Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Quantum Algorithms for Trace Distance Estimation

In quantum information, trace distance is a basic metric of distinguishability between quantum states. However, there is no known efficient approach to estimate the value of trace distance in general. In this paper, we propose efficient quantum algorithms for estimating the trace distance within additive error <inline-formula> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> between mixed quantum states of rank <inline-formula> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula>. Specifically, we first provide a quantum algorithm using <inline-formula> <tex-math notation="LaTeX">$r \cdot \widetilde O \left ({1/\varepsilon ^{2}}\right)$ </tex-math></inline-formula> queries to the quantum circuits that prepare the purifications of quantum states. Then, we modify this quantum algorithm to obtain another algorithm using <inline-formula> <tex-math notation="LaTeX">$\widetilde O \left ({r^{2}/\varepsilon ^{5}}\right)$ </tex-math></inline-formula> samples of quantum states, which can be applied to quantum state certification. These algorithms have query/sample complexities that are independent of the dimension <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> of quantum states, and their time complexities only incur an extra <inline-formula> <tex-math notation="LaTeX">$O \left ({\log \left ({N}\right)}\right)$ </tex-math></inline-formula> factor. In addition, we show that the decision version of low-rank trace distance estimation is <inline-formula> <tex-math notation="LaTeX">$\mathsf {BQP}$ </tex-math></inline-formula>-complete.
Deep-Learning-Aided Low-Complexity DOA Estimators for Ultra-Massive MIMO Overlapped Receive Array

Massive multiple input multiple output(MIMO)-based fully-digital receive antenna arrays bring huge amount of complexity to both traditional direction of arrival(DOA) estimation algorithms and neural network training. To address this challenge, two estimators called OPSC and OSAP-CBAM-CNN are proposed in this paper. The computational complexity of the traditional DOA algorithm is first considered to be reduced by dividing the total set of antennas into multiple overlapped subarrays uniformly, each subarray crosses each other proportionally and performs DOA estimation to generate coarse angles, and all angles are coherently combined to get the better estimation, the final DOA estimation can given by maximum likelihood alternating projection(ML-AP) in a very small range. To further reduce the complexity of traditional estimation algorithms, deep neural networks(DNN) are utilized to offline train the relationship between the received signal covariance matrix and the estimated angles. Due to the high complexity of the training network based on large-scale arrays, in the OSAP-CBAM-CNN method, the complex network is divided into several smaller networks based on the overlapped subarray to give rough DOA estimations, followed by coherent combining and AP algorithm to get the final DOA estimation. Simulation results show that as the number of antennas goes to large-scale, the proposed methods can achieve a remarkable complexity reduction over conventional ML-AP algorithm.
#EndSARS Protest: Discourse and Mobilisation on Twitter

Using the @NGRPresident Twitter handle, the Government of Nigeria issued a special directive banning Special Anti-Robbery Squad (SARS) with immediate e ﬀ ect. The SARS is a special police unit under the Nigeria Police Force tasked with the responsibility of ﬁghting violent crimes. However, the unit has been accused of waves of human rights abuse across the nation. According to a report by Amnesty International, between January 2017 and May 2020, 82 cases of police brutality have been committed. This has led to one of the major protests demanding more mea-sures to be taken. The #EndSARS hashtag was widely used by the protesters to amplify their messages and reach out to wider communities on Twitter. In this study, we present a critical analysis of how the online protest unfolded. Essentially, we examine how the protest evolves on Twitter, the nature of engagement with the protest themes, the factors inﬂuencing the protest and public perceptions about the online movement. We found that the mobilisation strategies include direct and indirect engagements with inﬂuential users, sharing direct stories and vicarious experiences. Also, there is evidence that suggests the deployment of automated accounts to promote the course of the protest. In terms of participation, over 70% of the protest is conﬁned within a few states in Nigeria, and the diaspora communities also lent their voices to the movement. The most active users are not those with high followership, and the majority of the protesters utilised mobile devices, accounting for 88% to mobilise and report on the protest. Moreover, we have examined how social media users interact with the #EndSARS movement and the response from the wider online communities. Needless to say, the themes in the online discourse are mostly about ending SARS and vicarious experiences with the police, however, there are topics around police reform and demand for regime change. We surmise that injecting a political undertone could lead to setbacks in social activism as it will likely provoke counter protest.
CS-lol: a Dataset of Viewer Comment with Scene in E-sports Live-streaming

