Miracle-dz/UniFAR-FTUs huggingface dataset:
Physical Layer Security of RIS-Assisted Communications Under Electromagnetic Interference

This work investigates the impact of ever-present electromagnetic interference (EMI) on the achievable secrecy performance of reconfigurable intelligent surface (RIS)-aided communication systems. We characterize the end-to-end RIS channel by considering key practical aspects such as spatial correlation, the transmit beamforming vector, phase-shift noise, the coexistence of direct and indirect channels, and the presence of strong/mild EMI on the receiver sides. We show that EMI’s effect on secrecy performance strongly depends on the eavesdropper’s ability to cancel the interference. This highlights the potential of EMI-based attacks to degrade physical layer security in RIS-aided communications.
On Higher-Order Reachability Games vs May Reachability

We consider the reachability problem for higher-order functional programs and study the relationship between reachability games (i.e., the reachability problem for programs with angelic and demonic nondeterminism) and may-reachability (i.e., the reachability problem for programs with only angelic nondeterminism). We show that reachability games for order-n programs can be reduced to may-reachability problems for order(n + 1) programs, and vice versa. We formalize the reductions by using higher-order fixpoint logic and prove their correctness. We also discuss applications of the reductions to higher-order program verification.
L-band Integral Field Spectroscopy of the HR 8799 Planetary System

Understanding the physical processes sculpting the appearance of young gas-giant planets is complicated by degeneracies confounding effective temperature, surface gravity, cloudiness, and chemistry. To enable more detailed studies, spectroscopic observations covering a wide range of wavelengths are required. Here we present the first L-band spectroscopic observations of HR 8799 d and e and the first low-resolution wide-bandwidth L-band spectroscopic measurements of HR 8799 c. These measurements were facilitated by an upgraded LMIRCam/ALES instrument at the Large Binocular Telescope, together with a new apodizing phase plate coronagraph. Our data are generally consistent with previous photometric observations covering similar wavelengths, yet there exists some tension with narrowband photometry for HR 8799 c. With the addition of our spectra, each of the three innermost observed planets in the HR 8799 system has had its spectral energy distribution measured with integral field spectroscopy covering ∼0.9–4.1 μm. We combine these spectra with measurements from the literature and fit synthetic model atmospheres. We demonstrate that the bolometric luminosity of the planets is not sensitive to the choice of model atmosphere used to interpolate between measurements and extrapolate beyond them. Combining luminosity with age and mass constraints, we show that the predictions of evolutionary models are narrowly peaked for effective temperature, surface gravity, and planetary radius. By holding these parameters at their predicted values, we show that more flexible cloud models can provide good fits to the data while being consistent with the expectations of evolutionary models.
Learning Audio Representations with MLPs

In this paper, we propose an efficient MLP-based approach for learning audio representations, namely timestamp and scene-level audio embeddings. We use an encoder consisting of sequentially stacked gated MLP blocks, which accept 2D MFCCs as inputs. In addition, we also provide a simple temporal interpolation-based algorithm for computing scene-level embeddings from timestamp embeddings. The audio representations generated by our method are evaluated across a diverse set of benchmarks at the Holistic Evaluation of Audio Representations (HEAR) challenge, hosted at the NeurIPS 2021 competition track. We achieved first place on the Speech Commands (full), Speech Commands (5 hours), and the Mridingham Tonic benchmarks. Furthermore, our approach is also the most resource-efficient among all the submitted methods, in terms of both the number of model parameters and the time required to compute embeddings.
Bridging the Data Gap between Training and Inference for Unsupervised Neural Machine Translation

