Miracle-dz/UniFAR-FTUs huggingface dataset:
The Communication of Social Stereotypes: The Effects of Group Discussion and Information Distribution on Stereotypic Appraisals

Stereotypes are fundamentally social constructs, formulated and modified through discussion and interaction with others. The present studies examined the impact of group discussion on stereotypes. In both studies, groups of participants discussed their impressions about a hypothetical target group after having read behaviors performed by target group members. These behaviors included both stereotypic and counterstereotypic examples, and the distribution of these behaviors varied across discussion group members. In some groups only 1 member knew of the counterstereotypic behaviors; in other groups this information was distributed across all group members. In general, discussion led to a polarization of the target group stereotypes, but this effect was lessened when the counterstereotypic behaviors were concentrated in 1 group member. In this case, these counterstereotypic behaviors were discussed more and retained better. It is now a well-establish ed finding that informal discussions among like-minded individuals lead to a polarization of group members' attitudes. This phenomenon has been termed group polarization (Brauer & Judd, 1996; Burnstein & Vinokur, 1977; Isenberg, 1986; Lamm & Myers, 1978; Myers & Lamm, 1976; Sanders & Baron, 1977). The effect has been shown with attitude objects as varied as women's emancipation (Paicheler, 1976), public sex education (Madsen, 1978), political issues (Brauer, Judd, & Gliner, 1995), political candidates (Moscovici & Zavalloni, 1969), hands of blackjack (Blascovich, Ginsburg, & Veach, 1975), evaluations of imaginary courses of action (Myers & Bishop, 1971), choice dilemmas involving risk taking (Kogan & Wallach, 1967), prosocial behavior (Schroeder, 1973), and the importance of life goals (Billig & Cochrane, 1976). Most of these studies have been run in laboratory settings and have involved student participants. However, numerous studies have also demonstrated group polarization in natural settings involving political decision makers (Kirkpatrick, Davis, & Robertson, 1976), army officers (Minix, 1976, cited in Myers, 1982), employees of mental
Subsite Specificity of Memapsin 2 (β-Secretase): Implications for Inhibitor Design†

Memapsin 2 is the protease known as β-secretase whose action on β-amyloid precursor protein leads to the production of the β-amyloid (Aβ) peptide. Since the accumulation of Aβ in the brain is a key event in the pathogenesis of Alzheimer's disease, memapsin 2 is an important target for the design of inhibitory drugs. Here we describe the residue preference for the subsites of memapsin 2. The relative kcat/KM values of residues in each of the eight subsites were determined by the relative initial cleavage rates of substrate mixtures as quantified by MALDI-TOF mass spectrometry. We found that each subsite can accommodate multiple residues. The S1 subsite is the most stringent, preferring residues in the order of Leu > Phe > Met > Tyr. The preferences of other subsites are the following: S2, Asp > Asn > Met; S3, Ile > Val > Leu; S4, Glu > Gln > Asp; S1‘, Met > Glu > Gln > Ala; S2‘, Val > Ile > Ala; S3‘, Leu > Trp > Ala; S4‘, Asp > Glu > Trp. In general, S subsites are more specific than the S‘ subsites. A pe.
Making Neural Networks Interpretable with Attribution: Application to Implicit Signals Prediction

Explaining recommendations enables users to understand whether recommended items are relevant to their needs and has been shown to increase their trust in the system. More generally, if designing explainable machine learning models is key to check the sanity and robustness of a decision process and improve their efficiency, it however remains a challenge for complex architectures, especially deep neural networks that are often deemed ”black-box”. In this paper, we propose a novel formulation of interpretable deep neural networks for the attribution task. Differently to popular post-hoc methods, our approach is interpretable by design. Using masked weights, hidden features can be deeply attributed, split into several input-restricted sub-networks and trained as a boosted mixture of experts. Experimental results on synthetic data and real-world recommendation tasks demonstrate that our method enables to build models achieving close predictive performances to their non-interpretable counterparts, while providing informative attribution interpretations.
Mass loss and element release of litter in the subalpine forest over one freeze-thaw season

