Miracle-dz/UniFAR-FTUs huggingface dataset:
Topic Modelling on Consumer Financial Protection Bureau Data: An Approach Using BERT Based Embeddings

Customers' reviews and comments are important for businesses to understand users' sentiment about the products and services. However, this data needs to be analyzed to assess the sentiment associated with topics/aspects to provide efficient customer assistance. LDA and LSA fail to capture the semantic relationship and are not specific to any domain. In this study, we evaluate BERTopic, a novel method that generates topics using sentence embeddings on Consumer Financial Protection Bureau (CFPB) data. Our work shows that BERTopic is flexible and yet provides meaningful and diverse topics compared to LDA and LSA. Furthermore, domain-specific pre-trained embeddings (FinBERT) yield even better topics. We evaluated the topics on coherence score (c_v) and UMass.
EFX Allocations: Simplifications and Improvements

The existence of EFX allocations is a fundamental open problem in discrete fair division. Given a set of agents and indivisible goods, the goal is to determine the existence of an allocation where no agent envies another following the removal of any single good from the other agent's bundle. Since the general problem has been illusive, progress is made on two fronts: $(i)$ proving existence when the number of agents is small, $(ii)$ proving existence of relaxations of EFX. In this paper, we improve results on both fronts (and simplify in one of the cases). We prove the existence of EFX allocations with three agents, restricting only one agent to have an MMS-feasible valuation function (a strict generalization of nice-cancelable valuation functions introduced by Berger et al. which subsumes additive, budget-additive and unit demand valuation functions). The other agents may have any monotone valuation functions. Our proof technique is significantly simpler and shorter than the proof by Chaudhury et al. on existence of EFX allocations when there are three agents with additive valuation functions and therefore more accessible. Secondly, we consider relaxations of EFX allocations, namely, approximate-EFX allocations and EFX allocations with few unallocated goods (charity). Chaudhury et al. showed the existence of $(1-\epsilon)$-EFX allocation with $O((n/\epsilon)^{\frac{4}{5}})$ charity by establishing a connection to a problem in extremal combinatorics. We improve their result and prove the existence of $(1-\epsilon)$-EFX allocations with $\tilde{O}((n/ \epsilon)^{\frac{1}{2}})$ charity. In fact, some of our techniques can be used to prove improved upper-bounds on a problem in zero-sum combinatorics introduced by Alon and Krivelevich.
A Property Induction Framework for Neural Language Models

To what extent can experience from language contribute to our conceptual knowledge? Computational explorations of this question have shed light on the ability of powerful neural language models (LMs) -- informed solely through text input -- to encode and elicit information about concepts and properties. To extend this line of research, we present a framework that uses neural-network language models (LMs) to perform property induction -- a task in which humans generalize novel property knowledge (has sesamoid bones) from one or more concepts (robins) to others (sparrows, canaries). Patterns of property induction observed in humans have shed considerable light on the nature and organization of human conceptual knowledge. Inspired by this insight, we use our framework to explore the property inductions of LMs, and find that they show an inductive preference to generalize novel properties on the basis of category membership, suggesting the presence of a taxonomic bias in their representations.
How can I investigate causal brain networks with iEEG?

While many human imaging methodologies probe the structural and functional connectivity of the brain, techniques to investigate cortical networks in a causal and directional manner are critical but limited. The use of iEEG enables several approaches to directly characterize brain regions that are functionally connected and in some cases also establish directionality of these connections. In this chapter we focus on the basis, method and application of the cortico-cortical evoked potential (CCEP), whereby electrical pulses applied to one set of intracranial electrodes yields an electrically-induced brain response at local and remote regions. In this chapter, CCEPs are first contextualized within common brain connectivity methods used to define cortical networks and how CCEP adds unique information. Second, the practical and analytical considerations when using CCEP are discussed. Third, we review the neurophysiology underlying CCEPs and the applications of CCEPs including exploring functional and pathological brain networks and probing brain plasticity. Finally, we end with a discussion of limitations, caveats, and directions to improve CCEP utilization in the future.
Remarks on the penguin decay $B_s \to \phi \phi$ with prospects for FCCee

