Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance of homodyne detection of binary PSK optical signals

This study evaluates the performance of an optical receiver for binary phase shift keyed (BPSK) signals in the presence of noise originating from the photodetectors and the phase fluctuations of the optical sources. Analysis of the homodyne detection process shows that the performance is degraded by two effects: One due to the phase error fluctuations of the recovered carrier and the other due to reduction of the energy per bit available for data recovery. The resulting power penalty can be minimized by dividing in an optimal way the received optical signal between the carrier recovery and the data recovery circuits of the receiver. The minimum penalty thus obtained depends on the 3-dB linewidth and on the transmission rate. For example, a penalty of 0.5 dB, relative to the quantum limit of 9 photon bit needed to achieve a BER of 10-9, imposes a minimum transmission rate of about 180 Gbit/s when the optical source has a 3-dB linewidth of 20 MHz.
Quantum non-demolition measurement based on an SU(1,1)-SU(2)-concatenated atom-light hybrid interferometer

1State Key Laboratory of Precision Spectroscopy, Quantum Institute for Light and Atoms, Department of Physics, East China Normal University, Shanghai 200062, China 2School of Physics and Astronomy, and Tsung-Dao Lee Institute, Shanghai Jiao Tong University, Shanghai 200240, China 3Shanghai Research Center for Quantum Sciences, Shanghai 201315, China 4Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi 030006, China 5Corresponding author: lqchen@phy.ecnu. edu.cn 6Corresponding author: chyuan@phy.ecnu. edu.cn
Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning)

We study Byzantine collaborative learning, where $n$ nodes seek to collectively learn from each others' local data. The data distribution may vary from one node to another. No node is trusted, and $f<n$ nodes can behave arbitrarily. We prove that collaborative learning is equivalent to a new form of agreement, which we call averaging agreement. In this problem, nodes start each with an initial vector and seek to approximately agree on a common vector, which is close to the average of honest nodes' initial vectors. We present two asynchronous solutions to averaging agreement, each we prove optimal according to some dimension. The first, based on the minimum-diameter averaging, requires $ n \geq 6f+1$, but achieves asymptotically the best-possible averaging constant up to a multiplicative constant. The second, based on reliable broadcast and coordinate-wise trimmed mean, achieves optimal Byzantine resilience, i.e., $n \geq 3f+1$. Each of these algorithms induces an optimal Byzantine collaborative learning protocol. In particular, our equivalence yields new impossibility theorems on what any collaborative learning algorithm can achieve in adversarial and heterogeneous environments.
Training BatchNorm Only in Neural Architecture Search and Beyond

This work investigates the usage of batch normalization in neural architecture search (NAS). Specifically, Frankle et al. find that training BatchNorm only can achieve nontrivial performance. Furthermore, Chen et al. claim that training BatchNorm only can speed up the training of the one-shot NAS supernet over ten times. Critically, there is no effort to understand 1) why training BatchNorm only can find the perform-well architectures with the reduced supernet-training time, and 2) what is the difference between the train-BN-only supernet and the standard-train supernet. We begin by showing that the train-BN-only networks converge to the neural tangent kernel regime, obtain the same training dynamics as train all parameters theoretically. Our proof supports the claim to train BatchNorm only on supernet with less training time. Then, we empirically disclose that train-BN-only supernet provides an advantage on convolutions over other operators, cause unfair competition between architectures. This is due to only the convolution operator being attached with BatchNorm. Through experiments, we show that such unfairness makes the search algorithm prone to select models with convolutions. To solve this issue, we introduce fairness in the search space by placing a BatchNorm layer on every operator. However, we observe that the performance predictor in Chen et al. is inapplicable on the new search space. To this end, we propose a novel composite performance indicator to evaluate networks from three perspectives: expressivity, trainability, and uncertainty, derived from the theoretical property of BatchNorm. We demonstrate the effectiveness of our approach on multiple NAS-benchmarks (NAS-Bench101, NAS-Bench-201) and search spaces (DARTS search space and MobileNet search space).
A 2+1+1 quadruple star system containing the most eccentric, low-mass, short-period, eclipsing binary known