Back-translation is a critical component of Unsupervised Neural Machine Translation (UNMT), which generates pseudo parallel data from target monolingual data. A UNMT model is trained on the pseudo parallel data with \text{\bf translated source}, and translates \text{\bf natural source} sentences in inference. The source discrepancy between training and inference hinders the translation performance of UNMT models. By carefully designing experiments, we identify two representative characteristics of the data gap in source: (1) \text{\textit{style gap}} (i.e., translated vs. natural text style) that leads to poor generalization capability; (2) \text{\textit{content gap}} that induces the model to produce hallucination content biased towards the target language. To narrow the data gap, we propose an online self-training approach, which simultaneously uses the pseudo parallel data \{natural source, translated target\} to mimic the inference scenario. Experimental results on several widely-used language pairs show that our approach outperforms two strong baselines (XLM and MASS) by remedying the style and content gaps.
Raw waveform speaker veriﬁcation for supervised and self-supervised learning

Speaker veriﬁcation models that directly operate upon raw waveforms are receiving growing attention. However, their performances are less competitive than the state-of-the-art hand-crafted feature-based counterparts, demonstrating equal error rates under 1% on the benchmark VoxCeleb1 evaluation protocol. In addition, they have yet not been explored with self-supervised learning frameworks. This paper proposes a new raw waveform speaker veriﬁcation model that incorporates techniques proven effective for speaker veriﬁcation, including the Res2Net backbone module and the aggregation method considering both context and channels. Under the best performing conﬁguration, the model shows an equal error rate of 0.89%, competitive with state-of-the-art models. We also explore the proposed model with a self-supervised learning framework and show the state-of-the-art performance in this line of research. Finally, we show that leveraging the model trained with self-supervision successfully serves as a pre-trained model under the semi-supervised scenario where it is assumed that only a limited amount of data has a ground truth label and a bigger data has no label.
Numerical Analysis of the Tuning Characteristics of two Electrode DFB Lasers

The optical power distribution inside a DFB laser cavity as demonstrated by Kogelnik and Shank [2] and the nonuniform current injection in separated electrodes both cause an axial dependence of the carrier density. Since gain and refractive index are function of carrier density [3], we adopted the model developed by Kikuchi et al. [4] to simulate the oscillation characteristics of the laser. We have thus the following set of equations where z-axis is along the cavity:
Improving Word Translation via Two-Stage Contrastive Learning

Word translation or bilingual lexicon induction (BLI) is a key cross-lingual task, aiming to bridge the lexical gap between different languages. In this work, we propose a robust and effective two-stage contrastive learning framework for the BLI task. At Stage C1, we propose to refine standard cross-lingual linear maps between static word embeddings (WEs) via a contrastive learning objective; we also show how to integrate it into the self-learning procedure for even more refined cross-lingual maps. In Stage C2, we conduct BLI-oriented contrastive fine-tuning of mBERT, unlocking its word translation capability. We also show that static WEs induced from the ‘C2-tuned’ mBERT complement static WEs from Stage C1. Comprehensive experiments on standard BLI datasets for diverse languages and different experimental setups demonstrate substantial gains achieved by our framework. While the BLI method from Stage C1 already yields substantial gains over all state-of-the-art BLI methods in our comparison, even stronger improvements are met with the full two-stage framework: e.g., we report gains for 112/112 BLI setups, spanning 28 language pairs.
Doubly-Robust Dynamic Treatment Regimen Estimation for Binary Outcomes

In precision medicine, Dynamic Treatment Regimes (DTRs) are treatment protocols that adapt over time in response to a patient’s observed characteristics. A DTR is a set of decision functions that takes an individual patient’s information as arguments and outputs an action to be taken. Building on observed data, the aim is to identify the DTR that optimizes expected patient outcomes. Multiple methods have been proposed for optimal DTR estimation with continuous outcomes. However, optimal DTR estimation with binary outcomes is more complicated and has received comparatively little attention. Solving a system of weighted generalized estimating equations, we propose a new balancing weight criterion to overcome the misspecification of generalized linear models’ nuisance components. We construct binary pseudo-outcomes, and develop a doubly-robust and easy-to-use method to estimate an optimal DTR with binary outcomes. We also outline the underlying theory, which relies on the balancing property of the weights; provide simulation studies that verify the double-robustness of our method; and illustrate the method in studying the effects of e-cigarette usage on smoking cessation, using observational data from the Population Assessment of Tobacco and Health (PATH) study. ar X iv :2 20 3. 08 26 9v 1 [ st at . M E ] 1 5 M ar 2 02 2 A preprint March 17, 2022
FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction

