Miracle-dz/UniFAR-FTUs huggingface dataset:
Foundations for Near-Term Quantum Natural Language Processing

We provide conceptual and mathematical foundations for near-term quantum natural language processing (QNLP), and do so in quantum computer scientist friendly terms. We opted for an expository presentation style, and provide references for supporting empirical evidence and formal statements concerning mathematical generality. We recall how the quantum model for natural language that we employ canonically combines linguistic meanings with rich linguistic structure, most notably grammar. In particular, the fact that it takes a quantum-like model to combine meaning and structure, establishes QNLP as quantum-native, on par with simulation of quantum systems. Moreover, the now leading Noisy Intermediate-Scale Quantum (NISQ) paradigm for encoding classical data on quantum hardware, variational quantum circuits, makes NISQ exceptionally QNLP-friendly: linguistic structure can be encoded as a free lunch, in contrast to the apparently exponentially expensive classical encoding of grammar. Quantum speed-up for QNLP tasks has already been established in previous work with Will Zeng. Here we provide a broader range of tasks which all enjoy the same advantage. Diagrammatic reasoning is at the heart of QNLP. Firstly, the quantum model interprets language as quantum processes via the diagrammatic formalism of categorical quantum mechanics. Secondly, these diagrams are via ZX-calculus translated into quantum circuits. Parameterisations of meanings then become the circuit variables to be learned. Our encoding of linguistic structure within quantum circuits also embodies a novel approach for establishing word-meanings that goes beyond the current standards in mainstream AI, by placing linguistic structure at the heart of Wittgenstein's meaning-is-context.
Learning normal appearance for fetal anomaly screening: Application to the unsupervised detection of Hypoplastic Left Heart Syndrome

Congenital heart disease is considered as one the most common groups of congenital malformations which affects 6 − 11 per 1000 newborns. In this work, an automated framework for detection of cardiac anomalies during ultrasound screening is proposed and evaluated on the example of Hypoplastic Left Heart Syndrome (HLHS), a sub-category of congenital heart disease. We propose an unsupervised approach that learns healthy anatomy exclusively from clinically confirmed normal control patients. We evaluate a number of known anomaly detection frameworks together with a new model architecture based on the α-GAN network and find evidence that the proposed model performs significantly better than the state-of-the-art in image-based anomaly detection, yielding average 0.81 AUC and a better robustness towards initialisation compared to previous works.
Online Model Selection: a Rested Bandit Formulation

Motivated by a natural problem in online model selection with bandit information, we introduce and analyze a best arm identification problem in the rested bandit setting, wherein arm expected losses decrease with the number of times the arm has been played. The shape of the expected loss functions is similar across arms, and is assumed to be available up to unknown parameters that have to be learned on the fly. We define a novel notion of regret for this problem, where we compare to the policy that always plays the arm having the smallest expected loss at the end of the game. We analyze an arm elimination algorithm whose regret vanishes as the time horizon increases. The actual rate of convergence depends in a detailed way on the postulated functional form of the expected losses. Unlike known model selection efforts in the recent bandit literature, our algorithm exploits the specific structure of the problem to learn the unknown parameters of the expected loss function so as to identify the best arm as quickly as possible. We complement our analysis with a lower bound, indicating strengths and limitations of the proposed solution.
The Lab vs The Crowd: An Investigation into Data Quality for Neural Dialogue Models

Challenges around collecting and processing quality data have hampered progress in data-driven dialogue models. Previous approaches are moving away from costly, resource-intensive lab settings, where collection is slow but where the data is deemed of high quality. The advent of crowd-sourcing platforms, such as Amazon Mechanical Turk, has provided researchers with an alternative cost-effective and rapid way to collect data. However, the collection of fluid, natural spoken or textual interaction can be challenging, particularly between two crowd-sourced workers. In this study, we compare the performance of dialogue models for the same interaction task but collected in two different settings: in the lab vs. crowd-sourced. We find that fewer lab dialogues are needed to reach similar accuracy, less than half the amount of lab data as crowd-sourced data. We discuss the advantages and disadvantages of each data collection method.
Self-Healing Small-Scale Swimmers.

