Miracle-dz/UniFAR-FTUs huggingface dataset:
Further development of SDSPT2 for strongly correlated electrons

ABSTRACT The static-dynamic-static multi-state multi-reference second-order perturbation theory (SDS-MS-MRPT2 or SDSPT2 in short), which employs a multi-partitioned (state-dependent) Møller–Plesset-like diagonal operator for the zeroth-order Hamiltonian and individual configuration state functions for the perturbers, is reconsidered by making use of the state-universal Dyall Hamiltonian for the zeroth-order Hamiltonian and the union of spin-adapted, internally contracted basis functions generated from all the reference functions for the perturbers. The graphical unitary group approach along with particle–hole correspondence is employed in the evaluation of Hamiltonian matrix elements. This particular variant of SDSPT2 can also be regarded as an extension of the spin-adapted, partially contracted variant of NEVPT2 (n-electron valence state second-order perturbation theory). The performance of SDSPT2 is examined by taking C2, LiF, and Fe(C5H5)2 as examples. The results show that the low-lying states of the systems can be obtained very accurately.
Comparative Evaluation of Different Emulators for Cardiac Mechanics

This paper outlines a comparison of different emulation based approaches to the task of parameter inference in a biomechanical model of the left ventricle of the heart, where the emulation models can account for variations in left ventricle geometry. Models considered include Gaussian processes, neural networks and random forests. We are able to achieve accurate parameter estimation for two of the model parameters, while the extension of statistical emulation to the multi geometry case allows us to observe identifiability issues in some of the model parameters. This was not observed in our previous single geometry emulation studies. Overall, this study shows the ability to generalize the single geometry emulation strategy to multiple geometries, pushing us closer towards in clinic decision support systems.
Autonomous dishwasher loading from cluttered trays using pre‐trained deep neural networks

Autonomous dishwasher loading is a benchmark problem in robotics that highlights the challenges of robotic perception, planning, and manipulation in an unstructured environment. Current approaches resort to a specialized solution, however, these technologies are not viable in a domestic setting. Learning‐based solutions seem promising for a general purpose solutions; however, they require large amounts of catered data to be applied in real‐world scenarios. This article presents a novel learning‐based solution without a training phase using pre‐trained object detection networks. By developing a perception, planning, and manipulation framework around an off‐the‐shelf object detection network, we are able to develop robust pick‐and‐place solutions that are easy to develop and general purpose requiring only a RGB feedback and a pinch gripper. Analysis of a real‐world canteen tray data is first performed and used for developing our in‐lab experimental setup. Our results obtained from real‐world scenarios indicate that such approaches are highly desirable for plug‐and‐play domestic applications with limited calibration. All the associated data and code of this work are shared in a public repository.
The Role Of Negative Personality Traits In Psychological Adaptation

The paper reflects one of the aspects of the research carried out within the framework of the project “Nature of variability of negative personality traits: a twin study”. The research reviews the adaptive component of negative personal traits. The sample of the study consisted of 136 members of monozygotic twins and 401 only children in their families aged 18-78. Life satisfaction was a generalized metric of psychological adaptation. It is shown that a number of negative personality traits (in particular, narcissism, authoritarianism) positively correlate with life satisfaction. The biased value of various personality traits, which can also indirectly serve as an indicator of adaptability of these psychological properties, was assessed using a semantic differential. The age-related changes in the perfect image of the self, which are associated primarily with some more attractive negative personal traits, as well as the multidirectional desired changes in personality traits in themselves and the twin (more power and conflict in themselves and less of the same in the brother/sister) also indicate that a number of negative personal traits play a positive role in psychological adaptation. It is assumed that these traits can have a compensatory function during stress, and the destructiveness of these traits can have a greater impact on people around than on themselves.
Radiative effects of long-range-transported Saharan air layers as determined from airborne lidar measurements

