Miracle-dz/UniFAR-FTUs huggingface dataset:
High-Fidelity Quantum Control by Polychromatic Pulse Trains.

We introduce a quantum control technique using polychromatic pulse trains, consisting of pulses with different carrier frequencies, i.e., different detunings with respect to the qubit transition frequency. We derive numerous polychromatic pulse trains, which generate broadband, narrowband, and passband excitation profiles for different target transition probabilities. This makes it possible to create high-fidelity excitation profiles which are either (i) robust to deviations in the experimental parameters, which is attractive for quantum computing, or (ii) more sensitive to such variations, which is attractive for crosstalk elimination and quantum sensing. The method is demonstrated experimentally using one of IBM's superconducting quantum processors, in a very good agreement between theory and experiment. These results demonstrate both the excellent coherence properties of the IBM qubits and the accuracy, robustness, and flexibility of the proposed quantum control technique. They also show that the detuning is a control parameter which is as efficient as the pulse phase that is commonly used in composite pulses. Hence the method opens a variety of perspectives for quantum control in areas where phase manipulation is difficult or inaccurate.
ZETAR: Modeling and Computational Design of Strategic and Adaptive Compliance Policies

Compliance management plays an important role in mitigating insider threats. Incentive design is a proactive and non-invasive approach to achieving compliance by aligning an insider's incentive with the defender's security objective, which motivates (rather than commands) an insider to act in the organization's interests. Controlling insiders' incentives for population-level compliance is challenging because they are neither precisely known nor directly controllable. To this end, we develop ZETAR, a zero-trust audit and recommendation framework, to provide a quantitative approach to model insiders' incentives and design customized recommendation policies to improve their compliance. We formulate primal and dual convex programs to compute the optimal bespoke recommendation policies. We create the theoretical underpinning for understanding trust, compliance, and satisfaction, which leads to scoring mechanisms of how compliant and persuadable an insider is. After classifying insiders as malicious, self-interested, or amenable based on their incentive misalignment levels with the defender, we establish bespoke information disclosure principles for these insiders of different incentive categories. We identify the policy separability principle and the set convexity, which enable finite-step algorithms to efficiently learn the Completely Trustworthy (CT) policy set when insiders' incentives are unknown. Finally, we present a case study to corroborate the design. Our results show that ZETAR can well adapt to insiders with different risk and compliance attitudes and significantly improve compliance. Moreover, trustworthy recommendations can provably promote cyber hygiene and insiders' satisfaction.
Multilinguals at SemEval-2022 Task 11: Transformer Based Architecture for Complex NER

We investigate the task of complex NER for the English language. The task is non-trivial due to the semantic ambiguity of the textual structure and the rarity of occurrence of such entities in the prevalent literature. Using pre-trained language models such as BERT, we obtain a competitive performance on this task. We qualitatively analyze the performance of multiple architectures for this task. All our models are able to outperform the baseline by a significant margin. Our best performing model beats the baseline F1-score by over 9%.
Origin of supermassive black holes in massive metal-poor protoclusters

While large numbers of supermassive black holes have been detected at z > 6, their origin is still essentially unclear. Numerical simulations have shown that the conditions for the classical direct collapse scenario are very restrictive and fragmentation is very difficult to be avoided. We thus consider here a more general case of a dense massive protostar cluster at low metallicity (≲ 10−3 Z⊙) embedded in gas. We estimate the mass of the central massive object, formed via collisions and gas accretion, considering the extreme cases of a logarithmically flat and a Salpeter-type initial mass function. Objects with masses of at least 104 M⊙ could be formed for inefficient radiative feedback, whereas ∼103 M⊙ objects could be formed when the accretion time is limited via feedback. These masses will vary depending on the environment and could be considerably larger, particularly due to the continuous infall of gas into the cloud. As a result, one may form intermediate mass black holes of ∼104 M⊙ or more. Upcoming observations with the James Webb Space Telescope (JWST) and other observatories may help to detect such massive black holes and their environment, thereby shedding additional light on such a formation channel.
CalmResponses: Displaying Collective Audience Reactions in Remote Communication