As one of the most important forest ecosystem processes,litter decomposition is controlled by integrated biotic and abiotic factors such as climate change,species composition,litter quality,soil temperature and moisture,as a consequence of which more attentions have been given to the decomposition process of litters in different forest ecosystems and their relations with biotic and abiotic factors over the past decades.As yet,numerous experiments on litter decomposition were carried out at 3-25 ℃,far less information is available on mass loss and element releases in litters of the subalpine forest during a freezing-thawing season.A simple experiment on leaf litter decomposition was therefore carried out in fir(Abies faxoniana Rehder E.H.Wilson) and birch(Betula platyphylla Sukaczev) forests in order to character the litter decomposition process during a freezing-thawing season and their ecological significance.Litter bags were put on the forest floor and in 10 cm soil layer beneath fir and birch forests before soil freezing in October 2006,and sampled after thawing in April and October 2007.Rates of litter decomposition and releases of carbon and nutrients were measured in different soil depths and forest stands during a freezing and following season.Over one freeze-thaw season,(19.4±2.0)% and(21.5±3.5)% leaf litters were decomposed in fir and birch forests,and accounted for 64.5% and 65.6% of the annual mass loss of the corresponding litters,respectively,implying that litter decomposition was influenced significantly by seasonal freeze-thaw cycle in the subalpine forest.During the freeze-thaw period,(15.0±1.0) % of C,(34.1±3.6) % of N,(17.0±0.9) % of P,(22.8±5.9) % of K,(20.1±0.1) % of Ca and(36.3±2.1) % of Mg were released from fir needle litter,and(20.7±0.1)% of C,(29.4±3.4) % of N,(15.7±1.3) % of P,(16.8±5.1) % of K,(21.3±1.8) % of Ca and(20.5±2.8) % of Mg released from birch leaf litter,respectively.Consequently,calculated by the leaf litter production in the corresponding forest,fresh leaf litter released(10.17±1.14) kg hm-2 and(5.61±1.12) kg hm-2 of N,(0.68±0.08) kg hm-2 and(0.34±0.07) kg hm-2 of P,(4.08±0.46) kg hm-2 and(1.21±0.24) kg hm-2 of K,(0.46±0.05) kg hm-2 and(0.300±0.059) kg hm-2 of Ca,and(0.09±0.01) kg hm-2 and(0.051±0.010) kg hm-2 of Mg into soil in fir and birch forests over one freeze-thaw season,respectively.Nutrients released from litters during soil freeze-thaw period are favorable for plant nutrition and growth in the next growing season,which are of ecological importance for maintaining the subalpine forest ecosystem process.
Nonvolatile ferroelectric control of topological states in 2D heterostructures

Quantum spin Hall (QSH) insulator materials feature topologically protected edge states that can drastically reduce dissipation and are useful for the next-generation electronics. However, the nonvolatile control of topological edge state is still a challenge. In this paper, based on first-principles calculations, the switchable topological states are found in the van der Waals (vdW) heterostructures consisting of two dimensional (2D) Bi(111) bilayer (BL) and {\alpha}-In2Se3 by reversing the electric polarization of the ferroelectric {\alpha}-In2Se3. The topological switching results from the different charge transfer associated with the two opposite polarization states of {\alpha}-In2Se3. This new topological switching mechanism has the unique advantages of being fully electrical as well as nonvolatile. Our finding provides an unprecedented approach to realize ferroelectric control of topological states in 2D materials, which will have great potential for applications in topological nanoscale electronics.
Cottonseed meal is a suitable replacement for soybean meal in supplements fed to Nellore heifers grazing Brachiaria decumbens

The objective was to evaluate the effect of substituting soybean meal with cottonseed meal in supplements containing 15% and 30% of crude protein (CP), as well as evaluate the effect of supplementation use, on the performance and nutritional status of Nellore heifers. Forty heifers that were 13 months old and 250 ± 4 kg were kept in five 2.5-ha paddocks covered with Brachiaria decumbens. They were compared as they received different supplements (1 kg/head.day) in a 2 × 2 + 1 factorial – two protein sources (soybean and cottonseed meal) by two protein concentrations (15% and 30%) plus a Control treatment, which was a mineral mix only. The supplemented heifers showed greater weight gains, final bodyweight (P < 0.10) and higher intake of all evaluated nutrients (P < 0.10), as well as greater digestibility of these nutrients compared with those from the Control treatment. The animals fed supplements containing 15% CP had lower digestibility of dry matter, organic matter, CP and neutral detergent insoluble fibre corrected for ash and protein. These animals also had lower total digestible nutrients as compared with those which received a supplement containing 30% CP (P < 0.10). The treatments containing cottonseed meal provided greater dry matter digestibility (P < 0.10). Cottonseed meal can fully substitute soybean meal to grazing heifers during the rainy season. Supplements containing 30% of CP provided greater digestibility than 15% CP supplements. Supplemented animals had better performance.
Fusion of Global-Local Features for Image Quality Inspection of Shipping Label

The demands of automated shipping address recognition and verification have increased to handle a large number of packages and to save costs associated with misdelivery. A previous study proposed a deep learning system where the shipping address is recognized and verified based on a camera image capturing the shipping address and barcode area. Because the system performance depends on the input image quality, inspection of input image quality is necessary for image preprocessing. In this paper, we propose an input image quality verification method combining global and local features. Object detection and scale-invariant feature transform in different feature spaces are developed to extract global and local features from several independent convolutional neural networks. The conditions of shipping label images are classified by fully connected fusion layers with concatenated global and local features. The experimental results regarding real captured and generated images show that the proposed method achieves better performance than other methods. These results are expected to improve the shipping address recognition and verification system by applying different image preprocessing steps based on the classified conditions.
SELF-REFERENTIAL THEORIES

