Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual-wavelength generation and tuning by controlling the apodized grating depth in microring resonators

Here we show a photonic design for tunable dual-wavelength generation deploying optical nonlinear mode coupling of two coupled III-V semiconductor microring resonators (MRRs) connected to a pump and drop waveguide buses. Here one of the two rings contains a grating, while the other has a planar surface. The underling mechanism for the dual wavelength generation originates from the resonance-detuning of the spectra resulting in non-linear mode mixing. Tunability of the wavelengths is achieved by altering the grating depth of the MRR and the power coupling coefficients. For the grating design of the MRR we select a trapezoidal-profiled apodized grating to gain low reflectivity at sidelobes. A time-domain travelling wave (TDTW) analysis gives a InGaAsP core refractive index of 3.3 surrounded by a grating InP cladding with n=3.2. We further confirm that the propagation of a Gaussian pulse input with 10 mW power and bandwidth of 0.76 ps is well confined within the mode propagation of the system. Taken together our results show a 2:1 fan-out of two spectrally separate signals for compact and high functional sources on chip.
Intercultural competence in engineering education: who are we teaching?

There is little doubt that our engineering graduates’ ability to identify cultural differences and their potential to impact on engineering projects, and to work effectively with these differences is of key importance in the modern engineering practice. Within engineering degree programs themselves there is also a significant need to recognise the impact of changing student and staff profiles on what happens in the classroom. The research described in this paper forms part of a larger project exploring issues of intercultural competence in engineering. This paper presents an observational and survey study of undergraduate and postgraduate engineering students from four institutions working in groups on tasks with a purely technical focus, or with a cultural and humanitarian element. The study sought to explore how students rate their own intercultural competence and team process and whether any differences exist depending on the nature of the task they are working on. We also investigated whether any differences were evident between groups of first year, second year and postgraduate students. The study used the miniCQS instrument & Van Dyne, 2008) and a Bales Interaction Process Analysis based scale 1950; Carney, 1976) to collect students self ratings of group process, task management, and cultural experience and behaviour. The Bales IPA was also used for coding video observations of students working in groups. Survey data were used to form descriptive variables to compare outcomes across the different tasks and contexts. Observations analysed in Nvivo were used to provide commentary and additional detail on the quantitative data. offering some basic insights into how task and student group composition could affect self ratings. Overall though, the results suggest minimal shift in how students view group function and their intercultural experience, irrespective of differing educational experience. The survey results, contrasted with group observations, indicate that either students are not translating their experience (in the group tasks) into critical self assessment of their cultural competence and teamwork, or that they become more critical of team performance and cultural competence as their competence in these areas grows, so their ratings remain consistent. Both outcomes indicate that students need more intensive guidance to build their critical self and peer assessment skills in these areas irrespective of their year level of study.
Analysis of two Binomial Proportions in Non-inferiority Confirmatory Trials

In this paper, we propose considering an exact likelihood score (ELS) test for non-inferiority comparison and we derive its test-based confidence interval for the difference between two independent binomial proportions. The p-value for this test is obtained by using exact binomial probabilities with the nuisance parameter being replaced by its restricted maximum likelihood estimate. Calculated type I errors revealed that the proposed ELS method has important advantages for non-inferiority comparisons over popular asymptotic methods for adequately powered confirmatory clinical trials, at 80% or 90% statistical power. For unbalanced sample sizes of the compared treatment groups, the type I errors for the asymptotic score method were shown to be higher than the nominal level in a systematic pattern over a range of the true proportions, but the ELS method did not suffer from such a problem. On average, the true type I error of the ELS method was closer to the nominal level than all considered methods in the empirical comparisons. Also, in rare cases, the type I errors of the ELS test exceeded the nominal level, but only by a small amount. In addition, the p-value and confidence interval using the ELS method can be obtained in less than 30 seconds of computer time for most confirmatory trials. The theoretical arguments and the attractive empirical evidence, along with fast computation time, should make the ELS method very attractive for consideration in statistical practice.
On the Age of Information for AMP based Grant-Free Random Access

With the rapid development of Internet of Things (IoT), massive devices are deployed, which poses severe chal-lenges on access networks due to limited communication re-sources. When massive users contend for access, the information freshness gets worse caused by increasing collisions. It could be fatal for information freshness sensing scenarios, such as remote monitoring systems or self-driving systems, in which information freshness plays a critical part. In this paper, by taking the Age of Information (AoI) as the primary performance indicator, the information freshness using approximate message passing (AMP) based grant-free scheme is investigated and compared with grant-based scheme. Base on the analysis, a user scheduling strategy with sleep threshold and forcing active threshold is proposed to further reduce average AoI (AAoI). Numerical results reveal that the AMP-based grant-free scheme can provide sufficient access capability with less pilot resources, and it is robust to the fluctuation of the number of active users. That ensures that the AMP-based grant-free scheme can keep the AAoI at a low level. It is also shown that the proposed threshold strategy can effectively improve the information freshness.
On the Convergence of the Backward Reachable Sets of Robust Controlled Invariant Sets For Discrete-time Linear Systems

