Miracle-dz/UniFAR-FTUs huggingface dataset:
End‐fire bidirectional circularly polarized composite antenna

A compact planar antenna with circular polarization (CP) along end fire direction is presented. End fire CP is achieved by the combination of patch equivalent electrical monopole and dipole antenna over the same substrate with a single feed. The calculated dimensions of the two antennas obtained through mathematical analysis are successful in achieving a perfect CP in the end fire direction provided a proper phase difference is maintained between them. Almost symmetrical bi‐directional radiation is obtained at both the end fire directions. Additional merits of the antenna include light weight, compactness, and simple design. The proposed antenna showed an enhanced axial ratio bandwidth of 22.2% with maximum gain of 3.54 dB. Mathematical analysis and simulation results were validated with experimental results for the given antenna. © 2017 Wiley Periodicals, Inc. Microwave Opt Technol Lett 59:1382–1388, 2017
UvA-DARE (Digital Academic Repository) The yield normal stress The yield normal stress The normal yield stress : supplementary information

Normal stresses in complex fluids lead to new flow phenomena because they can be comparable to or even larger than the shear stress itself. In addition, they are of paramount importance for formulating and testing constitutive equations for predicting non-viscometric flow behavior. Very little attention has so far been paid to the normal stresses of yield stress fluids, which are difficult to measure. We report the first systematic study of the first and second normal stress differences, N 1 (>0) and N 2 (<0), in both continuous and oscillatory shear of three model yield stress fluids. We show that both normal stress differences are quadratic functions of the shear stress both above and below the shear yield stress, leading to the existence of a yield normal stress.
O NLINE VERSUS I N PERSON M ATHEMATICS I NSTRUCTION : A C OMPARISON OF T WO I NSTRUCTIONAL M ODELS

Our paper explores the differences between online and traditional, in-person teaching and learning modalities, looking specifically at courses preparing teachers to be mathematics teacher leaders. In the context of current research on the teaching and learning of mathematics in an online setting, we share our own experiences. We describe the preparation for and teaching of online mathematics, focusing on establishing norms and the use of technology. The changing teaching and learning opportunities of the 21st century require discussion of these vital issues. We include stories of interactions between candidates and teachers and among groups of candidates in mathematics courses, detailing not just the discursive and work-sharing tools but the nature and nuance of these interactions and how they mediate mathematics learning. We share our online teaching and learning experiences, drawing on research to frame our impressions. By identifying key similarities and differences between instructional modalities and by reflecting critically on our own successes and challenges, we present a vision of online teaching and learning for mathematics courses, in particular those for mathematics specialists, that can be effective, inclusive, and relational.
Is Speech Pathology a Biomarker in Automatic Speaker Verification?

With the advancements in deep learning (DL) and an increasing interest in data-driven speech processing methods, a major challenge for speech data scientists in the healthcare domain is the anonymization of pathological speech, which is a required step to be able to make them accessible as a public training resource. In this paper, we investigate pathological speech data and compare their speaker veriﬁability with that of healthy individuals. We utilize a large pathological speech corpus of more than 2,000 test subjects with various speech and voice disorders from different ages and apply DL-based automatic speaker veriﬁcation (ASV) techniques. As a result, we obtained a mean equal error rate (EER) of 0 . 86 ± 0 . 16% , which is a factor of three lower than comparable healthy speech databases. We further perform detailed analyses of external inﬂuencing factors on ASV such as age, pathology, recording environment, and utterance length, to explore their respective effect. Our ﬁndings indicate that speech pathology is a potential biomarker in ASV. This is potentially of high interest for the anonymization of pathological speech data.
Concave and other generalizations of stochastic Gronwall inequalities

We provide nonlinear generalizations of a class of stochastic Gronwall inequalities that have been studied by von Renesse and Scheutzow (2010), Scheutzow (2013), Xie and Zhang (2020) and Mehri and Scheutzow (2021). This class of stochastic Gronwall inequalities is a useful tool for SDEs. More precisely, we study generalizations of the Bihari-LaSalle type. Whilst in a closely connected article by the author convex generalizations are studied, we investigate here concave and other generalizations. These types of estimates are useful to obtain existence and uniqueness of global solutions of path-dependent SDEs driven by L\'evy processes under one-sided non-Lipschitz monotonicity and coercivity assumptions.
Reversing Food Craving Preference Through Multisensory Exposure