We underline the theoretical interest of the vector-vector penguin decay B s → φφ , very clean from the experimental point of view. The CP-violation asymmetry A mixCP comes from the interference of mixing and decay λ φφ = qp AA . In the Standard Model (SM) and in the Naive Factorization limit, the CP phase from the mixing qp exactly cancels the CP phase from the decay ratio AA . Therefore, this mode is suitable to look for possible New Physics (NP) because A mixCP would directly indicate the departure from the SM in mixing. We estimate the deviation from this cancellation by analyzing possible small eﬀects in the SM, using in particular the QCD Factorization scheme. We compare the theoretical expectation for A mixCP to the measurement of LHCb, and the implications for NP. We pay also special attention to the transverse amplitude h = − , the longitudinal and transverse polarization fractions, and the interesting helicity-dependent observables λ h =0 φφ and λ h = − φφ . On the other hand, we make an estimation of the expected sensitivity at the future FCCee experiment for the CP phase and modulus of λ φφ . We ﬁnd δ ( | λ φφ | ) = 0 . 004 and δ ( φ φφ ) = 0 . 009 rad and, comparing to the LHCb data, we point out the expectations at FCCee in the search of NP.
On extendability of functionals on Hilbert $C^*$-modules

Let M ⊂ N be Hilbert C ∗ -modules over a C ∗ -algebra A with M ⊥ = 0. It was shown recently by J. Kaad and M. Skeide that there exists a non-zero A -valued functional on N such that its restriction onto M is zero. Here we show that this may happen even if A is monotone complete. On the other hand, we show that for certain type I W ∗ -algebras this cannot happen.
Omnidirectional spin-to-charge conversion in graphene/NbSe2 van der Waals heterostructures

The conversion of spin currents polarized in different directions into charge currents is a keystone for novel spintronic devices. Van der Waals heterostructures with tailored symmetry are a very appealing platform for such a goal. Here, by performing nonlocal spin precession experiments, we demonstrate the spin-to-charge conversion (SCC) of spins oriented in all three directions (x, y, and z). By analyzing the magnitude and temperature dependence of the signal in different configurations, we argue that the different SCC components measured are likely due to spin–orbit proximity and broken symmetry at the twisted graphene/NbSe2 interface. Such efficient omnidirectional SCC opens the door to the use of new architectures in spintronic devices, from spin–orbit torques that can switch any magnetization to the magnetic state readout of magnetic elements pointing in any direction.
BERP IV - Aerodynamis, performance and flight envelope

This paper summarises the aerodynamic design and flight test performance results from the AW101 BERP IV Technology Demonstrator Programme that commenced in October 1997 and concluded in summer 2007. Aerodynamically, BERP IV represents a development of the BERP III main rotor blade whilst changes were also made to the dynamic and structural design. New blade technologies were introduced whilst the blade manufacturing process was also improved. The paper covers the aerodynamic design process including considerations on twist, aerofoils and planform shape, and the analytical and test techniques used to study and evaluate the blade performance. The main rotor was demonstrated on the AW101 aircraft from September 2006, and the paper details the hover and forward flight performance, and the retreating blade stall envelope.
Multiformer: A Head-Configurable Transformer-Based Model for Direct Speech Translation

Transformer-based models have been achieving state-of-the-art results in several fields of Natural Language Processing. However, its direct application to speech tasks is not trivial. The nature of this sequences carries problems such as long sequence lengths and redundancy between adjacent tokens. Therefore, we believe that regular self-attention mechanism might not be well suited for it. Different approaches have been proposed to overcome these problems, such as the use of efficient attention mechanisms. However, the use of these methods usually comes with a cost, which is a performance reduction caused by information loss. In this study, we present the Multiformer, a Transformer-based model which allows the use of different attention mechanisms on each head. By doing this, the model is able to bias the self-attention towards the extraction of more diverse token interactions, and the information loss is reduced. Finally, we perform an analysis of the head contributions, and we observe that those architectures where all heads relevance is uniformly distributed obtain better results. Our results show that mixing attention patterns along the different heads and layers outperforms our baseline by up to 0.7 BLEU.
Transformers in 3D Point Clouds: A Survey