Herein, self-healing small-scale swimmers capable of autonomous propulsion and on-the-fly structural recovery are described. The new strategy instantaneously restores the functionality of the swimmer after it has suffered severe damage. Incorporation of magnetic microparticles in strips along with the printed functional body layers (consisting of conductive carbon, low-density hydrophobic polymer and catalytically active metal) results in rapid reorientation and reattachment of the moving damaged catalytic tail to its complimentary broken static body piece. Such magnetic alignment and attraction restores the original swimmer structure and propulsion behavior, independent of user input, displaying healing efficiencies as high as 88 percent. Modeling of the magnetic fields and simulations of various swimmer configurations are used to study the magnetic force field distribution around the swimmers. The influence of the damage position and pattern of multiple magnetic healing strips is examined and their influence upon healing efficiency is compared. Owing to its versatility, fast recovery, simplicity, and efficiency, the new on-the-fly self-healing strategy represents an important step towards the development of new classes of robots that can regain their functionality in situations of extreme mechanical damage where repair is not possible or challenging.
Microscopic equation of state of hot nuclear matter for numerical relativity simulations

Context. A precise understanding of the equation of state (EOS) of dense and hot matter is key to modeling relativistic astrophysical environments, including core-collapse supernovae (CCSNe), protoneutron star (PNSs) evolution, and compact binary mergers. Aims. In this paper, we extend the microscopic zero-temperature BL (Bombaci and Logoteta) nuclear EOS to finite temperature and arbitrary nuclear composition. We employ this new EOS to describe hot β-stable nuclear matter and to compute various structural properties of nonrotating PNS. We also apply the EOS to perform dynamical simulations of a spherically symmetric CCSN. Methods. The EOS is derived using the finite temperature extension of the Brueckner–Bethe–Goldstone quantum many-body theory in the Brueckner–Hartree–Fock approximation. Neutron star properties are computed by solving the Tolman–Oppenheimer–Volkoff structure equations numerically. The sperically symmetric CCSN simulations are performed using the AGILE-IDSA code. Results. Our EOS models are able to reproduce typical features of both PNS and spherically symmetric CCSN simulations. In addition, our EOS model is consistent with present measured neutron star masses and particularly with the masses: M = 2.01 ± 0.04 M⊙ and M = 2.14−0.18+0.20 M⊙ of the neutron stars in PSR J0348+0432 and PSR J0740+6620 respectively. Finally, we suggest a feasible mechanism to produce low-mass black holes (M ∼ 2 M⊙) that could have far-reaching consequences for interpreting the gravitational wave event GW190814 as a BH–BH merger.
IHashNet: Iris Hashing Network based on efficient multi-index hashing

Massive biometric deployments are pervasive in today's world. But despite the high accuracy of biometric systems, their computational efficiency degrades drastically with an increase in the database size. Thus, it is essential to index them. Here, in this paper, we propose an iris indexing scheme using real-valued deep iris features binarized to iris bar codes (IBC) compatible with the indexing structure. Firstly, for extracting robust iris features, we have designed a network utilizing the domain knowledge of ordinal filtering and learning their nonlinear combinations. Later these real-valued features are binarized. Finally, for indexing the iris dataset, we have proposed a Mcom loss that can transform the binary feature into an improved feature compatible with the Multi-Index Hashing scheme. This Mcom loss function ensures the equal distribution of Hamming distance among all the contiguous disjoint sub-strings. To the best of our knowledge, this is the first work in the iris indexing domain that presents an end-to-end iris indexing structure. Experimental results on four datasets are presented to depict the efficacy of the proposed approach.
No Need to Know Physics: Resilience of Process-based Model-free Anomaly Detection for Industrial Control Systems

