nology Language model; Transformer model trained a lan- guage model with 5.6 billion parameters on the English dataset. In the TNEWS classification task evaluated by CLUE[13], the BERT-C model exceeded the 59.46% accu- racy of ALBERT-xxlarge with an accuracy rate of 59.99%, an increase of 0.53%. In the QQP classification task evalu- ated by GLUE[11], the accuracy rate of 78.95% surpassed the accuracy rate of BERT-Large of 72.1%, an increase of 6.85%. Compared with the current accuracy rate of ERNIE, the first place in the GLUE evaluation of 75.2%, an increase of 3.75%.
Well-posedness for chemotaxis-fluid models in arbitrary dimensions

We study the Cauchy problem for the chemotaxis Navier-Stokes equations and the Keller-Segel-Navier-Stokes system. Local-in-time and global-in-time solutions satisfying fundamental properties such as mass conservation and nonnegativity preservation are constructed for low regularity data in 2 and higher dimensions under suitable conditions. Our initial data classes involve a new scale of function space, that is L − 1 2 ,N − 2 ( R N ) which collects divergence of vector-ﬁelds with components in the square Campanato space L 2 ,N − 2 ( R N ), N > 2 (and can be identiﬁed with the homogeneous Besov space ˙ B − 1 22 ( R N ) when N = 2) and are shown to be optimal in a certain sense. Moreover, uniqueness criterion for global solutions is obtained under certain limiting conditions. is the concentration of oxygen, n is the density of cells, u is the velocity ﬁeld of the ﬂuid governed by the incompressible Navier-Stokes equations with scalar pressure p and viscosity ν . The time-independent gravitational force exerted from a bacteria onto the ﬂuid is modelled through ∇ Φ. The constant ρ represents the magnitude of chemotaxis and D c , D n are diﬀusion coeﬃcients. The function f ( c ) models the inactivity level caused by a low supply of oxygen and χ ( c ) is a suitable cut-oﬀ function (usually determined by experiments). The second equation in (1.1) describes the mass balance equation for the cells combining the advection eﬀect (modelled through u · ∇ n ), the chemotactic eﬀect or the migration towards regions of high concentration of oxygen (modelled by div ( nχ ( c ) ∇ c )) and the diﬀusion of cells (modelled through D n ∆).
Nash Equilibrium Control Policy Against Bus-off Attacks in CAN Networks

A bus-off attack is a denial-of-service (DoS) attack which exploits error handling in the controller area network (CAN) to induce an honest node to disconnect itself from the CAN bus. This paper develops a stochastic transmission policy as a countermeasure for the controller-transmitter pair against the bus-off attack. We model this as a non-zero-sum linear-quadratic-Gaussian game between the controller-transmitter pair and the attacker. We derive Nash equilibria of the game for two different information structures of the attacker. We show that the attacker has a dominant attack strategy under both information structures. Under the dominant attack strategy, we show that the optimal control policy is linear in the system state. We further identify a necessary and a sufficient conditions on the transmission policy to have bounded average cost. The theoretical results are complemented by a detailed case study of a bus-off attack on a vehicular adaptive cruise control model.
Caesynth: Real-Time Timbre Interpolation and Pitch Control with Conditional Autoencoders

In this paper, we present a novel audio synthesizer, CAESynth, based on a conditional autoencoder. CAESynth synthesizes timbre in real-time by interpolating the reference sounds in their shared latent feature space, while controlling a pitch independently. We show that training a conditional autoen-coder based on accuracy in timbre classification together with adversarial regularization of pitch content allows timbre distribution in latent space to be more effective and stable for timbre interpolation and pitch conditioning. The proposed method is applicable not only to creation of musical cues but also to exploration of audio affordance in mixed reality based on novel timbre mixtures with environmental sounds. We demonstrate by experiments that CAESynth achieves smooth and high-fidelity audio synthesis in real-time through timbre interpolation and independent yet accurate pitch control for musical cues as well as for audio affordance with environmental sound. A Python implementation along with some generated samples are shared online.
Extreme local extrema of the sine-Gordon field

We prove that for $\beta<6\pi$ the local extremal process of the massive sine-Gordon field on the unit torus in $d=2$ converges to a Poisson point process with random intensity measure ${\rm Z}^{\mathrm{SG}}(dx) \otimes e^{-\alpha h}dh$ for some $\alpha>0$. The proof combines existing methods for the extremal process associated to the Gaussian free field, which was introduced and studied by Biskup and Louidor, and a strong coupling between the sine-Gordon field and the Gaussian free field.
AEAD Modes for ZUC Family Stream Ciphers