Abstract We study the structure of families of theories in the language of arithmetic extended to allow these families to refer to one another and to themselves. If a theory contains schemata expressing its own truth and expressing a specific Turing index for itself, and contains some other mild axioms, then that theory is untrue. We exhibit some families of true self-referential theories that barely avoid this forbidden pattern.
Field-induced magnetic phase transitions and the resultant giant anomalous Hall effect in the antiferromagnetic half-Heusler compound DyPtBi

We report field-induced magnetic phase transitions and transport properties of antiferromagnetic DyPtBi. We show that DyPtBi hosts a delicate balance between two different magnetic ground states, which can be controlled by a moderate magnetic field. Furthermore, it exhibits giant anomalous Hall effect (\sigma_A=1540 (ohm cm)^{-1},\theta_{AHE} = 24%) in a field-induced Type-I spin structure, presumably attributed to the enhanced Berry curvature associated with avoided band-crossings near the Fermi energy and / or non-zero spin chirality. The latter mechanism points DyPtBi towards a rare potential realization of anomalous Hall effect in an antiferromagnet with face-center-cubic lattice that was proposed in [Physical Review Letters 87, 116801 (2001)].
Identifying Coordinated Accounts in Disinformation Campaigns

Disinformation campaigns on social media, involving coordinated activities from malicious accounts towards manipulating public opinion, have become increasingly prevalent. There has been growing evidence of social media abuse towards influencing politics and social issues in other countries, raising numerous concerns. The identification and prevention of coordinated campaigns has become critical to tackling disinformation at its source. Existing approaches to detect malicious campaigns make strict assumptions about coordinated behaviours, such as malicious accounts perform synchronized actions or share features assumed to be indicative of coordination. Others require part of the malicious accounts in the campaign to be revealed in order to detect the rest. Such assumptions significantly limit the effectiveness of existing approaches. In contrast, we propose AMDN (Attentive Mixture Density Network) to automatically uncover coordinated group behaviours from account activities and interactions between accounts, based on temporal point processes. Furthermore, we leverage the learned model to understand and explain the behaviours of coordinated accounts in disinformation campaigns. We find that the average influence between coordinated accounts is the highest, whereas these accounts are not much influenced by regular accounts. We evaluate the effectiveness of the proposed method on Twitter data related to Russian interference in US Elections. Additionally, we identify disinformation campaigns in COVID-19 data collected from Twitter, and provide the first evidence and analysis of existence of coordinated disinformation campaigns in the ongoing pandemic.
Generating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document Summarization

We consider the problem of automatically generating a narrative biomedical evidence summary from multiple trial reports. We evaluate modern neural models for abstractive summarization of relevant article abstracts from systematic reviews previously conducted by members of the Cochrane collaboration, using the authors conclusions section of the review abstract as our target. We enlist medical professionals to evaluate generated summaries, and we find that summarization systems yield consistently fluent and relevant synopses, but these often contain factual inaccuracies. We propose new approaches that capitalize on domain-specific models to inform summarization, e.g., by explicitly demarcating snippets of inputs that convey key findings, and emphasizing the reports of large and high-quality trials. We find that these strategies modestly improve the factual accuracy of generated summaries. Finally, we propose a new method for automatically evaluating the factuality of generated narrative evidence syntheses using models that infer the directionality of reported findings.
Giant Flexoelectricity in Bent Silicon Thinfilms.

We reveal that strong flexoelectric effect of solids can be induced due to the signfi? cant charge migration along the strain gradient direction, which represents a new understanding of the origin of flexoelectricity. Beyond the linear response theory, we illustrate such charge migration that is driven by an electric field effect in bent silicon thinfilms. Due to such charge migration, the variation of atomic charge no longer represents a linear response to strain gradient and the resulting giant flexoelectric coeffcients being size dependent cannot be treated as a bulk property. The obtained flexoelectric coefficients compare well with the typical experimental values as reported in various ceramics. Our results shed light on elucidating the discrepancy between theory and experiment,and pave a new way to discover excellent flexoelectric performance in conventional materials.
Earliest Resolution to the Neutrino Mass Ordering