This paper considers discrete-time linear systems with bounded additive disturbances, and studies the convergence properties of the backward reachable sets of robust controlled invariant sets (RCIS). Under a simple condition, we prove that the backward reachable sets of an RCIS are guaranteed to converge to the maximal RCIS in Hausdorff distance, with an exponential convergence rate. When all sets are represented by polytopes, this condition can be checked numerically via a linear program. We discuss how the developed condition generalizes the existing conditions in the literature for (controlled) invariant sets of systems without disturbances (or without control inputs).
On the modified $J$-equation

In this paper, we study the modiﬁed J -equation introduced by Li–Shi [LS16]. We show that the solvability of the modiﬁed J -equation is equivalent to the coercivity of the modiﬁed J -functional on general compact K¨ahler manifolds. On toric manifolds we give a numerical necessary and suﬃcient condition for the existence of solutions, extending the results of Collins–Sz´ekelyhidi [CS17]. Nakai–Moishezon criterion.
Self-attention on Multi-Shifted Windows for Scene Segmentation

Scene segmentation in images is a fundamental yet challenging problem in visual content understanding, which is to learn a model to assign every image pixel to a categorical label. One of the challenges for this learning task is to consider the spatial and semantic relationships to obtain descriptive feature representations, so learning the feature maps from multiple scales is a common practice in scene segmentation. In this paper, we explore the effective use of self-attention within multi-scale image windows to learn descriptive visual features, then propose three different strategies to aggregate these feature maps to decode the feature representation for dense prediction. Our design is based on the recently proposed Swin Transformer models, which totally discards convolution operations. With the simple yet effective multi-scale feature learning and aggregation, our models achieve very promising performance on four public scene segmentation datasets, PASCAL VOC2012, COCO-Stuff 10K, ADE20K and Cityscapes.
Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations

In this paper, we show that recent advances in self-supervised feature learning enable unsupervised object discovery and semantic segmentation with a performance that matches the state of the field on supervised semantic segmentation 10 years ago. We propose a methodology based on unsupervised saliency masks and self-supervised feature clustering to kickstart object discovery followed by training a semantic segmentation network on pseudo-labels to bootstrap the system on images with multiple objects. We present results on PASCAL VOC that go far beyond the current state of the art (50.0 mIoU), and we report for the first time results on MS COCO for the whole set of 81 classes: our method discovers 34 categories with more than $20\%$ IoU, while obtaining an average IoU of 19.6 for all 81 categories.
Not all broken defenses are equal: The dead angles of adversarial accuracy

Robustness to adversarial attack is typically evaluated with adversarial accuracy. This metric is however too coarse to properly capture all robustness properties of machine learning models. Many defenses, when evaluated against a strong attack, do not provide accuracy improvements while still con-tributing partially to adversarial robustness. Popular certiﬁcation methods suffer from the same issue, as they provide a lower bound to accuracy. To capture ﬁner robustness properties we propose a new metric for L 2 robustness, adversarial angular sparsity , which partially answers the question “how many adversarial examples are there around an in-put”. We demonstrate its usefulness by evaluating both “strong” and “weak” defenses. We show that some state-of-the-art defenses, delivering very similar accuracy, can have very different sparsity on the inputs that they are not robust on. We also show that some weak defenses actually decrease robustness, while others strengthen it in a measure that accuracy cannot capture. These differences are predictive of how useful such defenses can become when combined with adversarial training.
Hierarchical Average Precision Training for Pertinent Image Retrieval

Image Retrieval is commonly evaluated with Average Precision (AP) or Recall@k. Yet, those metrics, are limited to binary labels and do not take into account errors' severity. This paper introduces a new hierarchical AP training method for pertinent image retrieval (HAP-PIER). HAPPIER is based on a new H-AP metric, which leverages a concept hierarchy to refine AP by integrating errors' importance and better evaluate rankings. To train deep models with H-AP, we carefully study the problem's structure and design a smooth lower bound surrogate combined with a clustering loss that ensures consistent ordering. Extensive experiments on 6 datasets show that HAPPIER significantly outperforms state-of-the-art methods for hierarchical retrieval, while being on par with the latest approaches when evaluating fine-grained ranking performances. Finally, we show that HAPPIER leads to better organization of the embedding space, and prevents most severe failure cases of non-hierarchical methods. Our code is publicly available at: https://github.com/elias-ramzi/HAPPIER.
TensorIR: An Abstraction for Automatic Tensorized Program Optimization