Abstract. The radiative effect of long-range-transported Saharan air layers is investigated on the basis of simultaneous airborne high-spectral-resolution and differential-absorption lidar measurements in the vicinity of Barbados. Within the observed Saharan air layers, increased water vapor concentrations compared to the dry trade wind atmosphere are found. The measured profiles of aerosol optical properties and water vapor mixing ratios are used to characterize the atmospheric composition in radiative transfer calculations, to calculate radiative effects of moist Saharan air layers and to determine radiative heating rate profiles. An analysis based on three case studies reveals that the observed enhanced amounts of water vapor within Saharan air layers have a much stronger impact on heating rate calculations than mineral dust aerosol. Maximum mineral dust short-wave heating and long-wave cooling rates are found at altitudes of highest dust concentration (short wave: +0.5 K d−1; long wave: −0.2 K d−1; net: +0.3 K d−1). However, when considering both aerosol concentrations and measured water vapor mixing ratios in radiative transfer calculations, the maximum heating/cooling rates shift to the top of the dust layer (short wave: +2.2 K d−1; long wave: −6.0 to −7.0 K d−1; net: −4.0 to −5.0 K d−1). Additionally, the net heating rates decrease with height – indicating a destabilizing effect in the dust layers. Long-wave counter-radiation of Saharan air layers is found to reduce cooling at the tops of the subjacent marine boundary layers and might lead to less convective mixing in these layers. The overall short-wave radiative effect of mineral dust particles in Saharan air layers indicates a maximum magnitude of −40 W m−2 at surface level and a maximum of −25 W m−2 at the top of the atmosphere.
NPCFace: Negative-Positive Collaborative Training for Large-scale Face Recognition

The training scheme of deep face recognition has greatly evolved in the past years, yet it encounters new challenges in the large-scale data situation where massive and diverse hard cases occur. Especially in the range of low false accept rate (FAR), there are various hard cases in both positives (intra-class) and negatives (inter-class). In this paper, we study how to make better use of these hard samples for improving the training. The literature approaches this by margin-based formulation in either positive logit or negative logits. However, the correlation between hard positive and hard negative is overlooked, and so is the relation between the margins in positive and negative logits. We find such correlation is significant, especially in the large-scale dataset, and one can take advantage from it to boost the training via relating the positive and negative margins for each training sample. To this end, we propose an explicit collaboration between positive and negative margins sample-wisely. Given a batch of hard samples, a novel Negative-Positive Collaboration loss, named NPCFace, is formulated, which emphasizes the training on both negative and positive hard cases via the collaborative-margin mechanism in the softmax logits, and also brings better interpretation of negative-positive hardness correlation. Besides, the emphasis is implemented with an improved formulation to achieve stable convergence and flexible parameter setting. We validate the effectiveness of our approach on various benchmarks of large-scale face recognition, and obtain advantageous results especially in the low FAR range.
Chemical properties and sensory characteristics of wild boar meat ( Sus scrofa scrofa ) fed with acorns ( Quercus robur )

Background: The wild boar (Sus scrofa scrofa) has favourable meat characteristics compared to the domestic pig particularly regarding the fat and cholesterol content. The present study aimed to determine whether the meat of wild boar fed with 20% or 40% acorns in their diet had different chemical properties and sensory meat characteristics. Methods: A group of six wild boar (Sus scrofa s.) with an initial live weight of 40 kg, received fresh whole Quercus robur acorns at 20% w/w of their daily feedstuff for 60 days, a second group received 40% w/w and a third group consumed commercial feed without acorns (control group). Proximal chemical analysis was performed on the feedstuff, acorns, and meat. Fatty acids and cholesterol in the Longissimus lumborum muscle were measured via gas chromatography. Results: The meat from the control fatty acid group had a higher composition of C16: 0 fatty acids and cholesterol, and also had a lower proportion of monounsaturated and polyunsaturated fatty acids, and of the n6: n3, P/S ratio, than the acorn-fed wild boar groups. Conclusions: The meat of wild board fed with acorn had better characteristics in terms of nutritional factors such as the level of monounsaturated fatty acids, polyunsaturated fatty acids, cholesterol, and sensory characteristics.
Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter

Lexicon information and pre-trained models, such as BERT, have been combined to explore Chinese sequence labeling tasks due to their respective strengths. However, existing methods solely fuse lexicon features via a shallow and random initialized sequence layer and do not integrate them into the bottom layers of BERT. In this paper, we propose Lexicon Enhanced BERT (LEBERT) for Chinese sequence labeling, which integrates external lexicon knowledge into BERT layers directly by a Lexicon Adapter layer. Compared with existing methods, our model facilitates deep lexicon knowledge fusion at the lower layers of BERT. Experiments on ten Chinese datasets of three tasks including Named Entity Recognition, Word Segmentation, and Part-of-Speech Tagging, show that LEBERT achieves state-of-the-art results.
Shared data granularity: A latent dimension of privacy scoring over online social networks

Privacy scoring aims at measuring the privacy violation risk of a user over an online social network (OSN). Existing work in the field rely on possibly biased or emotional survey data and focus only on personel purpose OSNs like Facebook. In contrast to existing work, in this thesis, we work with real-world OSN data collected from LinkedIn, the most popular professional-purpose OSN (ProOSN). Towards this end, we developed an extensive crawler to collect all relevant profile data of 5,389 LinkedIn users, modelled these data using both relational and graph databases and quantitatively analyzed all privacy risk scoring methods in the literature. Additionally, we propose a novel scoring method that consider the granularity of data an OSN user shares on her profile page. Extensive experimental evaluation of existing and proposed scoring methods indicates the effectiveness of the proposed solution.
A Synchronized Action Framework for Responsible Detection of Coordination on Social Media

The study of coordinated manipulation of conversations on social media has become more prevalent as social media's role in amplifying misinformation, hate, and polarization has come under scrutiny. We discuss the implications of successful coordination detection algorithms based on shifts of power, and consider how responsible coordination detection may be carried out through synchronized action. We then propose a Synchronized Action Framework for detection of automated coordination through construction and analysis of multi-view networks. We validate our framework by examining the Reopen America conversation on Twitter, discovering three coordinated campaigns. We further investigate covert coordination surrounding the protests and find the task to be far more complex than examples seen in prior work, demonstrating the need for our multi-view approach. A cluster of suspicious users is identified and the activity of three members is detailed. These users amplify protest messages using the same hashtags at very similar times, though they all focus on different states. Through this analysis, we emphasize both the potential usefulness of coordination detection algorithms in investigating amplification, and the need for careful and responsible deployment of such tools.
Using Distributed Reinforcement Learning for Resource Orchestration in a Network Slicing Scenario

The Network Slicing (NS) paradigm enables the partition of physical and virtual resources among multiple logical networks, possibly managed by different tenants. In such a scenario, network resources need to be dynamically allocated according to the slice requirements. In this paper, we attack the above problem by exploiting a Deep Reinforcement Learning approach. Our framework is based on a distributed architecture, where multiple agents cooperate towards a common goal. The agent training is carried out following the Advantage Actor Critic algorithm, which permits to handle continuous action spaces. By means of extensive simulations, we show that our approach yields better performance than both a static allocation of system resources and an efficient empirical strategy. At the same time, the proposed system ensures high adaptability to different scenarios without the need for additional training.
Communication Complexity of Private Simultaneous Quantum Messages Protocols

The private simultaneous messages model is a non-interactive version of the multiparty secure computation, which has been intensively studied to examine the communication cost of the secure computation. We consider its quantum counterpart, the private simultaneous quantum messages (PSQM) model, and examine the advantages of quantum communication and prior entanglement of this model. In the PSQM model, $k$ parties $P_1,\ldots,P_k$ initially share a common random string (or entangled states in a stronger setting), and they have private classical inputs $x_1,\ldots, x_k$. Every $P_i$ generates a quantum message from the private input $x_i$ and the shared random string (entangled states), and then sends it to the referee $R$. Receiving the messages, $R$ computes $F(x_1,\ldots,x_k)$. Then, $R$ learns nothing except for $F(x_1,\ldots,x_k)$ as the privacy condition. We obtain the following results for this PSQM model. (1) We demonstrate that the privacy condition inevitably increases the communication cost in the two-party PSQM model as well as in the classical case presented by Applebaum, Holenstein, Mishra, and Shayevitz. In particular, we prove a lower bound $(3-o(1))n$ of the communication complexity in PSQM protocols with a shared random string for random Boolean functions of $2n$-bit input, which is larger than the trivial upper bound $2n$ of the communication complexity without the privacy condition. (2) We demonstrate a factor two gap between the communication complexity of PSQM protocols with shared entangled states and with shared random strings by designing a multiparty PSQM protocol with shared entangled states for a total function that extends the two-party equality function. (3) We demonstrate an exponential gap between the communication complexity of PSQM protocols with shared entangled states and with shared random strings for a two-party partial function.
SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoising

On the WikiSQL 1 benchmark, most methods 001 tackle the challenge of text-to-SQL with pre- 002 deﬁned sketch slots and build sophisticated 003 sub-tasks to ﬁll these slots. Though achiev- 004 ing promising results, these methods suffer 005 from over-complex model structure. In this 006 paper, we present a simple yet effective ap- 007 proach that enables auto-regressive sequence- 008 to-sequence model to robust text-to-SQL gen- 009 eration. Instead of formulating the task of 010 text-to-SQL as slot-ﬁlling, we propose to train 011 sequence-to-sequence model with Schema- 012 a ware Denoising (SeaD), which consists of 013 two denoising objectives that train model to ei- 014 ther recover input or predict output from two 015 novel erosion and shufﬂe noises. These model- 016 agnostic denoising objectives act as the aux- 017 iliary tasks for structural data modeling dur- 018 ing sequence-to-sequence generation. In ad- 019 dition, we propose a clause-sensitive execu- 020 tion guided (EG) decoding strategy to over- 021 come the limitation of EG decoding for gen- 022 erative model. The experiments show that the 023 proposed method improves the performance of 024 sequence-to-sequence model in both schema 025 linking and grammar correctness and estab- 026 lishes new state-of-the-art on WikiSQL bench- 027 mark. Our work indicates that the capacity of 028 sequence-to-sequence model for text-to-SQL 029 may have been under-estimated and could be 030 enhanced by specialized denoising task.
$B-L$ model with $A_4\times Z_3\times Z_4$ symmetry for $3+1$ active$-$sterile neutrino mixing

We construct a B − L model with A 4 × Z 3 × Z 4 ﬂavor symmetry which can accounts for the recent 3 + 1 active-sterile neutrino data. The tiny neutrino mass and the mass hierarchy are obtained by the type-I seesaw mechanism. The hierarchy of the lepton masses is satisﬁed by a factor of v H (cid:0) v l Λ (cid:1) 2 ∼ 10 − 4 GeV of the electron mass compared to the muon and tau masses of the order of v H v l Λ ∼ 10 − 1 GeV . The 3 + 1 active-sterile neutrino mixings are predicted to be 0 . 015 ≤ | U 14 | 2 ≤ 0 . 045 , 0 . 004 ≤ | U 24 | 2 ≤ 0 . 012 and 0 . 004 ≤ | U 34 | 2 ≤ 0 . 014 for normal hierarchy while 0 . 020 ≤ | U 14 | 2 ≤ 0 . 045 , 0 . 008 ≤ | U 24 | 2 ≤ 0 . 018 and 0 . 008 ≤ | U 34 | 2 ≤ 0 . 022 for inverted hierarchy. Sterile neutrino masses are predicted to be 0 . 7 (cid:46) m s (eV) (cid:46) 3 . 16 for normal hierarchy and 2 . 6 (cid:46) m s (eV) (cid:46) 7 . 1 for inverted hierarchy. For three neutrino scheme the model predicts 0 . 3401 ≤ sin 2 θ 12 ≤ 0 . 3415 , 0 . 460 ≤ sin 2 θ 23 ≤ 0 . 540 , − 0 . 60 ≤ sin δ CP ≤ − 0 . 20 for normal hierarchy and 0 . 3402 ≤ sin 2 θ 12 ≤ 0 . 3416 , 0 . 434 ≤ sin 2 θ 23 ≤ 0 . 610 , − 0 . 95 ≤ sin δ CP ≤ − 0 . 60 for inverted hierarchy.
Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain Adaptation