Experiencing food craving is nearly ubiquitous and has several negative patho-logical impacts, but eﬀective intervention strategies to control or reverse craving remain limited. Food cue-reactivity tasks are often used to study food craving but most paradigms ignore individual food preferences, which could confound the ﬁndings. We explored the possibility of reversing food craving preference using psychophysical tasks on human participants considering their individual food preferences in a multisensory food exposure set-up. Participants were grouped into Positive Control (PC), Negative Control (NC), and Neutral Control (NEC) based on their preference for sweet and savory items. Participants reported their momentary craving of the displayed food stimuli through desire scale and bidding scale (willingness to pay) pre and post multisensory exposure. Participants were exposed to food items they either liked or disliked. Our results asserted the eﬀect of the multisensory food exposure showing statistically signiﬁcant increase in food craving for negative control post-exposure to disliked food items. Using computational model and statistical methods we also show that desire for food does not necessarily translate to willingness to pay every time and instantaneous subjective valuation of food craving is an important parameter for subsequent action. Our results further demonstrate the role of parietal N200 and centro-parietal P300 in reversing craving preference.
AGQA 2.0: An Updated Benchmark for Compositional Spatio-Temporal Reasoning

Prior benchmarks have analyzed models' answers to questions about videos in order to measure visual compositional reasoning. Action Genome Question Answering (AGQA) is one such benchmark. AGQA provides a training/test split with balanced answer distributions to reduce the effect of linguistic biases. However, some biases remain in several AGQA categories. We introduce AGQA 2.0, a version of this benchmark with several improvements, most namely a stricter balancing procedure. We then report results on the updated benchmark for all experiments.
Sapinet: A sparse event-based spatiotemporal oscillator for learning in the wild

We introduce Sapinet – a spike timing (event)-based multilayer neural network for learning in the wild – that is: one-shot online learning of multiple inputs without catastrophic forgetting, and without the need for data-speciﬁc hyperparameter retuning. Key features of Sapinet include data regularization, model scaling, data classiﬁcation, and denoising. The model also supports stimulus similarity mapping. We propose a systematic method to tune the network for performance. We studied the model performance on different levels of odor similarity, gaussian and impulse noise. Sapinet achieved high classiﬁcation accuracies on standard machine olfaction datasets without the requirement of ﬁne tuning for a speciﬁc dataset.
Transparent Shape from Single Polarization Images

This paper presents a data-driven approach for transparent shape from polarization. Due to the inherent high transmittance, the previous shape from polarization(SfP) methods based on specular reflection model have difficulty in estimating transparent shape, and the lack of datasets for transparent SfP also limits the application of the data-driven approach. Hence, we construct the transparent SfP dataset which consists of both synthetic and real-world datasets. To determine the reliability of the physics-based reflection model, we define the physics-based prior confidence by exploiting the inherent fault of polarization information, then we propose a multi-branch fusion network to embed the confidence. Experimental results show that our approach outperforms other SfP methods. Compared with the previous method, the mean and median angular error of our approach are reduced from 19 . 00 ◦ and 14 . 91 ◦ to 16 . 72 ◦ and 13 . 36 ◦ , and the accuracy 11 . 25 ◦ , 22 . 5 ◦ , 30 ◦ are improved from 38 . 36% , 77 . 36% , 87 . 48% to 45 . 51% , 78 . 86% , 89 . 98%, respectively. Our dataset and code will be publicly available at https://github.com/shaomq2187/TransSfP.
Finding and Analyzing Crash-Consistency Bugs in Persistent-Memory File Systems

We present a study of crash-consistency bugs in persistent-memory (PM) file systems and analyze their implications for file-system design and testing crash consistency. We develop FlyTrap, a framework to test PM file systems for crash-consistency bugs. FlyTrap discovered 18 new bugs across four PM file systems; the bugs have been confirmed by developers and many have been already fixed. The discovered bugs have serious consequences such as breaking the atomicity of rename or making the file system unmountable. We present a detailed study of the bugs we found and discuss some important lessons from these observations. For instance, one of our findings is that many of the bugs are due to logic errors, rather than errors in using flushes or fences; this has important applications for future work on testing PM file systems. Another key finding is that many bugs arise from attempts to improve efficiency by performing metadata updates in-place and that recovery code that deals with rebuilding in-DRAM state is a significant source of bugs. These observations have important implications for designing and testing PM file systems. Our code is available at https://github.com/utsaslab/flytrap .
Post Occupancy Evaluation an Academic Building: Lessons to Learn

Post occupancy evaluation (POE) is a tool used to determine the users’ satisfaction of a building after its completion. Most educational facilities do not conduct a post occupancy evaluation due to the absence of it in the contract agreement between the institution and architect. The client often chooses to bypass the POE to reduce the cost of the project. The purpose of this research is to conduct a POE of an academic building and identify areas of user dissatisfaction in order to enhance future projects. An academic building on a major, Midwestern university campus was remodeled during the academic year of 2014-15 followed by a POE with end users (students, faculty, and staff). The building houses the Dean’s office and two departments with a total of approximately 1400 Students and 65 Full-time faculty and staff. Personal interviews were conducted with the staff of the architectural firm responsible for the project (architect, Interior designer, and construction manager), and the university facility planning director and architect. Interviews and walk-throughs were also conducted with program directors and chairpersons. A questionnaire in three different formats to accommodate different majors was distributed to 750 students and 45 faculty and staff. Specialized areas included a student operated restaurant and kitchen, as well as interior design studios and apparel design labs. Overall, users were satisfied with the remodeled building with the exception of a few minor issues such as navigation through the building and temperature.
Scalable Training of Language Models using JAX pjit and TPUv4