We propose a system displaying audience eye gaze and nod reactions for enhancing synchronous remote communication. Recently, we have had increasing opportunities to speak to others remotely. In contrast to offline situations, however, speakers often have difficulty observing audience reactions at once in remote communication, which makes them feel more anxious and less confident in their speeches. Recent studies have proposed methods of presenting various audience reactions to speakers. Since these methods require additional devices to measure audience reactions, they are not appropriate for practical situations. Moreover, these methods do not present overall audience reactions. In contrast, we design and develop CalmResponses, a browser-based system which measures audience eye gaze and nod reactions only with a built-in webcam and collectively presents them to speakers. The results of our two user studies indicated that the number of fillers in speaker’s speech decreases when audiences’ eye gaze is presented, and their self-rating score increases when audiences’ nodding is presented. Moreover, comments from audiences suggested benefits of CalmResponses for them in terms of co-presence and privacy concerns.
Leaderless Population Protocols Decide Double-exponential Thresholds

Population protocols are a model of distributed computation in which finite-state agents interact randomly in pairs. A protocol decides for any initial configuration whether it satisfies a fixed property, specified as a predicate on the set of configurations. The state complexity of a predicate is smallest number of states of any protocol deciding that predicate. For threshold predicates of the form $x\ge k$, with $k$ constant, prior work has shown that they have state complexity $\Theta(\log\log k)$ if the protocol is extended with leaders. For ordinary protocols it is only known to be in $\Omega(\log\log k)\cap \mathcal{O}(\log k)$. We close this remaining gap by showing that it is $\Theta(\log\log k)$ as well, i.e. we construct protocols with $\mathcal{O}(n)$ states deciding $x\ge k$ with $k\ge2^{2^n}$.
Cosmological Lithium Solution from Discrete Gauged Baryon Minus Lepton Number

The cosmological lithium problem -- that theory predicts a primordial abundance far higher than the observed value -- has resisted decades of attempts by cosmologists, nuclear physicists, and astronomers alike to root out systematics. We reconsider this problem in the setting of the Standard Model extended by gauged baryon minus lepton number, which we spontaneously break by a scalar with charge $2 N_c N_g$. Cosmic strings from this breaking can support interactions converting three protons into three positrons, and we argue that an `electric'-`magnetic' interplay can give this process an amplified, strong-scale cross-section in an analogue of the Callan-Rubakov effect. We suggest such cosmic strings have disintegrated $\mathcal{O}(1)$ of the primordial lithium nuclei, and lay out what is necessary for this scheme to succeed. To our knowledge this is the first new physics mechanism with microphysical justification for the abundance of lithium uniquely to be modified after Big Bang Nucleosynthesis.
ON THE IDEMPOTENT CONJECTURE FOR SIDKI DOUBLES

— Let G be a group and X ( G ) its Sidki Double. The idempotent conjecture says that there should be no non-trivial idempotent in the complex group ring of a torsion-free group. We investigate this conjecture for the Sidki double of a torsion-free group, and obtain a partial result.
Baby Universes born from the Void

: We propose a novel construction of a third quantised baby universe Hilbert space H BU for the quantum gravity path integral. In contrast to the original description of α -parameters, both the bulk and boundary microscopic parameters are ﬁxed in our proposal. Wormholes and baby universes appear due to reﬁned observables, of the boundary dual quantum ﬁeld theories, that crucially involve the space of representations of the boundary gauge group. Irreducible representations, on which the path integral factorises, give rise to ﬁeld theoretic superselection sectors and replace the α states.
Inverse Problems Are Solvable on Real Number Signal Processing Hardware