Accurate segmentation of brain tumors from multi-modal Magnetic Resonance (MR) images is essential in brain tumor diagnosis and treatment. However, due to the existence of domain shifts among different modalities, the performance of networks decreases dramatically when training on one modality and performing on another, e.g., train on T1 image while performing on T2 image, which is often required in clinical applications. This also prohibits a network from being trained on labeled data and then transferred to unlabeled data from a different domain. To overcome this, unsupervised domain adaptation (UDA) methods provide effective solutions to alleviate the domain shift between labeled source data and unlabeled target data. In this paper, we propose a novel Bidirectional Global-to-Local (BiGL) adaptation framework under a UDA scheme. Specifically, a bidirectional image synthesis and segmentation module is proposed to segment the brain tumor using the intermediate data distributions generated for the two domains, which includes an image-to-image translator and a shared-weighted segmentation network. Further, a global-to-local consistency learning module is proposed to build robust representation alignments in an integrated way. Extensive experiments on a multi-modal brain MR benchmark dataset demonstrate that the proposed method outperforms several state-of-the-art unsupervised domain adaptation methods by a large margin, while a comprehensive ablation study validates the effectiveness of each key component. The implementation code of our method will be released at \url{https://github.com/KeleiHe/BiGL}.
Towards a Predictive Processing Implementation of the Common Model of Cognition

In this article, we present a cognitive architecture that is built from powerful yet simple neural models. Specifically, we describe an implementation of the common model of cognition grounded in neural generative coding and holographic associative memory. The proposed system creates the groundwork for developing agents that learn continually from diverse tasks as well as model human performance at larger scales than what is possible with existant cognitive architectures.
Synergistic effect between Pt0 and Bi2O3−x for efficient room-temperature alcohol oxidation under base-free aqueous conditions

Novel Pt/Bi2O3−x catalysts synthesized by reduction of Pt/Bi2O3 in an H2 atmosphere at 300 °C were found to be efficient (conversion of 94.1 ± 2.7%, selectivity to aldehyde >99%) for benzyl alcohol oxidation to benzaldehyde at room temperature (26 °C) under base-free aqueous conditions. The catalytic oxidation is a green-chemistry compatible process with atmospheric O2 as the oxidant at ambient pressure. The high catalytic activity of Pt/Bi2O3−x is ascribed to the synergistic effect between Pt0 and nearby partially reduced Bi2O3−x species. Pt0 acts as a site to cleave the α-C–H bond of alcohols while Bi2O3−x could facilitate the activation of oxygen.
Fine-grained Interpretation and Causation Analysis in Deep NLP Models

Deep neural networks have constantly pushed the state-of-the-art performance in natural language processing and are considered as the de-facto modeling approach in solving complex NLP tasks such as machine translation, summarization and question-answering. Despite the proven efficacy of deep neural networks at-large, their opaqueness is a major cause of concern. In this tutorial, we will present research work on interpreting fine-grained components of a neural network model from two perspectives, i) fine-grained interpretation, and ii) causation analysis. The former is a class of methods to analyze neurons with respect to a desired language concept or a task. The latter studies the role of neurons and input features in explaining the decisions made by the model. We will also discuss how interpretation methods and causation analysis can connect towards better interpretability of model prediction. Finally, we will walk you through various toolkits that facilitate fine-grained interpretation and causation analysis of neural models.
Cryptanalysis of Semidirect Product Key Exchange Using Matrices Over Non-Commutative Rings

It was recently demonstrated that the Matrix Action Key Exchange (MAKE) algorithm, a new type of key exchange protocol using the semidirect product of matrix groups, is vulnerable to a linear algebraic attack if the matrices are over a commutative ring. In this note, we establish conditions under which protocols using matrices over a non-commutative ring are also vulnerable to this attack. We then demonstrate that group rings $R[G]$ are examples of non-commutative rings that satisfy these conditions.