Symmetric Framework Research Articles

Asymmetric Structural Engineering of Hot-Exciton Emitters Achieving a Breakthrough in Non-Doped BT.2020 Blue OLEDs with a Record 9.5% External Quantum Efficiency.

High-efficiency non-doped deep-blue organic light-emitting diodes (OLEDs) meeting the standard of BT.2020 color gamut is desired but rarely reported. Herein, an asymmetric structural engineering based on crossed long-short axis (CLSA) strategy is developed to obtain three new deep-blue emitters (BICZ, PHDPYCZ, and PHPYCZ) with a hot-exciton characteristic. Compared to 2BuCz-CNCz featuring a symmetric single hole-transport framework, these asymmetric emitters with the introduction of different electron-transport units show the enhancement of photoluminescence efficiency and improvement of bipolar charge transport capacity. Further combined with high radiative exciton utilization efficiency and light outcoupling efficiency, the non-doped OLED based on PHPYCZ exhibits the best performance with an excellent current efficiency of 3.49%, a record-high maximum external quantum efficiency of 9.5%, and a CIE y coordinate of 0.049 approaching the BT.2020 blue point. The breakthrough obtained in this work can inspire the molecular design of deep-blue emitters for high-performance non-doped BT.2020 blue OLEDs.

Multistage feature fusion knowledge distillation

Generally, the recognition performance of lightweight models is often lower than that of large models. Knowledge distillation, by teaching a student model using a teacher model, can further enhance the recognition accuracy of lightweight models. In this paper, we approach knowledge distillation from the perspective of intermediate feature-level knowledge distillation. We combine a cross-stage feature fusion symmetric framework, an attention mechanism to enhance the fused features, and a contrastive loss function for teacher and student models at the same stage to comprehensively implement a multistage feature fusion knowledge distillation method. This approach addresses the problem of significant differences in the intermediate feature distributions between teacher and student models, making it difficult to effectively learn implicit knowledge and thus improving the recognition accuracy of the student model. Compared to existing knowledge distillation methods, our method performs at a superior level. On the CIFAR100 dataset, it boosts the recognition accuracy of ResNet20 from 69.06% to 71.34%, and on the TinyImagenet dataset, it increases the recognition accuracy of ResNet18 from 66.54% to 68.03%, demonstrating the effectiveness and generalizability of our approach. Furthermore, there is room for further optimization of the overall distillation structure and feature extraction methods in this approach, which requires further research and exploration.

Nexus between total natural resource rents and public debt within symmetric and asymmetric framework: Fresh insight from resource-rich economy

Many economies abundant in natural resources often leverage the rents from these resources as collateral for borrowing during periods of economic boom, intending to repay debts during periods of windfall. However, this practice can cultivate irresponsible borrowing tendencies, potentially leading to debt trap scenarios when global resource prices decline. Thus, our study delves into the interaction between public debt (PD) and total natural resource rents (TNR), examining both aggregate and disaggregate forms, with a specific focus on India spanning from 1980-81 to 2021–22.Our aggregate analysis reveals that PD exhibits heightened responsiveness to negative shocks in TNR compared to positive shocks, indicating an asymmetric impact of TNR on PD within the Indian context. Additionally, disaggregate analysis uncovers that both positive and negative shocks in coal rents (CR), natural gas rents (NGR), and forest rents (FR) significantly and adversely affect PD. Furthermore, a positive shock in mineral rents (MR) demonstrates a negative and significant impact on PD, while a negative shock in MR yields a positive but insignificant impact on PD. Overall, barring oil rents (OR), other forms of resource rents such as CR, NGR, FR, and MR showcase asymmetric and negative effects on PD. These findings underscore the importance of prudent institutional behavior and sound economic policies in overseeing and managing debt sustainability in resource-rich economies.

Pathological Character of Modifications to Coincident General Relativity: Cosmological Strong Coupling and Ghosts in f(Q) Theories.