Despite the success of Deep Learning (DL) serious reliability issues such as non-robustness persist. An interesting aspect is, whether these problems arise due to insufficient tools or due to fundamental limitations of DL. We study this question from the computability perspective, i.e., we characterize the limits imposed by the applied hardware. For this, we focus on the class of inverse problems, which, in particular, encompasses any task to reconstruct data from measurements. On digital hardware, a conceptual barrier on the capabilities of DL for solving finite-dimensional inverse problems has in fact already been derived. This paper investigates the general computation framework of Blum-Shub-Smale (BSS) machines which allows the processing and storage of arbitrary real values. Although a corresponding real world computing device does not exist at the moment, research and development towards real number computing hardware, usually referred to by the term"neuromorphic computing", has increased in recent years. In this work, we show that the framework of BSS machines does enable the algorithmic solvability of finite dimensional inverse problems. Our results emphasize the influence of the considered computing model in questions of accuracy and reliability.
Interfacial flow around a pusher bacterium

Abstract Motile bacteria play essential roles in biology that rely on their dynamic behaviours, including their ability to navigate, interact and self-organize. However, bacteria dynamics on fluid interfaces are not well understood. Swimmers adsorbed on fluid interfaces remain highly motile, and fluid interfaces are highly non-ideal domains that alter swimming behaviour. To understand these effects, we study flow fields generated by Pseudomonas aeruginosa PA01 in the pusher mode. Analysis of correlated displacements of tracers and bacteria reveals dipolar flow fields with unexpected asymmetries that differ significantly from their counterparts in bulk fluids. We decompose the flow field into fundamental hydrodynamic modes for swimmers in incompressible fluid interfaces. We find an expected force-doublet mode corresponding to propulsion and drag at the interface plane, and a second dipolar mode, associated with forces exerted by the flagellum on the cell body in the aqueous phase that are countered by Marangoni stresses in the interface. The balance of these modes depends on the bacteria's trapped interfacial configurations. Understanding these flows is broadly important in nature and in the design of biomimetic swimmers.
This Sample seems to be good enough! Assessing Coverage and Temporal Reliability of Twitter's Academic API

Because of its willingness to share data with academia and industry, Twitter has been the primary social media platform for scientific research as well as for consulting businesses and governments in the last decade. In recent years, a series of publications have studied and criticized Twitter's APIs and Twitter has partially adapted its existing data streams. The newest Twitter API for Academic Research allows to "access Twitter's real-time and historical public data with additional features and functionality that support collecting more precise, complete, and unbiased datasets. The main new feature of this API is the possibility of accessing the full archive of all historic Tweets. In this article, we will take a closer look at the Academic API and will try to answer two questions. First, are the datasets collected with the Academic API complete? Secondly, since Twitter's Academic API delivers historic Tweets as represented on Twitter at the time of data collection, we need to understand how much data is lost over time due to Tweet and account removal from the platform. Our work shows evidence that Twitter's Academic API can indeed create (almost) complete samples of Twitter data based on a wide variety of search terms. We also provide evidence that Twitter's data endpoint v2 delivers better samples than the previously used endpoint v1.1. Furthermore, collecting Tweets with the Academic API at the time of studying a phenomenon rather than creating local archives of stored Tweets, allows for a straightforward way of following Twitter's developer agreement. Finally, we will also discuss technical artifacts and implications of the Academic API. We hope that our work can add another layer of understanding of Twitter data collections leading to more reliable studies of human behavior via social media data.
Neural Convolutional Surfaces

This work is concerned with a representation of shapes that disentangles fine, local and possibly repeating geometry, from global, coarse structures. Achieving such disentanglement leads to two unrelated advantages: i) a significant compression in the number of parameters required to represent a given geometry; ii) the ability to manipulate either global geometry, or local details, without harming the other. At the core of our approach lies a novel pipeline and neural architecture, which are optimized to represent one specific atlas, representing one 3D surface. Our pipeline and architecture are designed so that disentanglement of global geometry from local details is accomplished through optimization, in a completely unsupervised manner. We show that this approach achieves better neural shape compression than the state of the art, as well as enabling manipulation and transfer of shape details. Project page http://geometry.cs. ucl.ac. uk/projects/2022/cnnmaps/.
An Algebraic System for Constructing Cryptographic Permutations over Finite Fields