We present an analysis of a newly discovered 2+1+1 quadruple system with TESS containing an unresolved eclipsing binary (EB) as part of TIC 121088960 and a close neighbor TIC 121088959. The EB consists of two very low-mass M dwarfs in a highly-eccentric (e = 0.709) short-period (P = 3.04358 d) orbit. Given the large pixel size of TESS and the small separation (3. ′′9) between TIC 121088959 and TIC 121088960 we used light centroid analysis of the difference image between in-eclipse and out-of-eclipse data to show that the EB likely resides in TIC 121088960, but contributes only ∼10% of its light. Radial velocity data were acquired with iSHELL at NASA’s Infrared Facility and the Coudé spectrograph at the McDonald 2.7-m telescope. For both images, the measured RVs showed no variation over the 11-day observational baseline, and the RV difference between the two images was 8±0.3 km s−1. The similar distances and proper motions of the two images indicate that TIC 121088959 and TIC 121088960 are a gravitationally bound pair. Gaia’s large RUWE and astrometric_excess_noise parameters for TIC 121088960, further indicate that this image is the likely host of the unresolved EB and is itself a triple star. We carried out an SED analysis and calculated stellar masses for the four stars, all of which are in the M dwarf regime: 0.19 M and 0.14 M for the EB stars and 0.43 M and 0.39 M for the brighter visible stars, respectively. Lastly, numerical simulations show that the orbital period of the inner triple is likely the range 1 to 50 years.
Slotted ground microstrip antenna with FSS reflector for high-gain horizontal polarisation

The design of a high-gain horizontally polarised antenna for ultra-wideband (UWB) operation is presented and experimentally demonstrated. Gain enhancement is achieved by using a frequency selective surface (FSS) reflector. Both the antenna and the FSS have simple structures and are printed on a similar substrate. The antenna is a three-stepped narrow rectangular slot excited using a three-stepped microstrip feed line. By optimising the lengths and widths of the sections of the slot and the feed line, an ultra-wide impedance bandwidth (3.2–12 GHz, measured) has been achieved. The FSS is a two-layer structure where each layer is an array of rectangular patches printed on one side of the substrate. With the application of the FSS, the antenna gain is enhanced by 3–4 dBi over most of the band. The polarisation purity (in terms of the measured cross-polar difference) is also improved with the FSS. Such an antenna–FSS combination will be useful for high-gain UWB applications requiring horizontal polarisation.
Human-Object Interaction Detection via Weak Supervision

The goal of this paper is Human-object Interaction (HO-I) detection. HO-I detection aims to find interacting human-objects regions and classify their interaction from an image. Researchers obtain significant improvement in recent years by relying on strong HO-I alignment supervision from [5]. HO-I alignment supervision pairs humans with their interacted objects, and then aligns human-object pair(s) with their interaction categories. Since collecting such annotation is expensive, in this paper, we propose to detect HO-I without alignment supervision. We instead rely on image-level supervision that only enumerates existing interactions within the image without pointing where they happen. Our paper makes three contributions: i) We propose Align-Former, a visual-transformer based CNN that can detect HO-I with only image-level supervision. ii) Align-Former is equipped with HO-I align layer, that can learn to select appropriate targets to allow detector supervision. iii) We evaluate Align-Former on HICO-DET [5] and V-COCO [13], and show that Align-Former outperforms existing image-level supervised HO-I detectors by a large margin (4.71% mAP improvement from 16.14% to 20.85% on HICO-DET [5]).
Wearable Eating Habit Sensing System Using Internal Body Sound

Continuous monitoring of eating habits could be useful in preventing lifestyle diseases such as metabolic syndrome. Conventional methods consist of self-reporting and calculating mastication frequency based on the myoelectric potential of the masseter muscle. Both these methods are significant burdens for the user. We developed a non-invasive, wearable sensing system that can record eating habits over a long period of time in daily life. Our sensing system is composed of two bone conduction microphones placed in the ears that send internal body sound data to a portable IC recorder. Applying frequency spectrum analysis on the collected sound data, we could not only count the number of mastications during eating, but also accurately differentiate between eating, drinking, and speaking activities. This information can be used to evaluate the regularity of meals. Moreover, we were able to analyze sound features to classify the types of foods eaten by food texture.
Leveraging Sequence Embedding and Convolutional Neural Network for Protein Function Prediction

The capability of accurate prediction of protein functions and properties is essential in the biotechnology industry, e.g. drug development and artificial protein synthesis, etc. The main challenges of protein function prediction are the large label space and the lack of labeled training data. Our method leverages unsupervised sequence embedding and the success of deep convolutional neural network to overcome these challenges. In contrast, most of the existing methods delete the rare protein functions to reduce the label space. Furthermore, some existing methods require additional bio-information (e.g., the 3-dimensional structure of the proteins) which is difficult to be determined in biochemical experiments. Our proposed method significantly outperforms the other methods on the publicly available benchmark using only protein sequences as input. This allows the process of identifying protein functions to be sped up.
Losses in GaAs microstrip