In recent years, a number of process-based anomaly detection schemes for Industrial Control Systems were proposed. In this work, we provide the first systematic analysis of such schemes, and introduce a taxonomy of properties that are verified by those detection systems. We then present a novel general framework to generate adversarial spoofing signals that violate physical properties of the system, and use the framework to analyze four anomaly detectors published at top security conferences. We find that three of those detectors are susceptible to a number of adversarial manipulations (e.g., spoofing with precomputed patterns), which we call Synthetic Sensor Spoofing and one is resilient against our attacks. We investigate the root of its resilience and demonstrate that it comes from the properties that we introduced. Our attacks reduce the Recall (True Positive Rate) of the attacked schemes making them not able to correctly detect anomalies. Thus, the vulnerabilities we discovered in the anomaly detectors show that (despite an original good detection performance), those detectors are not able to reliably learn physical properties of the system. Even attacks that prior work was expected to be resilient against (based on verified properties) were found to be successful. We argue that our findings demonstrate the need for both more complete attacks in datasets, and more critical analysis of process-based anomaly detectors. We plan to release our implementation as open-source, together with an extension of two public datasets with a set of Synthetic Sensor Spoofing attacks as generated by our framework.
AI-enabled Prediction of eSports Player Performance Using the Data from Heterogeneous Sensors

The emerging progress of eSports lacks the tools for ensuring high-quality analytics and training in Pro and amateur eSports teams. We report on an Artificial Intelligence (AI) enabled solution for predicting the eSports player in-game performance using exclusively the data from sensors. For this reason, we collected the physiological, environmental, and the game chair data from Pro and amateur players. The player performance is assessed from the game logs in a multiplayer game for each moment of time using a recurrent neural network. We have investigated that attention mechanism improves the generalization of the network and provides the straightforward feature importance as well. The best model achieves ROC AUC score 0.73. The prediction of the performance of particular player is realized although his data are not utilized in the training set. The proposed solution has a number of promising applications for Pro eSports teams as well as a learning tool for amateur players.
Molecules, clusters and crystals : the crystallisation of p-aminobenzoic acid from solution

The University of ManchesterRachel A SullivanFor the degree of Doctor of PhilosophyMolecules, Clusters and Crystals: The Crystallisation of p-aminobenzoic acid from SolutionJanuary 2015Nucleation is a key step in the crystallisation process, where a new crystalline solid phase is created from a supersaturated solution. The applications of crystallisation as a purification and separation technique span many industries, yet still no definitive molecular mechanism for nucleation exists. This PhD is part of a critical mass research project involving researchers from both the Universities of Manchester and Leeds. The aim is to reveal the relationship between structural components of the nucleation transition state, solution phase molecular self-assembly and nano cluster formation, through to critically sized crystalline nuclei which then grow to crystals. All work has been carried out on a small organic molecule, p-aminobenzoic acid (PABA). This PhD has delivered successful characterisation of PABA in the solid and solution state, along with a detailed understanding of its nucleation kinetics and growth rates from a range of solvents. PABA has two enantiotropically related polymorphs, ? and ? , with the former constructed of carboxylic acid dimers and the latter of a hydrogen bonded tetramer network linking alternate acid and amine functionalities. New determinations of the crystal structures of both forms were submitted to the CCDC with Ref codes of AMBNAC07 and 08 for ? and ? PABA respectively.A detailed morphological study on both forms of PABA employing modelling and experimental methods has revealed the effect of solvent on the growth habit. In all polar solvents, ? PABA displays a more important or slower growing (002) face than the calculated morphology implies. In water, ? PABA has a much smaller (101 ? ) face in comparison to ? PABA grown from alcohols. Crystallisation experiments demonstrate a clear solvent effect on the appearance of the two polymorphs. From organic solvents only ? PABA is obtained, from water both ? and ? PABA are crystallised. A database search (CCDC) suggests that water may play an important role in the stabilisation of the nucleation transition state for both ? and ? PABA. This is not possible in organic solvents.Detailed nucleation and crystal growth kinetics have been measured for ? PABA at 20�C in water, acetonitrile, ethyl acetate and 2-propanol. A clear solvent trend was observed in both the derived rates of molecular attachment and crystal growth. These were fastest in water, followed by acetonitrile, then ethyl acetate and finally slowest, in 2-propanol. This can be explained by the solvation of the carboxylic acid functional group, where 2-propanol is deemed the most effective solvator of building units in solution and on a crystal surface. This conclusion is supported by the solution FTIR spectroscopy, which clearly confirms strong solvation.
On the Afferrante-Carbone theory of ultratough peeling