In this paper we identify polynomial dynamical systems over ﬁnite ﬁelds as the central component of almost all iterative block cipher design strategies over ﬁnite ﬁelds. We propose a generalized triangular polynomial dynamical system (GTDS), and give a generic algebraic deﬁnition of iterative (keyed) permutation using GTDS. Our GTDS-based generic deﬁnition is able to describe widely used and well-known design strategies such as substitution permutation network (SPN), Feistel network and their variants among others. We show that the Lai-Massey design strategy for (keyed) permutations is also described by the GTDS. Our generic algebraic deﬁnition of iterative permutation is particularly useful for instantiating and systematically studying block ciphers and hash functions over F p aimed for multiparty computation and zero-knowledge based cryptographic protocols. Finally, we provide the discrepancy analysis a technique used to measure the (pseudo-)randomness of a sequence, for analyzing the randomness of the sequence generated by the generic permutation or block cipher described by GTDS.
Discussions on the Nature of GLEAM-X J162759.5–523504.3

The nature of the long-period radio transient GLEAM-X J162759.5−523504.3 (hereafter GLEAM-X J1627) is discussed. We try to understand both its radio emission and pulsation in the neutron star scenario as an alternative to the white dwarf model. We think that (1) from the radio emission point of view, GLEAM-X J1627 can be a radio-loud magnetar; (2) from the rotational evolution point of view, GLEAM-X J1627 is unlikely to be an isolated magnetar; (3) the 1091 s period is unlikely to be the precession period; (4) GLEAM-X J1627 may be a radio-loud magnetar spun down by a fallback disk; (5) the pulsar death line is modified due to the presence of a fallback disk or a twisted magnetic field. In both cases, a higher maximum acceleration potential can be obtained. This may explain why GLEAM-X J1627 is still radioactive with such a long pulsation period; and (6) general constraints on the neutron star magnetic field and initial disk mass are given analytically. Possible ways to discriminate between different modelings are also discussed.
An adaptive model checking test for functional linear model

Numerous studies have been devoted to the estimation and inference problems for functional linear models (FLM). However, few works focus on model checking problem that ensures the reliability of results. Limited tests in this area do not have tractable null distributions or asymptotic analysis under alternatives. Also, the functional predictor is usually assumed to be fully observed, which is impractical. To address these problems, we propose an adaptive model checking test for FLM. It combines regular moment-based and conditional moment-based tests, and achieves model adaptivity via the dimension of a residual-based subspace. The advantages of our test are manifold. First, it has a tractable chi-squared null distribution and higher powers under the alternatives than its components. Second, asymptotic properties under different underlying models are developed, including the unvisited local alternatives. Third, the test statistic is constructed upon finite grid points, which incorporates the discrete nature of collected data. We develop the desirable relationship between sample size and number of grid points to maintain the asymptotic properties. Besides, we provide a data-driven approach to estimate the dimension leading to model adaptivity, which is promising in sufficient dimension reduction. We conduct comprehensive numerical experiments to demonstrate the advantages the test inherits from its two simple components.
Systematic Unsupervised Recycled Field-Programmable Gate Array Detection