Deploying deep learning models on various devices has become an important topic. The wave of hardware specialization brings a diverse set of acceleration primitives for multi-dimensional ten- sor computations. These new acceleration primitives, along with the emerging machine learning models, bring tremendous engineering challenges. In this paper, we present TensorIR, a compiler abstraction for optimizing programs with these tensor computation primitives. TensorIR generalizes the loop nest representation used in existing machine learning compilers to bring tensor computation as the first-class citizen. Finally, we build an end-to-end framework on top of our abstraction to automatically optimize deep learning models for given tensor computation primitives. Experimental results show that TensorIR compilation automatically uses the tensor computation primitives for given hardware backends and delivers performance that is competitive to state-of-art hand-optimized systems across platforms.
Finding r-II Sibling Stars in the Milky Way with the Greedy Optimistic Clustering Algorithm

R-process enhanced stars with [Eu/Fe] ≥ +0.7 (so-called r-II stars) are believed to have formed in an extremely neutron-rich environment in which a rare astrophysical event (e.g., a neutron-star merger) occurred. This scenario is supported by the existence of an ultra-faint dwarf galaxy, Reticulum II, where most of the stars are highly enhanced in r-process elements. In this scenario, some small fraction of dwarf galaxies around the Milky Way were r enhanced. When each r-enhanced dwarf galaxy accreted to the Milky Way, it deposited many r-II stars in the Galactic halo with similar orbital actions. To search for the remnants of the r-enhanced systems, we analyzed the distribution of the orbital actions of N = 161 r-II stars in the solar neighborhood by using Gaia EDR3 data. Since the observational uncertainty is not negligible, we applied a newly developed greedy optimistic clustering method to the orbital actions of our sample stars. We found six clusters of r-II stars that have similar orbits and chemistry, one of which is a new discovery. Given the apparent phase-mixed orbits of the member stars, we interpret that these clusters are good candidates for remnants of completely disrupted r-enhanced dwarf galaxies that merged with the ancient Milky Way.
A Physical Model for the UV/Optical Power Spectra of AGN

The UV/optical variability of active galactic nuclei (AGN) has long been thought to be driven by the X-ray illumination of the accretion disk. However, recent multiwavelength campaigns of nearby Seyfert galaxies seem to challenge this paradigm, with an apparent discrepancy between observations and the underlying theory. In order to further probe the connection between the UV/optical and X-ray variability in AGN, we developed a physical model to reproduce the UV/optical power spectral densities (PSDs) of AGN assuming the thermal reprocessing of the X-rays in the disk. This model offers a novel way to probe the innermost regions of AGN. We use our model to study the variability of NGC 5548, and we infer that the X-ray and UV/optical PSDs as well as the interband UV/optical time lags are all well reproduced. We also derive constraints on the source physical parameters, such as the X-ray corona height and the accretion rate. Our results suggest that X-ray disk reprocessing accounts for the full variability properties of this AGN, within the considered timescales. Using earlier data of NGC 5548, we also show that our model can reproduce its PSD in different epochs, establishing the feasibility of using PSD modeling to investigate the time evolution of a source.
SHREC'22 Track: Sketch-Based 3D Shape Retrieval in the Wild

Sketch-based 3D shape retrieval (SBSR) is an important yet challenging task, which has drawn more and more attention in recent years. Existing approaches address the problem in a restricted setting, without appropriately simulating real application scenarios. To mimic the realistic setting, in this track, we adopt large-scale sketches drawn by amateurs of different levels of drawing skills, as well as a variety of 3D shapes including not only CAD models but also models scanned from real objects. We define two SBSR tasks and construct two benchmarks consisting of more than 46,000 CAD models, 1,700 realistic models, and 145,000 sketches in total. Four teams participated in this track and submitted 15 runs for the two tasks, evaluated by 7 commonly-adopted metrics. We hope that, the benchmarks, the comparative results, and the open-sourced evaluation code will foster future research in this direction among the 3D object retrieval community.
Interactive Recommendations for Optimal Allocations in Markets with Constraints

Recommendation systems when employed in markets play a dual role: they assist users in selecting their most desired items from a large pool and they help in allocating a limited number of items to the users who desire them the most. Despite the prevalence of capacity constraints on allocations in many real-world recommendation settings, a principled way of incorporating them in the design of these systems has been lacking. Motivated by this, we propose an interactive framework where the system provider can enhance the quality of recommendations to the users by opportunistically exploring allocations that maximize user rewards and respect the capacity constraints using appropriate pricing mechanisms. We model the problem as an instance of a low-rank combinatorial multi-armed bandit problem with selection constraints on the arms. We employ an integrated approach using techniques from collaborative ﬁltering, combinatorial bandits, and optimal resource allocation to provide an algorithm that provably achieves sub-linear regret, namely (cid:101) O ( (cid:112) NM ( N + M ) RT ) in T rounds for a problem with N users, M items and rank R mean reward matrix. Empirical studies on synthetic and real-world data also demonstrate the effectiveness and performance of our approach.
Seasonal Encoder-Decoder Architecture for Forecasting