Billions of live-streaming viewers share their opinions on scenes they are watching in real-time and interact with the event, commentators as well as other viewers via text comments. Thus, there is necessary to explore viewers’ comments with scenes in E-sport live-streaming events. In this paper, we developed CS-lol, a new large-scale dataset containing comments from viewers paired with descriptions of game scenes in E-sports live-streaming. Moreover, we propose a task, namely viewer comment retrieval, to retrieve the viewer comments for the scene of the live-streaming event. Results on a series of baseline retrieval methods derived from typical IR evaluation methods show our task as a challenging task. Finally, we release CS-lol and baseline implementation to the research community as a resource.
Automated speech- and text-based classification of neuropsychiatric conditions in a multidiagnostic setting

Speech patterns have been identified as potential diagnostic markers for neuropsychiatric conditions. However, most studies only compare a single clinical group to healthy controls, whereas clinical practice often requires differentiating between multiple potential diagnoses (multiclass settings). To address this, we assembled a dataset of repeated recordings from 420 participants (67 with major depressive disorder, 106 with schizophrenia and 46 with autism, as well as matched controls), and tested the performance of a range of conventional machine learning models and advanced Transformer models on both binary and multiclass classification, based on voice and text features. While binary models performed comparably to previous research (F1 scores between 0.54-0.75 for autism spectrum disorder, ASD; 0.67-0.92 for major depressive disorder, MDD; and 0.71-0.83 for schizophrenia); when differentiating between multiple diagnostic groups performance decreased markedly (F1 scores between 0.35-0.44 for ASD, 0.57-0.75 for MDD, 0.15-0.66 for schizophrenia, and 0.38-0.52 macro F1). Combining voice and text-based models yielded increased performance, suggesting that they capture complementary diagnostic information. Our results indicate that models trained on binary classification may learn to rely on markers of generic differences between clinical and non-clinical populations, or markers of clinical features that overlap across conditions, rather than identifying markers specific to individual conditions. We provide recommendations for future research in the field, suggesting increased focus on developing larger transdiagnostic datasets that include more fine-grained clinical features, and that can support the development of models that better capture the complexity of neuropsychiatric conditions and naturalistic diagnostic assessment.
Robot Duck Debugging: Can Attentive Listening Improve Problem Solving?

While thinking aloud has been reported to positively affect problem-solving, the effects of the presence of an embodied entity (e.g., a social robot) to whom words can be directed remain mostly unexplored. In this work, we investigated the role of a robot in a “rubber duck debugging” setting, by analyzing how a robot’s listening behaviors could support a thinking-aloud problem-solving session. Participants completed two different tasks while speaking their thoughts aloud to either a robot or an inanimate object (a giant rubber duck). We implemented and tested two types of listener behavior in the robot: a rule-based heuristic and a deep-learning-based model. In a between-subject user study with 101 participants, we evaluated how the presence of a robot affected users’ engagement in thinking aloud, behavior during the task, and self-reported user experience. In addition, we explored the impact of the two robot listening behaviors on those measures. In contrast to prior work, our results indicate that neither the rule-based heuristic nor the deep learning robot conditions improved performance or perception of the task, compared to an inanimate object. We discuss potential explanations and shed light on the feasibility of designing social robots as assistive tools in thinking-aloud problem-solving tasks.
SDSS J134441.83+204408.3: A Highly Asynchronous Short-period Magnetic Cataclysmic Variable with a 56 MG Field Strength

When the accreting white dwarf in a magnetic cataclysmic variable star (mCV) has a field strength in excess of 10 MG, it is expected to synchronize its rotational frequency to the binary orbit frequency, particularly at small binary separations, due to the steep radial dependence of the magnetic field. We report the discovery of an mCV (SDSS J134441.83+204408.3, hereafter J1344) that defies this expectation by displaying asynchronous rotation (P spin/P orb = 0.893) in spite of a high surface field strength (B = 56 MG) and a short orbital period (114 minutes). Previously misidentified as a synchronously rotating mCV, J1344 was observed by Transiting Exoplanet Survey Satellite during sector 50, and the resulting power spectrum shows distinct spin and orbital frequencies, along with various sidebands and harmonics. Although there are several other asynchronous mCVs at short orbital periods, the presence of cyclotron humps in J1344's Sloan Digital Sky Survey spectrum makes it possible to directly measure the field strength in the cyclotron-emitting region, and while a previously study estimated 65 MG based on its identification of two cyclotron humps, we revise this to 56 ± 2 MG based on the detection of a third hump and on our modeling of the cyclotron spectrum. Short-period mCVs with field strengths above 10 MG are normally expected to be synchronous, so the highly asynchronous rotation in J1344 presents an interesting challenge for theoretical studies of spin-period evolution.
Improving Reliability of Fine-tuning with Block-wise Optimisation