Sequence modeling has demonstrated state-of-the-art performance on natural language and document understanding tasks. However, it is challenging to correctly serialize tokens in form-like documents in practice due to their variety of layout patterns. We propose FormNet, a structure-aware sequence model to mitigate the suboptimal serialization of forms. First, we design Rich Attention that leverages the spatial relationship between tokens in a form for more precise attention score calculation. Second, we construct Super-Tokens for each word by embedding representations from their neighboring tokens through graph convolutions. FormNet therefore explicitly recovers local syntactic information that may have been lost during serialization. In experiments, FormNet outperforms existing methods with a more compact model size and less pre-training data, establishing new state-of-the-art performance on CORD, FUNSD and Payment benchmarks.
COMPUTATIONAL FLUID DYNAMICS RESEARCH ON PRESSURE LOSS OF CROSS-FLOW PERFORATED MUFFLER

The pressure loss of cross-flow perforated muffler has been computed with the procedure of physical modeling,simulation and data processing. Three-dimensional computational fluid dynam-ics (CFD) has been used to investigate the relations of porosities,flow velocity and diameter of the holes with the pressure loss. Accordingly,some preliminary results have been obtained that pressure loss increases with porosity descent as nearly a hyperbolic trend,rising flow velocity of the input makes the pressure loss increasing with parabola trend,diameter of holes affects little about pressure loss of the muffler. Otherwise,the holes on the perforated pipes make the air flow gently and meanly,which decreases the air impact to the wall and pipes in the muffler. A practical perforated muffler is used to illustrate the available of this method for pressure loss computation,and the comparison shows that the computation results with the method of CFD has reference value for muffler design.
Flux Qubit Based on Hybrid Ferromagnetic-Superconducting Device

One of the promising strategy to realize a quantum bit is based on creation of a superposition of circulating currents in a superconducting device [1, 2, 3, 4, 5, 6]. Such qubit realization, termed “flux qubit” is realized by creating a superconducting loop subject to external magnetic field (Fig. 1a). When the loop encloses (nearly) half of the superconducting flux quantum, Φ ≈ 12Φ0, there is an energy near degeneracy of clock-wise (CW) and counter-clockwise (CCW) circulation of superconducting currents which is exploited to create a quantum bit [1, 7, 8]. Some of the scalability issues are naturally related to necessety of creating stable external field in large area that also should be calibrated. Here we propose a realization of flux qubit without the external magnetic field that will provide a compact design. We propose biasing the device in a superconductor-ferromagnet hybrid realization where the flux bias is induced purely by vector potential from the ferromagnet component (Fig. 1b). First, we give analytical estimates for the idealized case, and then a self-consistent calculation for realistic material constants and geometries.
Hardness for triangle problems under even more believable hypotheses: reductions from real APSP, real 3SUM, and OV

