Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantum Vector DC Magnetometry via Selective Phase Accumulation

Precision measurement of magnetic fields is an important goal for fundamental science and practical sensing technology. Sensitive detection of a vector magnetic field is a crucial issue in quantum magnetometry, it remains a challenge to estimate a vector DC magnetic field with high efficiency and high precision. Here, we propose a general protocol for quantum vector DC magnetometry via selective phase accumulation of both non-entangled and entangled quantum probes. Based upon the Ramsey interferometry, our protocol may achieve selective phase accumulation of only one magnetic field component by inserting well-designed pulse sequence. In the parallel scheme, three parallel quantum interferometries are utilized to estimate three magnetic field components independently.In the sequential scheme, by applying a pulse sequence along different directions, three magnetic field components can be estimated simultaneously via only one quantum interferometry. In particular, if the input state is an entangled state such as the Greenberger-Horne-Zeilinger state, the measurement precisions of all three components may approach the Heisenberg limit. Our study not only develops a general protocol for measuring vector magnetic fields via quantum probes, but also provides a feasible way to achieve Heisenberg-limited multi-parameter estimation via many-body quantum entanglement.
On a mixed discontinuous Galerkin method

Abstract. For the model Poisson problem we study the stabilized Bassi-Rebay discontinuous Galerkin method. The method is written in a mixed formulation, in which independent and fully discontinuous basis functions are used both for the scalar unknown and its flux. The continuity requirement is imposed by Nitsche’s technique [Abh. Math. Sem. Univ. Hamburg, 36 (1970/71), pp. 9–15]. In the implementation the flux is then eliminated by local condensing. We show that the method is stable and optimally convergent for all positive values of the stability parameter. We also perform an a posteriori error analysis. The theoretical results are verified by numerical computations.
Onset conditions for vortex breakdown in supersonic flows

This study proposes an onset condition of shock-free supersonic vortex breakdown from the axial momentum variation, which applies in the presence or absence of a stagnation point. The condition is derived from a comprehensive approach to vortex breakdown. Supersonic breakdown appeared when the swirl parameter and Mach number were small. Moreover, bubble-type breakdowns with a stagnation point, which occur in subsonic conditions, could not occur under the supersonic condition in the present analysis. The predicted breakdowns under this condition were consistent with the results of the three-dimensional numerical simulations for Mach numbers ranging from 1.5 to 5.0. Supersonic vortex breakdowns were clearly captured by the helicity contours in the numerical results. The threshold of the downstream Mach number required for spiral breakdown with no stagnation point was also theoretically derived and verified in numerical results. These findings provide new insights into vortex breakdown in supersonic flows.
Sub-Nyquist Sampling OFDM Radar

In this paper, we propose a sub-Nyquist sampling (SNS) orthogonal frequency-division multiplexing (OFDM) radar system capable of reducing the analog-to-digital converter (ADC) sampling rate in OFDM radar without any additional manipulations of its hardware and waveform. To this end, the proposed system utilizes the ADC sampling rate of <inline-formula> <tex-math notation="LaTeX">${B/L}$ </tex-math></inline-formula> to sample the received baseband signal with a bandwidth of <inline-formula> <tex-math notation="LaTeX">$B$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">${L}$ </tex-math></inline-formula> is a positive proper divisor of the number of subcarriers. This divides the baseband signal into <inline-formula> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula> sub-bands, folding into a sub-Nyquist frequency band due to aliasing. By leveraging known modulation symbols of the transmitted signal, the folded signal can be unfolded to the full-band signal. This allows an estimation of target ranges with the range resolution of the full signal bandwidth <inline-formula> <tex-math notation="LaTeX">$B$ </tex-math></inline-formula> without the degradation of the maximum unambiguous range. During the signal-unfolding process, the signals from other sub-bands remain as symbol-mismatch noise (SMN), which significantly degrades the signal-to-noise ratio (SNR) of the detected targets. It also causes weaker targets to be submerged under the noise in range profiles. To resolve this, a symbol-mismatch noise cancellation (SMNC) technique is also proposed, which reconstructs the interfering signals from the other sub-bands using the detected targets and subtracts them from the unfolded signal. As a result, the proposed sub-Nyquist sampling OFDM radar and corresponding signal processing technique enable a reduction in the ADC sampling rate by the ratio of <inline-formula> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula> while incurring only a <inline-formula> <tex-math notation="LaTeX">$10\log _{10}{L}$ </tex-math></inline-formula> increase in the noise due to noise folding. This is validated through simulations and measurements with various sub-sampling ratios.
MASC: A Tool for Mutation-Based Evaluation of Static Crypto-API Misuse Detectors