In order to improve the efficiency of using ZUC primitives, we give two AEAD (Authenticated Encryption with Associated Data) modes for them, ZUC-GXM and ZUC-MUR. They are suitable for ZUC (ZUC-128) and two cases of ZUC-256. The former is a nonce-based AEAD, which is following the GCM framework. The latter is a nonce misuse-resistant one which is based on the framework of SIV variance, providing more robust applications for ZUC family stream ciphers.
Unsupervised Approaches for Out-Of-Distribution Dermoscopic Lesion Detection

There are limited works showing the efficacy of unsupervised Out-of-Distribution (OOD) methods on complex medical data. Here, we present preliminary findings of our unsupervised OOD detection algorithm, SimCLR-LOF, as well as a recent state of the art approach (SSD), applied on medical images. SimCLR-LOF learns semantically meaningful features using SimCLR and uses LOF for scoring if a test sample is OOD. We evaluated on the multi-source International Skin Imaging Collaboration (ISIC) 2019 dataset, and show results that are competitive with SSD as well as with recent supervised approaches applied on the same data.
Semiclassical Approach to the Nonlocal Kinetic Model of Metal Vapor Active Media

A semiclassical approach based on the WKB–Maslov method is developed for the kinetic ionization equation in dense plasma with approximations characteristic of metal vapor active media excited by a contracted discharge. We develop the technique for constructing the leading term of the semiclassical asymptotics of the Cauchy problem solution for the kinetic equation under the supposition of weak diffusion. In terms of the approach developed, the local cubic nonlinear term in the original kinetic equation is considered in a nonlocal form. This allows one to transform the nonlinear nonlocal kinetic equation to an associated linear partial differential equation with a given accuracy of the asymptotic parameter using the dynamical system of moments of the desired solution of the equation. The Cauchy problem solution for the nonlinear nonlocal kinetic equation can be obtained from the solution of the associated linear partial differential equation and some algebraic equations for the coefficients of the linear equation. Within the developed approach, the plasma relaxation in metal vapor active media is studied with asymptotic solutions expressed in terms of higher transcendental functions. The qualitative analysis of such the solutions is given.
Papaya: Practical, Private, and Scalable Federated Learning

Cross-device Federated Learning (FL) is a distributed learning paradigm with several challenges that differentiate it from traditional distributed learning, variability in the system characteristics on each device, and millions of clients coordinating with a central server being primary ones. Most FL systems described in the literature are synchronous - they perform a synchronized aggregation of model updates from individual clients. Scaling synchronous FL is challenging since increasing the number of clients training in parallel leads to diminishing returns in training speed, analogous to large-batch training. Moreover, stragglers hinder synchronous FL training. In this work, we outline a production asynchronous FL system design. Our work tackles the aforementioned issues, sketches of some of the system design challenges and their solutions, and touches upon principles that emerged from building a production FL system for millions of clients. Empirically, we demonstrate that asynchronous FL converges faster than synchronous FL when training across nearly one hundred million devices. In particular, in high concurrency settings, asynchronous FL is 5x faster and has nearly 8x less communication overhead than synchronous FL.
Exploiting Robust Unsupervised Video Person Re-identification

Unsupervised video person re-identification (reID) methods usually depend on global-level features. And many supervised reID methods employed local-level features and achieved significant performance improvements. However, applying local-level features to unsupervised methods may introduce an unstable performance. To improve the performance stability for unsupervised video reID, this paper introduces a general scheme fusing part models and unsupervised learning. In this scheme, the global-level feature is divided into equal local-level feature. A local-aware module is employed to explore the poentials of local-level feature for unsupervised learning. A global-aware module is proposed to overcome the disadvantages of local-level features. Features from these two modules are fused to form a robust feature representation for each input image. This feature representation has the advantages of local-level feature without suffering from its disadvantages. Comprehensive experiments are conducted on three benchmarks, including PRID2011, iLIDS-VID, and DukeMTMC-VideoReID, and the results demonstrate that the proposed approach achieves state-of-the-art performance. Extensive ablation studies demonstrate the effectiveness and robustness of proposed scheme, local-aware module and global-aware module. The code and generated features are available at https://github.com/deropty/uPMnet.