—Transformers have been at the heart of the Natural Language Processing (NLP) and Computer Vision (CV) revolutions. The signiﬁcant success in NLP and CV inspired exploring the use of Transformers in point cloud processing. However, how do Transformers cope with the irregularity and unordered nature of point clouds? How suitable are Transformers for different 3D representations (e.g., point- or voxel-based)? How competent are Transformers for various 3D processing tasks? As of now, there is still no systematic survey of the research on these issues. For the ﬁrst time, we provided a comprehensive overview of increasingly popular Transformers for 3D point cloud analysis. We start by introducing the theory of the Transformer architecture and reviewing its applications in 2D/3D ﬁelds. Then, we present three different taxonomies (i.e., implementation-, data representation-, and task-based), which can classify current Transformer-based methods from multiple perspectives. Furthermore, we present the results of an investigation of the variants and improvements of the self-attention mechanism in 3D. To demonstrate the superiority of Transformers in point cloud analysis, we present comprehensive comparisons of various Transformer-based methods for classiﬁcation, segmentation, and object detection. Finally, we suggest three potential research directions, providing beneﬁt references for the development of 3D Transformers.
Double-Sided Information Aided Temporal-Correlated Massive Access

This letter considers temporal-correlated massive access, where each device, once activated, is likely to transmit continuously over several consecutive frames. Motivated by that the device activity at each frame is correlated to not only its previous frame but also its next frame, we propose a double-sided information (DSI) aided joint activity detection and channel estimation algorithm based on the approximate message passing (AMP) framework. The DSI is extracted from the estimation results in a sliding window that contains the target detection frame and its previous and next frames. The proposed algorithm demonstrates superior performance over the state-of-the-art methods.
Deformation of the triangular spin-1/2 lattice in Na 2 SrCo(PO 4 ) 2

Crystal structure and thermodynamic properties of Na 2 SrCo(PO 4 ) 2 , the chemical sibling of the triangular quantum spin-liquid candidate Na 2 BaCo(PO 4 ) 2 , are reported. From single crystal x-ray diﬀraction and high-resolution synchrotron x-ray powder diﬀraction, the compound was found to crystallize in the monoclinic space group P 2 1 /a at room temperature, in contrast to the trigonal Na 2 BaCo(PO 4 ) 2 . Above 650 K, the symmetry of Na 2 SrCo(PO 4 ) 2 changes to C 2 /m , while around 1025 K a further transformation toward trigonal symmetry is observed. The monoclinic symmetry leads to a small deformation of the CoO 6 octahedra beyond the trigonal distortion ubiquitous in this structure type, and results in the stronger g -tensor anisotropy ( g z /g xy = 1 . 6) as well as the increased XXZ anisotropy ( J z /J xy = 1 . 7) compared to the Ba-compound, while the average coupling strength, J av = (2 J xy + J z ) / 3 ’ 1 . 3 K, remains unchanged. The N´eel temperature increases from 140 mK (Ba) to 600 mK (Sr), and a weak spin canting with the net in-plane moment of 0 . 066(4) µ B / f.u. appears. We show that the ordering temperature of a triangular antiferromagnet is capably controlled by its structural distortions.
Propification and the Scalable Comonad

String diagrams can nicely express numerous computations in symmetric strict monoidal categories (SSMC). To be entirely exact, this is only true for props: the SSMCs whose monoid of objects are free. In this paper, we show a propification theorem asserting that any SSMC is monoidally equivalent to a coloured prop. As a consequence, all SSMCs are within reach of diagrammatical methods. We introduce a diagrammatical calculus of bureaucracy isomorphisms, allowing us to handle graphically non-free monoids of objects. We also connect this construction with the scalable notations previously introduced to tackle large-scale diagrammatic reasoning.
On the origin of core radio emissions from black hole sources in the realm of relativistic shocked accretion flow