Deep learning (DL) in general and Recurrent neural networks (RNNs) in particular have seen high success levels in sequence based applications. This paper pertains to RNNs for time series modelling and forecasting. We propose a novel RNN architecture capturing (stochastic) seasonal correlations intelligently while capable of accurate multi-step forecasting. It is motivated from the well-known encoder-decoder (ED) architecture and multiplicative seasonal auto-regressive model. It incorporates multi-step (multi-target) learning even in the presence (or absence) of exogenous inputs. It can be employed on single or multiple sequence data. For the multiple sequence case, we also propose a novel greedy recursive procedure to build (one or more) predictive models across sequences when per-sequence data is less. We demonstrate via extensive experiments the utility of our proposed architecture both in single sequence and multiple sequence scenarios.
Almost optimum $\ell$-covering of $\mathbb{Z}_n$

A subset $B$ of ring $\mathbb{Z}_n$ is called a $\ell$-covering set if $\{ ab \pmod n \mid 0\leq a \leq \ell, b\in B\} = \mathbb{Z}_n$. We show there exists a $\ell$-covering set of $\mathbb{Z}_n$ of size $O(\frac{n}{\ell}\log n)$ for all $n$ and $\ell$, and how to construct such set. We also show examples where any $\ell$-covering set must have size $\Omega(\frac{n}{\ell}\frac{\log n}{\log \log n})$. The proof uses a refined bound for relative totient function obtained through sieve theory, and existence of a large divisor with linear divisor sum.
Concatenation of the Gottesman-Kitaev-Preskill code with the XZZX surface code

Bosonic codes provide an alternative option for quantum error correction. An important category of bosonic codes called the Gottesman-Kitaev-Preskill (GKP) code has aroused much interest recently. Theoretically, the error correction ability of GKP code is limited since it can only correct small shift errors in position and momentum quadratures. A natural approach to promote the GKP error correction for large-scale, fault-tolerant quantum computation is concatenating encoded GKP states with a stabilizer code. The performance of the XZZX surface-GKP code, i.e., the single-mode GKP code concatenated with the XZZX surface code is investigated in this paper under two different noise models. Firstly, in the code-capacity noise model, the asymmetric rectangular GKP code with parameter $\lambda$ is introduced. Using the minimum weight perfect matching decoder combined with the continuous-variable GKP information, the optimal threshold of the XZZX-surface GKP code reaches $\sigma\approx0.67$ when $\lambda=2.1$, compared with the threshold $\sigma\approx0.60$ of the standard surface-GKP code. Secondly, we analyze the shift errors of two-qubit gates in the actual implementation and build the full circuit-level noise model. By setting the appropriate bias parameters, the logical error rate is reduced by several times in some cases. These results indicate the XZZX surface-GKP codes are more suitable for asymmetric concatenation under the general noise models. We also estimate the overhead of the XZZX-surface GKP code which uses about 291 GKP states with the noise parameter 18.5 dB ($\kappa/g \approx 0.71\%$) to encode a logical qubit with the error rate $2.53\times10^{-7}$, compared with the qubit-based surface code using 3041 qubits to achieve almost the same logical error rate.
Configuration spaces, graded spaces, and polysymmetric functions

This paper presents techniques for computing motivic measures of configuration spaces when points of the base space are weighted. This generality allows for a more flexible notion of configuration space which can be useful in applications, and subsumes some disparate problems that were previously given ad-hoc treatments. The space of effective zero-cycles of given weight generalizes the ordinary symmetric product and has a canonical stratification by splitting type. We prove formulas for the motivic measures of open and closed strata of the space of zero-cycles of given weight on a quasiprojective variety with an arbitrary grading by natural numbers. This lets us compute, among other things, the motive of the space of geometrically irreducible hypersurfaces in projective space. We introduce a general form of plethysm suitable for the graded situation which makes use of a new generalization of symmetric functions. Our main formula solves a motivic formulation of the inverse problem to Pólya enumeration.
Submodular Dominance and Applications

In submodular optimization we often deal with the expected value of a submodular function f on a distribution D over sets of elements. In this work we study such submodular expectations for negatively dependent distributions. We introduce a natural notion of negative dependence, which we call Weak Negative Regression (WNR), that generalizes both Negative Association and Negative Regression. We observe that WNR distributions satisfy Submodular Dominance , whereby the expected value of f under D is at least the expected value of f under a product distribution with the same element-marginals. Next, we give several applications of Submodular Dominance to submodular optimization. In particular, we improve the best known submodular prophet inequalities, we develop new rounding techniques for polytopes of set systems that admit negatively dependent distributions, and we prove existence of contention resolution schemes for WNR distributions.