While software engineers are optimistically adopting crypto-API misuse detectors (or crypto-detectors) in their software development cycles, this momentum must be accompanied by a rigorous understanding of crypto-detectors’ effectiveness at finding crypto-API misuses in practice. This demo paper presents the technical details and usage scenarios of our tool, namely Mutation Analysis for evaluating Static Crypto-API misuse detectors (MASC). We developed 12 generalizable, usage based mutation operators and three mutation scopes, namely Main Scope, Similarity Scope, and Exhaustive Scope, which can be used to expressively instantiate compilable variants of the crypto-API misuse cases. Using MASC, we evaluated nine major crypto-detectors, and discovered 19 unique, undocumented flaws. We designed MASC to be configurable and user-friendly; a user can configure the parameters to change the nature of generated mutations. Furthermore, MASC comes with both Command Line Interface and Web-based front-end, making it practical for users of different levels of expertise.
Multi-interactive Feature Learning and a Full-time Multi-modality Benchmark for Image Fusion and Segmentation

Multi-modality image fusion and segmentation play a vital role in autonomous driving and robotic operation. Early efforts focus on boosting the performance for only one task, e.g., fusion or segmentation, making it hard to reach ‘Best of Both Worlds’. To overcome this issue, in this paper, we propose a Multi-interactive Feature learning architecture for image fusion and Segmentation, namely SegMiF, and exploit dual-task correlation to promote the performance of both tasks. The SegMiF is of a cascade structure, containing a fusion sub-network and a commonly used segmentation sub-network. By slickly bridging intermediate features between two components, the knowledge learned from the segmentation task can effectively assist the fusion task. Also, the benefited fusion network supports the segmentation one to perform more pretentiously. Besides, a hierarchical interactive attention block is established to ensure fine-grained mapping of all the vital information between two tasks, so that the modality/semantic features can be fully mutual-interactive. In addition, a dynamic weight factor is introduced to automatically adjust the corresponding weights of each task, which can balance the interactive feature correspondence and break through the limitation of laborious tuning. Furthermore, we construct a smart multi-wave binocular imaging system and collect a full-time multi-modality benchmark with 15 annotated pixel-level categories for image fusion and segmentation. Extensive experiments on several public datasets and our benchmark demonstrate that the proposed method outputs visually appealing fused images and perform averagely 7.66% higher segmentation mIoU in the real-world scene than the state-of-the-art approaches. The source code and benchmark are available at https://github.com/JinyuanLiu-CV/SegMiF.
Learning Optimal Admission Control in Partially Observable Queueing Networks

We present an efficient reinforcement learning algorithm that learns the optimal admission control policy in a partially observable queueing network. Specifically, only the arrival and departure times from the network are observable, and optimality refers to the average holding/rejection cost in infinite horizon. While reinforcement learning in Partially Observable Markov Decision Processes (POMDP) is prohibitively expensive in general, we show that our algorithm has a regret that only depends sub-linearly on the maximal number of jobs in the network, $S$. In particular, in contrast with existing regret analyses, our regret bound does not depend on the diameter of the underlying Markov Decision Process (MDP), which in most queueing systems is at least exponential in $S$. The novelty of our approach is to leverage Norton's equivalent theorem for closed product-form queueing networks and an efficient reinforcement learning algorithm for MDPs with the structure of birth-and-death processes.
Beyond Kitaev physics in strong spin-orbit coupled magnets.