Modern large language models require distributed training strategies due to their size. The challenges of efficiently and robustly training them are met with rapid developments on both software and hardware frontiers. In this technical report, we explore challenges and design decisions associated with developing a scalable training framework, and present a quantitative analysis of efficiency improvements coming from adopting new software and hardware solutions.
Techtile: a Flexible Testbed for Distributed Acoustic Indoor Positioning and Sensing

The proposed infrastructure, named Techtile, provides a unique R&D facility as features dispersed electronics enables transmission and capturing of a multitude of signals in 3D. Specific available equipment that enhances the design process from smooth prototyping to a commercial product is discussed. The acoustic parameters of the room, particularly the reverberation and ambient noise, are measured to take these into account for future innovative acoustic indoor positioning and sensing systems. This can have a positive influence on the accuracy and precision. The wooden construction represents an acoustically challenging room for audible sound with a maximum measured RT60 value of 1.17s at 5kHz, which is comparable to the reverberation properties of a lecture or small concert hall. For ultrasound it is rather challenging due to the present ambient noise sources. In general, the Techtile room can be compared with a home or quiet office environment, in terms of sound pressure levels (SPLs). In addition to the acoustic properties, possible research and development options are discussed in combination with the associated challenges. Many of the designs described are available through open source.
Simultaneous Lane-Keeping and Obstacle Avoidance by Combining Model Predictive Control and Control Barrier Functions

We combine Model Predictive Control (MPC) and Control Barrier Function (CBF) design methods to create a hierarchical control law for simultaneous lane-keeping (LK) and obstacle avoidance (OA): at the low level, MPC performs LK via trajectory tracking during nominal operation; and at the high level, various CBF-based safety filters that ensure LK and OA are designed and compared across practical scenarios. In particular, we show that Exponential Safety (ESf) and Prescribed-Time Safety (PTSf) filters, which override the MPC control when necessary, result in feasible Quadratic Programs when OA-safety is prioritized. We additionally investigate control designs subject to input constraints by using Input-Constrained-CBFs. Finally, we compare the performance of combinations of ESf, PTSf, and their input-constrained counterparts with respect to the LK and OA goals in two simulation studies for early- and late-detected obstacle scenarios.
CoDGraD: A Code-based Distributed Gradient Descent Scheme for Decentralized Convex Optimization

In this paper, we consider a large network containing many regions such that each region is equipped with a worker with some data processing and communication capability. For such a network, some workers may become stragglers due to the failure or heavy delay on computing or communicating. To resolve the above straggling problem, a coded scheme that introduces certain redundancy for every worker was recently proposed, and a gradient coding paradigm was developed to solve convex optimization problems when the network has a centralized fusion center. In this paper, we propose an iterative distributed algorithm, referred as Code-Based Distributed Gradient Descent algorithm (CoDGraD), to solve convex optimization problems over distributed networks. In each iteration of the proposed algorithm, an active worker shares the coded local gradient and approximated solution of the convex optimization problem with non-straggling workers at the adjacent regions only. In this paper, we also provide the consensus and convergence analysis for the CoDGraD algorithm and we demonstrate its performance via numerical simulations.
Galaxy rotation favors prolate dark matter haloes

The flattening rotation velocity $v(r)\to {\rm constant}$ found by Vera Rubin and collaborators and very apparent in the SPARC galaxy-rotation data coincides with Kepler's law in one less dimension. Thus, it is naturally reproduced by elongated dark matter distributions with the axis of prolateness perpendicular to the galactic plane. This theoretical understanding is borne out by the detailed fits to the rotation data that we here report: for equal dark matter profile, elongated distributions provide smaller $\chi^2$ than purely spherical ones. We also propose to use the geometric mean of the individual halo ellipticities, as opposed to their arithmetic average, because $s=c/a\in (0,\infty)$ corresponds to spherical haloes for $s=1$, so that the usually reported average is skewed towards oblateness and fails to reveal the large majority of prolate haloes. Several independently coded fitting exercises concur in yielding $s<1$ for most of the database entries and the oblate exceptions are understood and classified. This likely prolateness is of consequence for the estimated dark matter density near Earth.
Overparameterized Linear Regression Under Adversarial Attacks