In an elegant and interesting theory of ultratough peeling of an elastic tape from a viscoelastic substrate, Afferrante and Carbone (2016) find that, in contrast to the classic elastic Kendall's theory, there are conditions for which the load for steady state peeling could be arbitrarily large in steady state peeling, at low angles of peeling - what they call "ultratough" peeling. It is here shown in fact that this occurs near critical speeds where the elastic energy term of Kendall's equation is balanced by the viscoelastic dissipation. Surprisingly, this seems to lead to toughness enhancement higher than the limit value observed in a very large crack in a infinite viscoelastic body, possibly even considering a limit on the stress transmitted. Kendall's experiments in turn had considered viscoelastic tapes (rather than substrates), and his viscoelastic findinds seem to lead to a much simpler picture. The Afferrante-Carbone theory suggests the viscoelastic effect to be an on-off mechanism, since for large angles of peeling it is almost insignificant, while only below a certain threshold, this "ultratough" peeling seems to appear. Experimental and/or numerical verification would be most useful.
Differentiation of Human Embryonic Stem Cells into Neuron, Cholinergic, and Glial Cells

Human embryonic stem cells (hESCs) are pluripotent cells, capable of differentiation into different cellular lineages given the opportunity. Derived from the inner cell mass of blastocysts in early embryonic development, the cell self-renewal ability makes them a great tool for regenerative medicine, and there are different protocols available for maintaining hESCs in their undifferentiated state. In addition, protocols for differentiation into functional human neural stem cells (hNSCs), which have the potential for further differentiation into various neural cell types, are available. However, many protocols are time-consuming and complex and do not always fit for purpose. In this study, we carefully combined, optimized, and developed protocols for differentiation of hESCs into adherent monolayer hNSCs over a short period of time, with the possibility of both expansion and freezing. Moreover, the method details further differentiation into neurons, cholinergic neurons, and glial cells in a simple, single step by step protocol. We performed immunocytochemistry, qPCR, and electrophysiology to examine the expression profile and characteristics of the cells to verify cell lineage. Using presented protocols, the creation of neuronal cultures, cholinergic neurons, and a mixed culture of astrocytes and oligodendrocytes can be completed within a three-week time period.
Low depth algorithms for quantum amplitude estimation

We design and analyze two new low depth algorithms for amplitude estimation (AE) achieving an optimal tradeoff between the quantum speedup and circuit depth. For $\beta \in (0,1]$, our algorithms require $N= O( \frac{1}{ \epsilon^{1+\beta}})$ oracle calls and require the oracle to be called sequentially $D= O( \frac{1}{ \epsilon^{1-\beta}})$ times to perform amplitude estimation within additive error $\epsilon$. These algorithms interpolate between the classical algorithm $(\beta=1)$ and the standard quantum algorithm ($\beta=0$) and achieve a tradeoff $ND= O(1/\epsilon^{2})$. These algorithms bring quantum speedups for Monte Carlo methods closer to realization, as they can provide speedups with shallower circuits. The first algorithm (Power law AE) uses power law schedules in the framework introduced by Suzuki et al \cite{S20}. The algorithm works for $\beta \in (0,1]$ and has provable correctness guarantees when the log-likelihood function satisfies regularity conditions required for the Bernstein Von-Mises theorem. The second algorithm (QoPrime AE) uses the Chinese remainder theorem for combining lower depth estimates to achieve higher accuracy. The algorithm works for discrete $\beta =q/k$ where $k \geq 2$ is the number of distinct coprime moduli used by the algorithm and $1 \leq q \leq k-1$, and has a fully rigorous correctness proof. We analyze both algorithms in the presence of depolarizing noise and provide experimental comparisons with the state of the art amplitude estimation algorithms.
Discovering Underground Maps from Fashion