We review the recent advances and current challenges in the field of strong spin-orbit coupled Kitaev materials, with a particular emphasis on the physics beyond the exactly-solvable Kitaev spin liquid point. To this end, we present a comprehensive overview of the key exchange interactions in candidate materials with a specific focus on systems featuring effective $J_{\rm eff}\! =\! 1/2$ magnetic moments. This includes, but not limited to, $5d^5$ iridates, $4d^5$ ruthenates and $3d^7$ cobaltates. Our exploration covers the microscopic origins of these interactions, along with a systematic attempt to map out the most intriguing correlated regimes of the multi-dimensional parameter space. Our approach is guided by robust symmetry and duality transformations as well as insights from a wide spectrum of analytical and numerical studies. We also survey higher spin Kitaev models and recent exciting results on quasi-one-dimensional models and discuss their relevance to higher-dimensional models. Finally, we highlight some of the key questions in the field as well as future directions.
Adapting the NICT-JLE Corpus for Disfluency Detection Models

The detection of disfluencies such as hesitations, repetitions and false starts commonly found in speech is a widely studied area of research. With a standardised process for evaluation using the Switchboard Corpus, model performance can be easily compared across approaches. This is not the case for disfluency detection research on learner speech, however, where such datasets have restricted access policies, making comparison and subsequent development of improved models more challenging. To address this issue, this paper describes the adaptation of the NICT-JLE corpus, containing approximately 300 hours of English learners' oral proficiency tests, to a format that is suitable for disfluency detection model training and evaluation. Points of difference between the NICT-JLE and Switchboard corpora are explored, followed by a detailed overview of adaptations to the tag set and meta-features of the NICT-JLE corpus. The result of this work provides a standardised train, heldout and test set for use in future research on disfluency detection for learner speech.
Regulating AI: Applying Insights from Behavioural Economics and Psychology to the Application of Article 5 of the EU AI Act

Article 5 of the European Union’s Artificial Intelligence Act is intended to regulate AI use to prevent potentially harmful consequences. Nevertheless, applying this legislation practically is likely to be challenging because of ambiguously used terminologies and because it fails to specify which manipulation techniques may be invoked by AI, potentially leading to significant harm. This paper aims to bridge this gap by defining key terms and demonstrating how AI may invoke these techniques, drawing from insights in psychology and behavioural economics. First, this paper provides definitions of the terms “subliminal techniques”, “manipulative techniques” and “deceptive techniques”. Secondly, we identified from the literature in cognitive psychology and behavioural economics three subliminal and five manipulative techniques and exemplify how AI might implement these techniques to manipulate users in real-world case scenarios. These illustrations may serve as a practical guide for stakeholders to detect cases of AI manipulation and consequently devise preventive measures. Article 5 has also been criticised for offering inadequate protection. We critically assess the protection offered by Article 5, proposing specific revisions to paragraph 1, points (a) and (b) of Article 5 to increase its protective effectiveness.
ES-MVSNet: Efficient Framework for End-to-end Self-supervised Multi-View Stereo

Compared to the multi-stage self-supervised multi-view stereo (MVS) method, the end-to-end (E2E) approach has received more attention due to its concise and efficient training pipeline. Recent E2E self-supervised MVS approaches have integrated third-party models (such as optical flow models, semantic segmentation models, NeRF models, etc.) to provide additional consistency constraints, which grows GPU memory consumption and complicates the model's structure and training pipeline. In this work, we propose an efficient framework for end-to-end self-supervised MVS, dubbed ES-MVSNet. To alleviate the high memory consumption of current E2E self-supervised MVS frameworks, we present a memory-efficient architecture that reduces memory usage by 43% without compromising model performance. Furthermore, with the novel design of asymmetric view selection policy and region-aware depth consistency, we achieve state-of-the-art performance among E2E self-supervised MVS methods, without relying on third-party models for additional consistency signals. Extensive experiments on DTU and Tanks&Temples benchmarks demonstrate that the proposed ES-MVSNet approach achieves state-of-the-art performance among E2E self-supervised MVS methods and competitive performance to many supervised and multi-stage self-supervised methods.
Seasonality Based Reranking of E-commerce Autocomplete Using Natural Language Queries