We study the error of linear regression in the face of adversarial attacks. In this framework, an adversary changes the input to the regression model in order to maximize the prediction error. We provide bounds on the prediction error in the presence of an adversary as a function of the parameter norm and the error in the absence of such an adversary. We show how these bounds make it possible to study the adversarial error using analysis from non-adversarial setups. The obtained results shed light on the robustness of overparameterized linear models to adversarial attacks. Adding features might be either a source of additional robustness or brittleness. On the one hand, we use asymptotic results to illustrate how double-descent curves can be obtained for the adversarial error. On the other hand, we derive conditions under which the adversarial error can grow to infinity as more features are added, while at the same time, the test error goes to zero. We show this behavior is caused by the fact that the norm of the parameter vector grows with the number of features. It is also established that <inline-formula><tex-math notation="LaTeX">$\ell _\infty$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\ell _{2}$</tex-math></inline-formula>-adversarial attacks might behave fundamentally differently due to how the <inline-formula><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\ell _{2}$</tex-math></inline-formula>-norms of random projections concentrate. We also show how our reformulation allows for solving adversarial training as a convex optimization problem. This fact is then exploited to establish similarities between adversarial training and parameter-shrinking methods and to study how the training might affect the robustness of the estimated models.
Are fast scramblers good thermal baths?

The Sachdev-Ye-Kitaev (SYK 4 ) model has attracted attention for its fast scrambling properties and its thermalization rate that is set only by the temperature. In this work we ask the question of whether the SYK 4 model is also a good thermal bath, in the sense that it allows a system coupled to it to thermalize. We address this question by considering the dynamics of a system of N random non-interacting Majorana fermions coupled to an SYK 4 bath with M Majorana fermions that we solve with Keldysh techniques in the limit of M (cid:29) N (cid:29) 1 . We compare this nonequilibrium setting with a conventional bath made of free non-interacting degrees of freedom with a continous spectrum. We show that the SYK 4 bath is more eﬃcient in thermalising the system at weak coupling, due to its enhanced density of states at low frequency, while at strong system-bath couplings both type of environments give rise to a similar time scale for thermalisation.
CUNI-KIT System for Simultaneous Speech Translation Task at IWSLT 2022

In this paper, we describe our submission to the Simultaneous Speech Translation at IWSLT 2022. We explore strategies to utilize an offline model in a simultaneous setting without the need to modify the original model. In our experiments, we show that our onlinization algorithm is almost on par with the offline setting while being 3x faster than offline in terms of latency on the test set. We also show that the onlinized offline model outperforms the best IWSLT2021 simultaneous system in medium and high latency regimes and is almost on par in the low latency regime. We make our system publicly available.
Coarse graining empirical densities and currents in continuous-space steady states

We present the conceptual and technical background required to describe and understand the correlations and fluctuations of the empirical density and current of steady-state diffusion processes on all time scales -- observables central to statistical mechanics and thermodynamics on the level of individual trajectories. We focus on the important and non-trivial effect of a spatial coarse graining. Making use of a generalized time-reversal symmetry we provide deeper insight about the physical meaning of fluctuations of the coarse-grained empirical density and current, and explain why a systematic variation of the coarse-graining scale offers an efficient method to infer bounds on a system's dissipation. Moreover, we discuss emerging symmetries in the statistics of the empirical density and current, and the statistics in the central-limit regime. More broadly our work promotes the application of stochastic calculus as a powerful direct alternative to Feynman-Kac theory and path-integral methods.
SpoofGAN: Synthetic Fingerprint Spoof Images

A major limitation to advances in fingerprint presentation attack detection (PAD) is the lack of publicly available, large-scale datasets, a problem which has been compounded by increased concerns surrounding privacy and security of biometric data. Furthermore, most state-of-the-art PAD algorithms rely on deep networks which perform best in the presence of a large amount of training data. This work aims to demonstrate the utility of synthetic (both bona fide and PA style) fingerprints in supplying these algorithms with sufficient data to improve the performance of fingerprint PAD algorithms beyond the capabilities when training on a limited amount of publicly available “real” datasets. First, we provide details of our approach in modifying a state-of-the-art generative architecture to synthesize high quality bona fide and PA fingerprints. Then, we provide quantitative and qualitative analysis to verify the quality of our synthetic fingerprints in mimicking the distribution of real data samples. We showcase the utility of our synthetic bona fide and PA fingerprints in training a deep network for fingerprint PAD, which dramatically boosts the performance across three different evaluation datasets compared to an identical model trained on real data alone. Finally, we demonstrate that only 25% of the original (real) dataset is required to obtain similar detection performance when augmenting the training dataset with synthetic data. We make our synthetic dataset and model publicly available to encourage further research on this topic: https://github.com/groszste/SpoofGAN.