We hereby illustrate and numerically demonstrate via a simplified proof of concept calculation tuned to the latest average neutrino global data that the combined sensitivity of JUNO with NOvA and T2K experiments has the potential to be the first fully resolved ($\geq$5$\sigma$) measurement of neutrino Mass Ordering (MO) around 2028; tightly linked to the JUNO schedule. Our predictions account for the key ambiguities and the most relevant $\pm$1$\sigma$ data fluctuations. In the absence of any concrete MO theoretical prediction and given its intrinsic binary outcome, we highlight the benefits of having such a resolved measurement in the light of the remarkable MO resolution ability of the next generation of long baseline neutrino beams experiments. We motivate the opportunity of exploiting the MO experimental framework to scrutinise the standard oscillation model, thus, opening for unique discovery potential, should unexpected discrepancies manifest. Phenomenologically, the deepest insight relies on the articulation of MO resolved measurements via at least the two possible methodologies matter effects and purely vacuum oscillations. Thus, we argue that the JUNO vacuum MO measurement may feasibly yield full resolution in combination to the next generation of long baseline neutrino beams experiments.
A Bilateral Game Approach for Task Outsourcing in Multi-Access Edge Computing

Multi-access edge computing (MEC) is a promising architecture to provide low-latency applications for future Internet of Things (IoT)-based network systems. Together with the increasing scholarly attention on task offloading, the problem of servers’ resource allocation has been widely studied. The limited computational resources of edge servers (ESs) cannot meet the different demands of terminal entities (TEs). This makes it a challenge to efficiently schedule computational tasks on ESs. In this paper, we consider a MEC resource transaction market with multiple ESs and multiple TEs, which are interdependent and mutually influence each other. This paper aims to investigate the dynamic tasks allocation problem between TEs and ESs and to meet the optimal benefits for both parties in MEC system. However, this many-to-many interaction requires resolving several problems, including task allocation, TEs’ selection on ESs and conflicting interests of both parties. A bilateral game framework is applied to tackle the tasks allocation problem by modeling the problem as two noncooperative games: the supplier and customer side games. The existence and uniqueness of the Nash equilibrium in the aforementioned games are proved. A distributed task outsourcing algorithm (DTOA) is designed to determine the equilibrium. Our simulation results have demonstrated the superior performance of DTOA in increasing the ESs’ profit and TEs’ payoffs, as well as flattening the peak and off-peak loads.
Towards Structured Prediction in Bioinformatics with Deep Learning

Using machine learning, especially deep learning, to facilitate biological research is a fascinating research direction. However, in addition to the standard classification or regression problems, in bioinformatics, we often need to predict more complex structured targets, such as 2D images and 3D molecular structures. The above complex prediction tasks are referred to as structured prediction. Structured prediction is more complicated than the traditional classification but has much broader applications, considering that most of the original bioinformatics problems have complex output objects. Due to the properties of those structured prediction problems, such as having problem-specific constraints and dependency within the labeling space, the straightforward application of existing deep learning models can lead to unsatisfactory results. Here, we argue that the following ideas can help resolve structured prediction problems in bioinformatics. Firstly, we can combine deep learning with other classic algorithms, such as probabilistic graphical models, which model the problem structure explicitly. Secondly, we can design the problem-specific deep learning architectures or methods by considering the structured labeling space and problem constraints, either explicitly or implicitly. We demonstrate our ideas with six projects from four bioinformatics subfields, including sequencing analysis, structure prediction, function annotation, and network analysis. The structured outputs cover 1D signals, 2D images, 3D structures, hierarchical labeling, and heterogeneous networks. With the help of the above ideas, all of our methods can achieve SOTA performance on the corresponding problems. The success of these projects motivates us to extend our work towards other more challenging but important problems, such as health-care problems, which can directly benefit people's health and wellness.
Performance Optimization of Federated Person Re-identification via Benchmark Analysis

Federated learning is a privacy-preserving machine learning technique that learns a shared model across decentralized clients. It can alleviate privacy concerns of personal re-identification, an important computer vision task. In this work, we implement federated learning to person re-identification (FedReID) and optimize its performance affected by statistical heterogeneity in the real-world scenario. We first construct a new benchmark to investigate the performance of FedReID. This benchmark consists of (1) nine datasets with different volumes sourced from different domains to simulate the heterogeneous situation in reality, (2) two federated scenarios, and (3) an enhanced federated algorithm for FedReID. The benchmark analysis shows that the client-edge-cloud architecture, represented by the federated-by-dataset scenario, has better performance than client-server architecture in FedReID. It also reveals the bottlenecks of FedReID under the real-world scenario, including poor performance of large datasets caused by unbalanced weights in model aggregation and challenges in convergence. Then we propose two optimization methods: (1) To address the unbalanced weight problem, we propose a new method to dynamically change the weights according to the scale of model changes in clients in each training round; (2) To facilitate convergence, we adopt knowledge distillation to refine the server model with knowledge generated from client models on a public dataset. Experiment results demonstrate that our strategies can achieve much better convergence with superior performance on all datasets. We believe that our work will inspire the community to further explore the implementation of federated learning on more computer vision tasks in real-world scenarios.