Query autocomplete (QAC) also known as typeahead, suggests list of complete queries as user types prefix in the search box. It is one of the key features of modern search engines specially in e-commerce. One of the goals of typeahead is to suggest relevant queries to users which are seasonally important. In this paper we propose a neural network based natural language processing (NLP) algorithm to incorporate seasonality as a signal and present end to end evaluation of the QAC ranking model. Incorporating seasonality into autocomplete ranking model can improve autocomplete relevance and business metric.
N-gram Boosting: Improving Contextual Biasing with Normalized N-gram Targets

Accurate transcription of proper names and technical terms is particularly important in speech-to-text applications for business conversations. These words, which are essential to understanding the conversation, are often rare and therefore likely to be under-represented in text and audio training data, creating a significant challenge in this domain. We present a two-step keyword boosting mechanism that successfully works on normalized unigrams and n-grams rather than just single tokens, which eliminates missing hits issues with boosting raw targets. In addition, we show how adjusting the boosting weight logic avoids over-boosting multi-token keywords. This improves our keyword recognition rate by 26% relative on our proprietary in-domain dataset and 2% on LibriSpeech. This method is particularly useful on targets that involve non-alphabetic characters or have non-standard pronunciations.
Cognitive TransFuser: Semantics-guided Transformer-based Sensor Fusion for Improved Waypoint Prediction

Sensor fusion approaches for intelligent self-driving agents remain key to driving scene understanding given visual global contexts acquired from input sensors. Specifically, for the local waypoint prediction task, single-modality networks are still limited by strong dependency on the sensitivity of the input sensor, and thus recent works therefore promote the use of multiple sensors in fusion in feature level in practice. While it is well known that multiple data modalities encourage mutual contextual exchange, it requires global 3D scene understanding in real-time with minimal computation upon deployment to practical driving scenarios, thereby placing greater significance on the training strategy given a limited number of practically usable sensors. In this light, we exploit carefully selected auxiliary tasks that are highly correlated with the target task of interest (e.g., traffic light recognition and semantic segmentation) by fusing auxiliary task features and also using auxiliary heads for waypoint prediction based on imitation learning. Our RGB-LIDAR-based multi-task feature fusion network, coined Cognitive TransFuser, augments and exceeds the baseline network by a significant margin for safer and more complete road navigation in the CARLA simulator. We validate the proposed network on the Town05 Short and Town05 Long Benchmark through extensive experiments, achieving up to 44.2 FPS real-time inference time.
DCTM: Dilated Convolutional Transformer Model for Multimodal Engagement Estimation in Conversation

Conversational engagement estimation is posed as a regression problem, entailing the identification of the favorable attention and involvement of the participants in the conversation. This task arises as a crucial pursuit to gain insights into human's interaction dynamics and behavior patterns within a conversation. In this research, we introduce a dilated convolutional Transformer for modeling and estimating human engagement in the MULTIMEDIATE 2023 competition. Our proposed system surpasses the baseline models, exhibiting a noteworthy 7% improvement on test set and 4% on validation set. Moreover, we employ different modality fusion mechanism and show that for this type of data, a simple concatenated method with self-attention fusion gains the best performance.
Text2KGBench: A Benchmark for Ontology-Driven Knowledge Graph Generation from Text