The 3SUM hypothesis, the All-Pairs Shortest Paths (APSP) hypothesis and the Strong Exponential Time Hypothesis are the three main hypotheses in the area of fine-grained complexity. So far, within the area, the first two hypotheses have mainly been about integer inputs in the Word RAM model of computation. The “Real APSP” and “Real 3SUM” hypotheses, which assert that the APSP and 3SUM hypotheses hold for real-valued inputs in a reasonable version of the Real RAM model, are even more believable than their integer counterparts. Under the very believable hypothesis that at least one of the Integer 3SUM hypothesis, Integer APSP hypothesis or SETH is true, Abboud, Vassilevska W. and Yu [STOC 2015] showed that a problem called Triangle Collection requires n3−o(1) time on an n-node graph. The main result of this paper is a nontrivial lower bound for a slight generalization of Triangle Collection, called All-Color-Pairs Triangle Collection, under the even more believable hypothesis that at least one of the Real 3SUM, the Real APSP, and the Orthogonal Vector (OV) hypotheses is true. Combined with slight modifications of prior reductions from Triangle Collection, we obtain polynomial conditional lower bounds for problems such as the (static) ST-Max Flow problem and dynamic versions of Max Flow, Single-Source Reachability Count, and Counting Strongly Connected Components, now under the new weaker hypothesis. Our main result is built on the following two lines of reductions. In the first line of reductions, we show Real APSP and Real 3SUM hardness for the All-Edges Sparse Triangle problem. Prior reductions only worked from the integer variants of these problems. In the second line of reductions, we show Real APSP and OV hardness for a variant of the Boolean Matrix Multiplication problem. Along the way we show that Triangle Collection is equivalent to a simpler restricted version of the problem, simplifying prior work. Our techniques also have other interesting implications, such as a super-linear lower bound of Integer All-Numbers 3SUM based on the Real 3SUM hypothesis, and a tight lower bound for a string matching problem based on the OV hypothesis.
Sinkhorn MPC: Model predictive optimal transport over dynamical systems*

We consider the optimal control problem of steering an agent population to a desired distribution over an infinite horizon. This is an optimal transport problem over a dynamical system, which is challenging due to its high computational cost. In this paper, we propose Sinkhorn MPC, which is a dynamical transport algorithm combining model predictive control and the so-called Sinkhorn algorithm. The notable feature of the proposed method is that it achieves cost-effective transport in real time by performing control and transport planning simultaneously. In particular, for linear systems with an energy cost, we reveal the fundamental properties of Sinkhorn MPC such as ultimate boundedness and asymptotic stability.
A random matrix theory approach to damping in deep learning

We conjecture that the inherent difference in generalisation between adaptive and non-adaptive gradient methods in deep learning stems from the increased estimation noise in the flattest directions of the true loss surface. We demonstrate that typical schedules used for adaptive methods (with low numerical stability or damping constants) serve to bias relative movement towards flat directions relative to sharp directions, effectively amplifying the noise-to-signal ratio and harming generalisation. We further demonstrate that the numerical damping constant used in these methods can be decomposed into a learning rate reduction and linear shrinkage of the estimated curvature matrix. We then demonstrate significant generalisation improvements by increasing the shrinkage coefficient, closing the generalisation gap entirely in both logistic regression and several deep neural network experiments. Extending this line further, we develop a novel random matrix theory based damping learner for second order optimisers inspired by linear shrinkage estimation. We experimentally demonstrate our learner to be very insensitive to the initialised value and to allow for extremely fast convergence in conjunction with continued stable training and competitive generalisation. We also find that our derived method works well with adaptive gradient methods such as Adam.
Personal Knowledge Graphs: Use Cases in e-learning Platforms

Personal Knowledge Graphs (PKGs) are introduced by the semantic web community as small-sized user-centric knowledge graphs (KGs). PKGs fill the gap of personalised representation of user data and interests on the top of big, well-established encyclopedic KGs, such as DBpedia [21]. Inspired by the widely recent usage of PKGs in the medical domain to represent patient data, this PhD proposal aims to adopt a similar technique in the educational domain in e-learning platforms by deploying PKGs to represent users and learners. We propose a novel PKG development that relies on ontology and interlinks to Linked Open Data. Hence, adding the dimension of personalisation and explainability in users’ featured data while respecting privacy. This research design is developed in two use cases: a collaborative search learning platform and an e-learning platform. Our preliminary results show that e-learning platforms can get benefited from our approach by providing personalised recommendations and more user and group-specific data.
Heaps reduction, decorated diagrams, and the affine Temperley-Lieb algebra of type $C$

In this paper we propose a combinatorial framework to study a diagrammatic representation of the aﬃne Temperley-Lieb algebra of type (cid:101) C n introduced by Ernst. In doing this, we deﬁne two procedures, a decoration algorithm on diagrams and a reduction algorithm on heaps of independent interest. Using this approach, an explicit algorithmic description of Ernst representation map is provided from which its faithfulness can be deduced. We also give a construction of the inverse map.
Is Fermi liquid topologically protected?