Data on the losses associated with microstrip transmission lines with impedances from approximately 20 to 90 Omega at frequencies up to 40 GHz for structures fabricated on 100- mu m ( mu -mil) GaAs are presented. The data were taken from transmission measurements of lightly coupled, multiple half-wavelength resonators. A comparison of the loss data with the predicted losses from three popular CAE tools is provided. The loss models agree fairly well with measured data through 40 GHz. Since the measured loss roughly follows a square-law frequency relationship through 20 GHz, it should be possible to adjust the loss below this frequency and above 1 GHz by modifying the resistivity of the conductor in the simulator. <<ETX>>
MEFNet: Multi-scale Event Fusion Network for Motion Deblurring

Traditional frame-based cameras inevitably suffer from motion blur due to long exposure times. As a kind of bio-inspired camera, the event camera records the intensity changes in an asynchronous way with high temporal resolution, providing valid image degradation information within the exposure time. In this paper, we rethink the event-based image deblurring problem and unfold it into an end-to-end two-stage image restoration network. To effectively utilize event information, we design (i) a novel symmetric cumulative event representation speciﬁcally for image deblurring, and (ii) an afﬁne event-image fusion module applied at multiple levels of our network. We also propose an event mask gated connection between the two stages of the network so as to avoid information loss. At the dataset level, to foster event-based motion deblurring and to facilitate evaluation on challenging real-world images, we introduce the High-Quality Blur (HQBlur) dataset, captured with an event camera in an illumination-controlled optical laboratory. Our Multi-Scale Event Fusion Network (MEFNet) sets the new state of the art for motion deblurring, surpassing both the prior best-performing image-based method and all event-based methods with public implementations on the GoPro (by up to 2.38dB) and HQBlur datasets, even in extreme blurry conditions. Source code and dataset will be made publicly available.
The Effect of People Recommenders on Echo Chambers and Polarization

The effects of online social media on critical issues, such as polarization and misinformation, are under scrutiny due to the disruptive consequences that these phenomena can have on our societies. Among the algorithms routinely used by social media platforms, people-recommender systems are of special interest, as they directly contribute to the evolution of the social network structure, affecting the information and the opinions users are exposed to. In this paper, we propose a novel framework to assess the effect of people recommenders on the evolution of opinions. Our proposal is based on Monte Carlo simulations combining link recommendation and opinion-dynamics models. In order to control initial conditions, we define a random network model to generate graphs with opinions, with tunable amounts of modularity and homophily. Finally, we join these elements into a methodology able to study the causal relationship between the recommender system and the echo chamber effect. Our method can also assess if such relationships are statistically significant. We also show how such a framework can be used to measure, by means of simulations, the impact of different intervention strategies. Our thorough experimentation shows that people recommenders can in fact lead to a significant increase in echo chambers. However, this happens only if there is considerable initial homophily in the network. Also, we find that if the network already contains echo chambers, the effect of the recommendation algorithm is negligible. Such findings are robust to two very different opinion dynamics models, a bounded confidence model and an epistemological model.
Conflict-free Collaborative Set Sharing for Distributed Systems

Collaborative Data Sharing is widely noticed to be essential for distributed systems. Among several proposed strategies, conflict-free techniques are considered useful for serverless concurrent systems. They aim at making shared data be consistent between peers in such a way that their local data do not become equal at once, but they arrive at the same data eventually when no updates occur in any peer. Although the Conflict-free Replicated Data Type (CRDT) approach could be used in data sharing as well, it puts restrictions on available operations so as to concurrent updates never cause conflicts. Even for sets, popular operations such as insertion and deletion are not freely used, for example. We propose a novel scheme for Conflict-free Collaborative Set Sharing that allows both insertion and deletion operations. It will provide a new synchronization method for data sharing and gives a fresh insight into designing conflict-free replicated data types. We might consider that this becomes a substitute for CRDTs.
Unequal Opportunities in Multi-hop Referral Programs