The fashion sense—meaning the clothing styles people wear—in a geographical region can reveal information about that region. For example, it can reflect the kind of activities people do there, or the type of crowds that frequently visit the region (e.g., tourist hot spot, student neighborhood, business center). We propose a method to create underground neighborhood maps of cities by analyzing how people dress. Using publicly available images from across a city, our method automatically segments the map into neighborhoods with a similar fashion sense. Our approach further allows discovering insights about a city, such as detecting distinct neighborhoods (what is the most unique region of NYC? ) and answering analogy questions between cities (what is the "Downtown LA" of Bogota? ). We also present two new underground map benchmarks derived from non-image data for 37 cities worldwide. Our method shows promising results on both these benchmarks as well as experiments with human judges. "The map is not the thing mapped. "—Eric Temple Bell
Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising

In e-commerce advertising, the ad platform usually relies on auction mechanisms to optimize different performance metrics, such as user experience, advertiser utility, and platform revenue. However, most of the state-of-the-art auction mechanisms only focus on optimizing a single performance metric, e.g., either social welfare or revenue, and are not suitable for e-commerce advertising with various, dynamic, difficult to estimate, and even conflicting performance metrics. In this paper, we propose a new mechanism called Deep GSP auction, which leverages deep learning to design new rank score functions within the celebrated GSP auction framework. These new rank score functions are implemented via deep neural network models under the constraints of monotone allocation and smooth transition. The requirement of monotone allocation ensures Deep GSP auction nice game theoretical properties, while the requirement of smooth transition guarantees the advertiser utilities would not fluctuate too much when the auction mechanism switches among candidate mechanisms to achieve different optimization objectives. We deployed the proposed mechanisms in a leading e-commerce ad platform and conducted comprehensive experimental evaluations with both offline simulations and online A/B tests. The results demonstrated the effectiveness of the Deep GSP auction compared to the state-of-the-art auction mechanisms.
Security and Privacy for Mobile Edge Caching: Challenges and Solutions

Mobile edge caching is a promising technology for next-generation mobile networks to effectively offer service environments and cloud-storage capabilities at the edge of networks. By exploiting the storage and computing resources at the network edge, mobile edge caching can significantly reduce service latency, decrease network load, and improve the user experience. On the other hand, edge caching is subject to a number of threats regarding privacy violations and security breaches. In this article, we first introduce the architecture of mobile edge caching, and address the key problems regarding why, where, what, and how to cache. Then we examine the potential cyber threats, including cache poisoning attacks, cache pollution attacks, cache side-channel attacks, and cache deception attacks, which result in huge concerns about privacy, security, and trust in content placement, content delivery, and content usage for mobile users, respectively. After that, we propose a service-oriented and location-based efficient key distribution protocol (SOLEK) as an example in response to efficient and secure content delivery in mobile edge caching. Finally, we discuss the potential techniques for privacy-preserving content placement, efficient and secure content delivery, and trustful content usage, which are expected to draw more attention and efforts into secure edge caching.
Fairness Considered Harmful: On the Non-portability of Fair-ML in India

Conventional algorithmic fairness is Western in its sub-groups, values, and optimizations. In this paper, we ask how portable the assumptions of this largely Western take on algorithmic fairness are to a different geo-cultural context such as India. Based on 36 expert interviews with Indian scholars, and an analysis of emerging algorithmic deployments in India, we identify three clusters of challenges that engulf the large distance between machine learning models and oppressed communities in India. We argue that a mere translation of technical fairness work to Indian subgroups may serve only as a window dressing, and instead, call for a collective re-imagining of Fair-ML, by re-contextualising data and models, empowering oppressed communities, and more importantly, enabling ecosystems.