The book by Volovik [1] contains the argument, which can be considered as the topological proof of the Luttinger theorem. The Green function of the ideal Fermi gas has a pole in the (E, |p|) plane (where E and p are energy and momentum). This pole is considered to be analogous to a vortex in liquid helium. Since a vortex is topologically stable against restricted perturbations, one can include interaction adiabatically (as a succession of small perturbations), and observe transformation of the Fermi gas pole to the Fermi liquid pole. In this argument, the topological stability arises already on the level of the ideal Fermi gas, which is in conflict with its Cooper instability. We discuss the origin of this controversy.
X-Learner: Learning Cross Sources and Tasks for Universal Visual Representation

In computer vision, pre-training models based on largescale supervised learning have been proven effective over the past few years. However, existing works mostly focus on learning from individual task with single data source (e.g., ImageNet for classification or COCO for detection). This restricted form limits their generalizability and usability due to the lack of vast semantic information from various tasks and data sources. Here, we demonstrate that jointly learning from heterogeneous tasks and multiple data sources contributes to universal visual representation, leading to better transferring results of various downstream tasks. Thus, learning how to bridge the gaps among different tasks and data sources is the key, but it still remains an open question. In this work, we propose a representation learning framework called X-Learner, which learns the universal feature of multiple vision tasks supervised by various sources, with expansion and squeeze stage: 1) Expansion Stage: X-Learner learns the task-specific feature to alleviate task interference and enrich the representation by reconciliation layer. 2) Squeeze Stage: X-Learner condenses the model to a reasonable size and learns the universal and generalizable representation for various tasks transferring. Extensive experiments demonstrate that X-Learner achieves strong performance on different tasks without extra annotations, modalities and computational costs compared to existing representation learning methods. Notably, a single X-Learner model shows remarkable gains of 3.0%, 3.3% and 1.8% over current pretrained models on 12 downstream datasets for classification, object detection and semantic segmentation.
NELA-Local: A Dataset of U.S. Local News Articles for the Study of County-level News Ecosystems

In this paper, we present a dataset of over 1.4M online news articles from 313 local U.S. news outlets published over 20 months (between April 4th, 2020 and December 31st, 2021). These outlets cover a geographically diverse set of communities across the United States. In order to estimate characteristics of the local audience, included with this news article data is a wide range of county-level metadata, including demographics, 2020 Presidential Election vote shares, and community resilience estimates from the U.S. Census Bureau. The NELA-Local dataset can be found at: https://dataverse.harvard. edu/dataset.xhtml? persistentId=doi:10.7910/DVN/GFE66K.
Neural RF SLAM for unsupervised positioning and mapping with channel state information

We present a neural network architecture for jointly learning user locations and environment mapping up to isometry, in an unsupervised way, from channel state information (CSI) values with no location information. The model is based on an encoder-decoder architecture. The encoder network maps CSI values to the user location. The decoder network models the physics of propagation by parametrizing the environment using virtual anchors. It aims at reconstructing, from the encoder output and virtual anchor location, the set of time of flights (ToFs) that are extracted from CSI using super-resolution methods. The neural network task is set prediction and is accordingly trained end-to-end. The proposed model learns an interpretable latent, i.e., user location, by just enforcing a physics-based decoder. It is shown that the proposed model achieves sub-meter accuracy on synthetic ray tracing based datasets with single anchor SISO setup while recovering the environment map up to 4cm median error in a 2D environment and 15cm in a 3D environment.