We study the relativistic, inviscid, advective accretion flow around the black holes and investigate a key feature of the accretion flow, namely the shock waves. We observe that the shock-induced accretion solutions are prevalent and such solutions are commonly obtained for a wide range of the flow parameters, such as energy (${\cal E}$) and angular momentum (λ), around the black holes of spin value 0 ≤ ak < 1. When the shock is dissipative in nature, a part of the accretion energy is released through the upper and lower surfaces of the disc at the location of the shock transition. We find that the maximum accretion energies that can be extracted at the dissipative shock ($\Delta {\cal E}^{\rm max}$) are $\sim 1\%$ and $\sim 4.4\%$ for Schwarzschild black holes (ak → 0) and Kerr black holes (ak → 1), respectively. Using $\Delta {\cal E}^{\rm max}$, we compute the loss of kinetic power (equivalently shock luminosity, Lshock) that is enabled to comply with the energy budget for generating jets/outflows from the jet base (i.e., post-shock flow). We compare Lshock with the observed core radio luminosity (LR) of black hole sources for a wide mass range spanning 10 orders of magnitude with sub-Eddington accretion rate and perceive that the present formalism seems to be potentially viable to account LR of 16 Galactic black hole X-ray binaries (BH-XRBs) and 2176 active galactic nuclei (AGNs). We further aim to address the core radio luminosity of intermediate-mass black hole (IMBH) sources and indicate that the present model formalism perhaps adequate to explain core radio emission of IMBH sources in the sub-Eddington accretion limit.
Analyzing Lottery Ticket Hypothesis from PAC-Bayesian Theory Perspective

The lottery ticket hypothesis (LTH) has attracted attention because it can explain why over-parameterized models often show high generalization ability. It is known that when we use iterative magnitude pruning (IMP), which is an algorithm to find sparse networks with high generalization ability that can be trained from the initial weights independently, called winning tickets, the initial large learning rate does not work well in deep neural networks such as ResNet. However, since the initial large learning rate generally helps the optimizer to converge to flatter minima, we hypothesize that the winning tickets have relatively sharp minima, which is considered a disadvantage in terms of generalization ability. In this paper, we confirm this hypothesis and show that the PAC-Bayesian theory can provide an explicit understanding of the relationship between LTH and generalization behavior. On the basis of our experimental findings that flatness is useful for improving accuracy and robustness to label noise and that the distance from the initial weights is deeply involved in winning tickets, we offer the PAC-Bayes bound using a spike-and-slab distribution to analyze winning tickets. Finally, we revisit existing algorithms for finding winning tickets from a PAC-Bayesian perspective and provide new insights into these methods.
CQR-SQL: Conversational Question Reformulation Enhanced Context-Dependent Text-to-SQL Parsers

Context-dependent text-to-SQL is the task of translating multi-turn questions into database-related SQL queries. Existing methods typically focus on making full use of history context or previously predicted SQL for currently SQL parsing, while neglecting to explicitly comprehend the schema and conversational dependency, such as co-reference, ellipsis and user focus change. In this paper, we propose CQR-SQL, which uses auxiliary Conversational Question Reformulation (CQR) learning to explicitly exploit schema and decouple contextual dependency for SQL parsing. Specifically, we first present a schema enhanced recursive CQR method to produce domain-relevant self-contained questions. Secondly, we train CQR-SQL models to map the semantics of multi-turn questions and auxiliary self-contained questions into the same latent space through schema grounding consistency task and tree-structured SQL parsing consistency task, which enhances the abilities of SQL parsing by adequately contextual understanding. At the time of writing, our CQR-SQL achieves new state-of-the-art results on two context-dependent text-to-SQL benchmarks SParC and CoSQL.
Physics guided neural networks for modelling of non-linear dynamics

The success of the current wave of artificial intelligence can be partly attributed to deep neural networks, which have proven to be very effective in learning complex patterns from large datasets with minimal human intervention. However, it is difficult to train these models on complex dynamical systems from data alone due to their low data efficiency and sensitivity to hyperparameters and initialisation. This work demonstrates that injection of partially known information at an intermediate layer in a DNN can improve model accuracy, reduce model uncertainty, and yield improved convergence during the training. The value of these physics-guided neural networks has been demonstrated by learning the dynamics of a wide variety of nonlinear dynamical systems represented by five well-known equations in nonlinear systems theory: the Lotka-Volterra, Duffing, Van der Pol, Lorenz, and Henon-Heiles systems.
QoS-Aware User Scheduling in Crowded XL-MIMO Systems Under Non-Stationary Multi-State LoS/NLoS Channels