Finetuning can be used to tackle domain-specific tasks by transferring knowledge. Previous studies on finetuning focused on adapting only the weights of a task-specific classifier or re-optimizing all layers of the pre-trained model using the new task data. The first type of methods cannot mitigate the mismatch between a pre-trained model and the new task data, and the second type of methods easily cause over-fitting when processing tasks with limited data. To explore the effectiveness of fine-tuning, we propose a novel block-wise optimization mechanism, which adapts the weights of a group of layers of a pre-trained model. In our work, the layer selection can be done in four different ways. The first is layer-wise adaptation, which aims to search for the most salient single layer according to the classification performance. The second way is based on the first one, jointly adapting a small number of top-ranked layers instead of using an individual layer. The third is block based segmentation, where the layers of a deep network is segmented into blocks by non-weighting layers, such as the MaxPooling layer and Activation layer. The last one is to use a fixed-length sliding window to group layers block by block. To identify which group of layers is the most suitable for finetuning, the search starts from the target end and is conducted by freezing other layers excluding the selected layers and the classification layers. The most salient group of layers is determined in terms of classification performance. In our experiments, the proposed approaches are tested on an often-used dataset, Tf_flower, by finetuning five typical pre-trained models, VGG16, MobileNet-v1, MobileNet-v2, MobileNet-v3, and ResNet50v2, respectively. The obtained results show that the use of our proposed block-wise approaches can achieve better performances than the two baseline methods and the layer-wise method.
Extracting the quantum geometric tensor of an optical Raman lattice by Bloch-state tomography

In Hilbert space, the geometry of the quantum state is identified by the quantum geometric tensor (QGT), whose imaginary part is the Berry curvature and real part is the quantum metric tensor. Here, we propose and experimentally implement a complete Bloch state tomography to directly measure eigenfunction of an optical Raman lattice for ultracold atoms. Through the measured eigenfunction, the distribution of the complete QGT in the Brillouin zone is reconstructed, with which the topological invariants are extracted by the Berry curvature and the distances of quantum states in momentum space are measured by the quantum metric tensor. Further, we experimentally test a predicted inequality between the Berry curvature and quantum metric tensor, which reveals a deep connection between topology and geometry.
Distribution Aligned Feature Clustering for Zero-Shot Sketch-Based Image Retrieval

Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) is a challenging cross-modal retrieval task. In prior arts, the retrieval is conducted by sorting the distance between the query sketch and each image in the gallery. However, the domain gap and the zero-shot setting make neural networks hard to generalize. This paper tackles the challenges from a new perspective: utilizing gallery image features. We propose a Cluster-then-Retrieve (ClusterRetri) method that performs clustering on the gallery images and uses the cluster centroids as proxies for retrieval. Furthermore, a distribution alignment loss is proposed to align the image and sketch features with a common Gaussian distribution, reducing the domain gap. Despite its simplicity, our proposed method outperforms the state-of-the-art methods by a large margin on popular datasets, e.g., up to 31% and 39% relative improvement of mAP@all on the Sketchy and TU-Berlin datasets.
Neighborhood-based Hypergraph Core Decomposition

We propose neighborhood-based core decomposition : a novel way of decomposing hypergraphs into hierarchical neighborhood-cohesive subhypergraphs. Alternative approaches to decomposing hypergraphs, e.g., reduction to clique or bipartite graphs, are not meaningful in certain applications, the later also results in inefficient decomposition; while existing degree-based hypergraph decomposition does not distinguish nodes with different neighborhood sizes. Our case studies show that the proposed decomposition is more effective than degree and clique graph-based decompositions in disease intervention and in extracting provably approximate and application-wise meaningful densest subhypergraphs. We propose three algorithms: Peel, its efficient variant E-Peel, and a novel local algorithm: Local-core with parallel implementation. Our most efficient parallel algorithm Local-core(P) decomposes hypergraph with 27M nodes and 17M hyperedges in-memory within 91 seconds by adopting various optimizations. Finally, we develop a new hypergraph-core model, the (neighborhood, degree)-core by considering both neighborhood and degree constraints, design its decomposition algorithm Local-core+Peel, and demonstrate its superiority in spreading diffusion.
Using Kaldi for Automatic Speech Recognition of Conversational Austrian German

As dialogue systems are becoming more and more interac-tional and social, also the accurate automatic speech recognition (ASR) of conversational speech is of increasing im-portance. This shifts the focus from short, spontaneous, task-oriented dialogues to the much higher complexity of casual face-to-face conversations. However, the collection and annotation of such conversations is a time-consuming process and data is sparse for this speciﬁc speaking style. This paper presents ASR experiments with read and conversational Austrian German as target. In order to deal with having only limited resources available for conversational German and, at the same time, with a large variation among speakers with respect to pronunciation characteristics, we improve a Kaldi-based ASR system by incorporating a (large) knowledge-based pronunciation lexicon, while exploring different data-based methods to restrict the number of pronunciation variants for each lexical entry. We achieve best WER of 0 . 4 % on Austrian German read speech and best average WER of 48 . 5 % on conversational speech. We ﬁnd that by using our best pronunciation lexicon a similarly high performance can be achieved than by increasing the size of the data used for the language model by approx. 360% to 760%. Our ﬁndings indicate that for low-resource scenarios – despite the general trend in speech technology towards using data-based methods only – knowledge-based approaches are a successful, efﬁcient method.
Emergent Electronic Kagome Lattice in Correlated Charge-Density-Wave State of 1T-TaS$_2$