With the expansion of the semiconductor supply chain, the use of recycled field-programmable gate arrays (FPGAs) has become a serious concern. Several methods for detecting recycled FPGAs by analyzing the ring oscillator (RO) frequencies have been proposed; however, most assume the known fresh FPGAs (KFFs) as the training data in machine-learning-based classification. In this study, we propose a novel recycled FPGA detection method based on an unsupervised anomaly detection scheme when there are few or no KFFs available. As the RO frequencies in the neighboring logic blocks on an FPGA are similar because of systematic process variation, our method compares the RO frequencies and does not require KFFs. The proposed method efficiently identifies recycled FPGAs through outlier detection using direct density ratio estimation. Experiments using Xilinx Artix-7 FPGAs demonstrate that the proposed method successfully distinguishes recycled FPGAs from 35 fresh FPGAs. In contrast, a conventional recycled FPGA detection method results in certain misclassification.
On Mobility and Interaction Landscapes: Identifying Three Strands of Developments for Mobile Interaction Design Research

In this chapter we address how the landscape of mobile HCI is changing. With a point of departure taken in how mobile interaction design is nowadays not only about interface design for digital devices, but about the design of wide-ranging interaction landscapes we explore how mobile HCI are now heavily interwoven in complex arrangements of computational devices, platforms and services. In exploring these current developments, this chapter identifies three contemporary strands of developments related to 1) the formation of new interaction landscapes, 2) the long tail of interaction, and 3) digital materiali-ties. With a point of departure taken in these three strands, this chapter presents a research agenda for moving forward followed by a discussion on the implications of this agenda for mobile HCI research.
Testing multiflavored ultralight dark matter models with SPARC

We perform maximum likelihood estimates (MLEs) for single and double flavor ultralight dark matter (ULDM) models using the Spitzer Photometry and Accurate Rotation Curves (SPARC) database. These estimates are compared to MLEs for several commonly used cold dark matter (CDM) models. By comparing various CDM models we find, in agreement with previous studies, that the Burkert and Einasto models tend to perform better than other commonly used CDM models. We focus on comparisons between the Einasto and ULDM models and analyze cases for which the ULDM particle masses are: free to vary; and fixed. For each of these analyses, we perform fits assuming the soliton and halo profiles are: summed together; and matched at a given radius. When we let the particle masses vary, we find a negligible preference for any particular range of particle masses, within $10^{-25}\,\text{eV}\leq m\leq10^{-19}\,\text{eV}$, when assuming the summed models. For the matched models, however, we find that almost all galaxies prefer particles masses in the range $10^{-23}\,\text{eV}\lesssim m\lesssim10^{-20}\,\text{eV}$. For both double flavor models we find that most galaxies prefer approximately equal particle masses. We find that the summed models give much larger variances with respect to the soliton-halo (SH) relation than the matched models. When the particle masses are fixed, the matched models give median and mean soliton and halo values that fall within the SH relation bounds, for most masses scanned. When the particle masses are fixed in the fitting procedure, we find the best fit results for the particle mass $m=10^{-20.5}\,\text{eV}$ (for the single flavor models) and $m_1=10^{-20.5}\,\text{eV}$, $m_2=10^{-20.2}\,\text{eV}$ for the double flavor, matched model. We discuss how our study will be furthered using a reinforcement learning algorithm.
Detecting Anchors' Opinion in Hinghlish News Delivery

Humans like to express their opinions and crave the opinions of others. Mining and detecting opinions from various sources are beneficial to individuals, organisations, and even governments. One such organisation is news media, where a general norm is not to showcase opinions from their side. Anchors are the face of the digital media, and it is required for them not to be opinionated. However, at times, they diverge from the accepted norm and insert their opinions into otherwise straightforward news reports, either purposefully or unintentionally. This is primarily seen in debates as it requires the anchors to be spontaneous, thus making them vulnerable to add their opinions. The consequence of such mishappening might lead to biased news or even supporting a certain agenda at the worst. To this end, we propose a novel task of anchors' opinion detection in debates. We curate code-mixed news debates and develop the ODIN dataset. A total of 2054 anchors' utterances in the dataset are marked as opinionated or non-opinionated. Lastly, we propose DetONADe, an interactive attention-based framework for classifying anchors' utterances and obtain the best weighted-F1 score of 0.703. A thorough analysis and evaluation show many interesting patterns in the dataset and predictions.