Ensuring that the quality-of-service (QoS) requirements are satisfied in wireless communications systems with high user density is challenging due to the limitations on the transmit power budget and the number of resource blocks. In this paper, we propose a QoS-aware joint user scheduling and power allocation technique to enhance the number of served users in the downlink of crowded extra-large scale massive multiple-input multiple-output (XL-MIMO) with minimum QoS requirements guarantee. The proposed technique is constituted by two sequential procedures: the clique search-based scheduling (CBS) algorithm for user scheduling followed by optimal power allocation with transmit power budget and minimum achievable rate per user constraints.We propose a generalized non-stationary multi-state channel model based on spherical wave propagation assuming that users under line-of-sight (LoS) and non-LoS (NLoS) transmission coexist in the same communication cell. This is done to accurately evaluate the proposed technique in realistic XL-MIMO scenarios. Numerical results reveal that the proposed CBS algorithm provides fair coverage over the whole cell area, achieving remarkable numbers of scheduled users with satisfied QoS requirements when users under the LoS and NLoS channel states coexist in the communication cell.
VQBB: Image-to-image Translation with Vector Quantized Brownian Bridge

Image-to-image translation is an important and challenging problem in computer vision. Existing approaches like Pixel2Pixel [7], DualGAN [12] suffer from the instability of GAN and fail to generate diverse outputs because they model the task as a one-to-one mapping. Although diffusion models can generate images with high quality and diversity, current conditional diffusion models still can not maintain high similarity with the condition image on image-to-image translation tasks due to the Gaussian noise added in the reverse process. To address these issues, a novel Vector Quantized Brownian Bridge(VQBB) diffusion model is proposed in this paper. On one hand, Brownian Bridge diffusion process can model the transformation between two domains more accurate and ﬂexible than the existing Markov diffusion methods. As far as the authors know, it is the ﬁrst work for Brownian Bridge diffusion process proposed for image-to-image translation. On the other hand, the proposed method improved the learning efﬁciency and translation accuracy by conﬁning the diffusion process in the quantized latent space. Finally, numerical experimental results validated the performance of the proposed method.
Rydberg exciton-polaritons in a magnetic field

We theoretically investigate exciton-polaritons in a two-dimensional (2D) semiconductor heterostructure, where a static magnetic field is applied perpendicular to the plane. To explore the interplay between magnetic field and a strong light-matter coupling, we employ a fully microscopic theory that explicitly incorporates electrons, holes and photons in a semiconductor microcavity. Furthermore, we exploit a mapping between the 2D harmonic oscillator and the 2D hydrogen atom that allows us to efficiently solve the problem numerically for the entire Rydberg series as well as for the ground-state exciton. In contrast to previous approaches, we can readily obtain the real-space exciton wave functions and we show how they shrink in size with increasing magnetic field, which mirrors their increasing interaction energy and oscillator strength. We compare our theory with recent experiments on exciton-polaritons in GaAs heterostructures in an external magnetic field and we find excellent agreement with the measured polariton energies. Crucially, we are able to capture the observed light-induced changes to the exciton in the regime of very strong light-matter coupling where a perturbative coupled oscillator description breaks down. Our work can guide future experimental efforts to engineer and control Rydberg excitons and exciton-polaritons in a range of 2D materials.
Configuration of five points in $\mathbb P^3$ and their limits

We give a classiﬁcation of ordered ﬁve points in P 3 under the diagonal action of GL 4 over an algebraically closed ﬁeld of characteristic 0, by an explicit description of the diagonal action of GL 4 on the quintuple of the projective varieties P 3 . This is the second simplest setting, where a reductive subgroup of H of G has an open orbit in a (generalised) ﬂag variety X of G but #( H \ X ) = ∞ . The closure relations among inﬁnitely many orbits are also given.