The recent advances in large language models (LLM) and foundation models with emergent capabilities have been shown to improve the performance of many NLP tasks. LLMs and Knowledge Graphs (KG) can complement each other such that LLMs can be used for KG construction or completion while existing KGs can be used for different tasks such as making LLM outputs explainable or fact-checking in Neuro-Symbolic manner. In this paper, we present Text2KGBench, a benchmark to evaluate the capabilities of language models to generate KGs from natural language text guided by an ontology. Given an input ontology and a set of sentences, the task is to extract facts from the text while complying with the given ontology (concepts, relations, domain/range constraints) and being faithful to the input sentences. We provide two datasets (i) Wikidata-TekGen with 10 ontologies and 13,474 sentences and (ii) DBpedia-WebNLG with 19 ontologies and 4,860 sentences. We define seven evaluation metrics to measure fact extraction performance, ontology conformance, and hallucinations by LLMs. Furthermore, we provide results for two baseline models, Vicuna-13B and Alpaca-LoRA-13B using automatic prompt generation from test cases. The baseline results show that there is room for improvement using both Semantic Web and Natural Language Processing techniques.
Sowing 'Seeds of Doubt': Cottage Industries of Election and Medical Misinformation in Brazil and the United States

We conducted ethnographic research with 31 misinformation creators and consumers in Brazil and the US before, during, and after a major election to understand the consumption and production of election and medical misinformation. This study contributes to research on misinformation ecosystems by focusing on poorly understood small players, or"micro-influencers", who create misinformation in peer-to-peer networks. We detail four key tactics that micro-influencers use. First, they typically disseminate"gray area"content rather than expert-falsified claims, using subtle aesthetic and rhetorical tactics to evade moderation. Second, they post in small, closed groups where members feel safe and predisposed to trust content. Third, they explicitly target misinformation consumers' emotional and social needs. Finally, they post a high volume of short, repetitive content to plant seeds of doubt and build trust in influencers as unofficial experts. We discuss the implications these micro-influencers have for misinformation interventions and platforms' efforts to moderate misinformation.
Cannabis use and age at onset of symptoms in subjects at clinical high risk for psychosis

Dragt S, Nieman DH, Schultze‐Lutter F, van der Meer F, Becker H, de Haan L, Dingemans PM, Birchwood M, Patterson P, Salokangas RKR, Heinimaa M, Heinz A, Juckel G, Graf von Reventlow H, French P, Stevens H, Ruhrmann S, Klosterkötter J, Linszen DH, on behalf of the EPOS group. Cannabis use and age at onset of symptoms in subjects at clinical high risk for psychosis.
Determination of Chemical and Microbiological Quality in Commercial Tahini Samples

Tahini is a food product that is frequently preferred both directly and as a main ingredient in various ready-to-eat food products and is consumed with interest, especially in the Middle East geography. Its reputation has recently spread across continents to countries like Canada and the United States of America. Tahini was subjected to a number of analyses to ascertain its physicochemical and microbiological quality within the context of the study since it is a highly consumed product with high consumption and demand values. In this context, tahini samples of 10 different brands were collected from producers and commercially sold markets in Balikesir and Bursa. Samples were taken from two different lot numbers for each company. As a result of the analyzes made on the tahini samples, it was determined that the total oil amount of the samples changed between 49.76-58.7%, the salt amount changed between 0.001-0.0027%, the ash amount was between 1.02%-1.28%, and the moisture value was between 1.1-1.5%; as a result of microbiological cultivations in which the presence of yeast and mold were analyzed, an average of 75 CFU/g viability was determined, while Escherichia coli, which was screened as an indicator of fecal contamination, Staphylococcus aureus and Salmonella spp, which are pathogenic microorganisms, were not found to be contaminated in all tahini samples. One of the tahini samples was not found in accordance with the Turkish Food Codex Tahini Communiqué (TFC) in terms of the amount of oil. It is concluded that 90% of the samples were manufactured in line with the TFC when the results of the chemical and microbiological analyses were combined.