As modern social networks allow for faster and broader interactions with friends and acquaintances, online referral programs that promote sales through existing users are becoming increasingly popular. Because it is all too common that online networks reproduce historical structural bias, members of disadvantaged groups often benefit less from such referral opportunities. For instance, one-hop referral programs that distribute rewards only among pairs of friends or followers may offer less rewards and opportunities to minorities in networks where it was proved that their degrees is statistically smaller. Here, we examine the fairness of general referral programs, increasingly popular forms of marketing in which an existing referrer is encouraged to initiate the recruitment of new referred users over multiple hops. While this clearly expands opportunities for rewards, it remains unclear whether it helps addressing fairness concerns, or make them worse. We show, from studying 4 real-world networks and performing theoretical analysis on networks created with minority-majority affiliations and homophily, that the change of bias in multi-hop referral programs highly depends on the network structures and the referral strategies. Specifically, under three different constrained referral strategies which limit the number of referrals each person can share to a fixed number, we show that even with no explicit intention to discriminate and without access to sensitive attributes such as gender and race, certain referral strategies can still amplify the structural biases further when higher hops are allowed. Moreover, when there is no constraint on the number of referrals each person can distribute and when the effect of referral strategies is removed, we prove a precise condition under which the bias in 1-hop referral programs is amplified in higher-hop referral programs.
Variational Learning for Unsupervised Knowledge Grounded Dialogs

Recent methods for knowledge grounded dialogs generate responses by incorporating information from an external textual document. These methods do not require the exact document to be known during training and rely on the use of a retrieval system to fetch relevant documents from a large index. The documents used to generate the responses are modeled as latent variables whose prior probabilities need to be estimated. Models such as RAG, marginalize the document probabilities over the documents retrieved from the index to define the log-likelihood loss function which is optimized end-to-end. In this paper, we develop a variational approach to the above technique wherein, we instead maximize the Evidence Lower bound (ELBO). Using a collection of three publicly available open-conversation datasets, we demonstrate how the posterior distribution, which has information from the ground-truth response, allows for a better approximation of the objective function during training. To overcome the challenges associated with sampling over a large knowledge collection, we develop an efficient approach to approximate the ELBO. To the best of our knowledge, we are the first to apply variational training for open-scale unsupervised knowledge grounded dialog systems.
Probability-Generating Function Kernels for Spherical Data

Probability-generating function (PGF) kernels are introduced, which constitute a class of kernels supported on the unit hypersphere, for the purposes of spherical data analysis. PGF kernels generalize RBF kernels in the context of spherical data. The properties of PGF kernels are studied. A semi-parametric learning algorithm is introduced to enable the use of PGF kernels with spherical data.
Distributed Forward-Backward Methods without Central Coordination

In this work, we propose and analyse forward-backward-type algorithms for ﬁnding a zero in the sum of ﬁnitely many monotone operators, which are not based on reduction to a two operator inclusion in the product space. Each iteration of the studied algorithms requires one resolvent evaluation per set-valued operator, one forward evaluation per cocoercive operator, and two forward evaluations per monotone operator. Unlike existing methods, the structure of the proposed algorithms are suitable for distributed, decentralised implementation in ring networks without the need for a central coordinator to enforce consensus between nodes
The Tropical Manin–Mumford Conjecture

In analogy with the Manin–Mumford conjecture for algebraic curves, one may ask how a metric graph under the Abel–Jacobi embedding intersects torsion points of its Jacobian. We show that the number of torsion points is finite for metric graphs of genus ${g\geq 2}$, which are biconnected and have edge lengths that are “sufficiently irrational” in a precise sense. Under these assumptions, the number of torsion points is bounded by $3g-3$. Next, we study bounds on the number of torsion points in the image of higher-degree Abel–Jacobi embeddings, which send $d$-tuples of points to the Jacobian. This motivates the definition of the “independent girth” of a graph, a number that is a sharp upper bound for $d$ such that the higher-degree Manin–Mumford property holds.
Layering and vertical transport in sheared double-diffusive convection in the diffusive regime

Abstract A sequence of two- and three-dimensional simulations are conducted for the double-diffusive convection (DDC) flows in the diffusive regime subjected to an imposed shear. For a wide range of control parameters, and for sufficiently strong perturbation of the conductive initial state, staircase-like structures spontaneously develop, with relatively well-mixed layers separated by sharp interfaces of enhanced scalar gradient. Such staircases appear to be robust even in the presence of strong shear over very long times, with early-time coarsening of the observed layers. For the same set of control parameters, different asymptotic layered states, with markedly different vertical scalar fluxes, can arise for different initial perturbation structures. The imposed shear significantly spatio-temporally modifies the vertical transport of the various scalars. The flux ratio $\gamma ^*$ (i.e. the ratio between the density fluxes due to the total salt flux and the total heat flux) is found, at steady state, to be essentially equal to the square root of the ratio of the salt diffusivity to the thermal diffusivity, consistent with the physical model proposed by Linden & Shirtcliffe (J. Fluid Mech., vol. 87, 1978, pp. 417–432) and the variational arguments presented by Stern (J. Fluid Mech., vol. 114, 1982, pp. 105–121) for unsheared double-diffusive convection.
Knowledge sharing in industrial research

• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.