: Quantum materials with tunable correlated and/or topological electronic states, such as the electronic Kagome lattice, provide an ideal platform to study the exotic quantum properties. However, the real-space investigations on the correlated electronic Kagome lattice have been rarely reported. Herein, we report on the electronic Kagome lattice emerging in the correlated charge–density–wave (CDW) state of 1 T -TaS 2 at ~200 K via variable-temperature scanning tunneling microscopy (VT-STM). This emergent Kagome lattice can be considered a fractional electron-filling superstructure with reduced translational and rotational symmetries, confirmed by STM measurements and density functional theory simulations. The characteristic band structure and density of states of this electronic Kagome lattice are further explored based on theoretical calculations. Our results demonstrate a self-organized electronic Kagome lattice from the correlated CDW state via the effective tuning parameter of temperature and provide a platform to directly explore the interplay of correlated electrons and topological physics.
A machine-learning procedure to detect network attacks

The goal of this note is to assess whether simple machine learning algorithms can be used to determine whether and how a given network has been attacked. The procedure is based on the $k$-Nearest Neighbor and the Random Forest classification schemes, using both intact and attacked Erd\H{o}s-R\'enyi, Barabasi-Albert and Watts-Strogatz networks to train the algorithm. The types of attacks we consider here are random failures and maximum-degree or maximum-betweenness node deletion. Each network is characterized by a list of 4 metrics, namely the normalized reciprocal maximum degree, the global clustering coefficient, the normalized average path length and the assortativity: a statistical analysis shows that this list of graph metrics is indeed significantly different in intact or damaged networks. We test the procedure by choosing both artificial and real networks, performing the attacks and applying the classification algorithms to the resulting graphs: the procedure discussed here turns out to be able to distinguish between intact networks and those attacked by the maximum-degree of maximum-betweenness deletions, but cannot detect random failures. Our results suggest that this approach may provide a basis for the analysis and detection of network attacks.
Semantics for a Logic of Presuppositions

In 1952 P. F. Strawson proposed a logic of presuppositions. It is an interpretation of Aristotelian logic, i.e. of the logic of the traditional syllogism. In 1981 Richard Diaz published a monograph in which he presented truth-relevant logic. This paper shows that truth-relevant logic is but a propositional version of the logic of presuppositions. A semantics of the logic of presuppositions is developed using truth-relevant logic. The semantics is then further extended to polyadic logic and some consequences discussed.
Capillary surfers: Wave-driven particles at a vibrating fluid interface

We present an experimental study of capillary surfers, a new fluid-mediated active system that bridges the gap between dissipation- and inertia-dominated regimes. Surfers are wave-driven particles that self-propel and interact on a fluid interface via an extended field of surface waves. A surfer's speed and interaction with its environment can be tuned broadly through the particle, fluid, and vibration parameters. The wave nature of interactions among surfers allows for multistability of interaction modes and promises a number of novel collective behaviors.
Doubly-Robust Inference for Conditional Average Treatment Effects with High-Dimensional Controls

Plausible identification of conditional average treatment effects (CATEs) may rely on controlling for a large number of variables to account for confounding factors. In these high-dimensional settings, estimation of the CATE requires estimating first-stage models whose consistency relies on correctly specifying their parametric forms. While doubly-robust estimators of the CATE exist, inference procedures based on the second stage CATE estimator are not doubly-robust. Using the popular augmented inverse propensity weighting signal, we propose an estimator for the CATE whose resulting Wald-type confidence intervals are doubly-robust. We assume a logistic model for the propensity score and a linear model for the outcome regression, and estimate the parameters of these models using an `1 (Lasso) penalty to address the high dimensional covariates. Our proposed estimator remains consistent at the nonparametric rate and our proposed pointwise and uniform confidence intervals remain asymptotically valid even if one of the logistic propensity score or linear outcome regression models are misspecified. ∗We are grateful to Denis Chetverikov, Andres Santos, Zhipeng Liao, Jinyong Hahn, Rosa Matzkin, Shuyang Sheng and participants in UCLA’s Econometrics Proseminar for helpful comments. †Corresponding author: mnavjeevan@g.ucla. edu. 1 ar X iv :2 30 1. 06 28 3v 1 [ ec on . E M ] 1 6 Ja n 20 23