The intrinsic presence of ghosts in the symmetric teleparallel framework is elucidated. We illustrate our general arguments in f(Q) theories by studying perturbations in the three inequivalent spatially flat cosmologies. Two of these branches exhibit reduced linear spectra, signalling they are infinitely strongly coupled. For the remaining branch we unveil the presence of seven gravitational degrees of freedom and show that at least one of them is a ghost. Our results rule out f(Q) cosmologies and clarify the number of propagating degrees of freedom in these theories.

Strategic design of a rare trigonal symmetric luminescent covalent organic framework by linker modification

We designed a trigonal symmetric imine-linked covalent organic framework (COF), TFPC-DAB, with control over the angularity of the building units, where a bent C2-symmetric diamine, such as 1,3-diaminobenzene (1,3-DAB or DAB), with an exo-angle of 120° was used instead of those with an exo-angle of 180°, in combination with a C3-symmetric trialdehyde, such as tri(4-formylphenoxy)cyanurate (TFPC). Its synthesis was accomplished by reacting the building units in a mixture of mesitylene/dioxane/6 M acetic acid under solvothermal conditions. The phase purity, thermal stability, and porosity of TFPC-DAB were established by various analytical techniques. Utilizing the Density Functional Tight Binding (DFTB+) simulation and Pawley refinement, the best fit of the small angle x-ray pattern was found to have an AA stacking of TFPC-DAB in the trigonal space group P3 with low refinement parameters. Such smart materials are in huge demand to detect hazardous corrosive chemicals, such as HCl and NH3. The dual features of electron deficient π-acidic triazine moiety and heteroatoms (N/O) from TFPC and electron rich phenyl units from DAB embodied in the framework enhance its luminescent property and thereby make it suitable for solvent-based HCl and NH3 sensing. The detection limits for HCl and NH3 in methanol were found to be 14 and 82 ppb, respectively. The effect of solvent polarity on the sensing studies was observed with much lower detection limits in dioxane: 2.5 and 11 ppb for HCl and NH3, respectively. A detailed theoretical calculation using density functional theory and configurational bias Monte Carlo modules was conducted for understanding interactions between the COF and HCl or NH3 analytes.

SE-NDEND: A novel symmetric watermarking framework with neural network-based chaotic encryption for Internet of Medical Things

The development of the Internet of Medical Things heavily relies on big data, and data security based on medical images has become a growing concern in society. Digital watermarking serves as a crucial technique for protecting and tracing medical image data copyright, as well as enabling forensic analysis. However, existing deep watermarking methods often neglect the protection of watermarks after extraction, leading to potential copyright disputes. To address this issue, this paper proposes SE-NDEND, a novel symmetric watermarking framework with neural network-based chaotic encryption for the Internet of Medical Things that significantly enhances the effectiveness and security of watermarking while maintaining robustness. Specifically, the SE-NDEND leverages neural networks to simulate chaotic systems and generate chaotic sequences, mitigating the complexity and high cost of implementing chaotic systems using hardware circuits. Moreover, we introduce a new noise layer with Moiré distortion that interacts with the decoder, forming a symmetric network structure that bolsters the robustness of watermarking. Parameters are jointly trained and shared during the training process to counteract potential interference from the noise layer. Experimental results validate the effectiveness of SE-NDEND in enhancing copyright protection, traceability, and forensic capabilities, surpassing existing deep learning methods in terms of visual quality (with PSNR of 45.8492 dB and SSIM of 0.9874), security, and robustness. The proposed framework can find application in protecting medical image data in the Internet of Medical Things.

An investigation of financial openness, trade openness, gross capital formation, urbanization, financial development, education and energy nexus in BRI: Evidence from the symmetric and asymmetric framework

Clean energy development can bring numerous benefits, such as decreased greenhouse gas emissions, improved air quality, and increased job opportunities in the green industry. These advantages can be achieved through the collaborative efforts of all stakeholders involved. Ultimately, adopting clean energy can lead to a healthier planet and economy. Energy availability and scarcity influence the aggregated economy. The present study explores the interrelationships between financial openness, trade openness, gross capital formation, urbanization, financial development, education, and energy within the Belt and Road Initiative (BRI) nations. A panel of 56 nations has considered empirical investigation for 2002–2020. The coefficients extracted from CS-ARDL revealed a catalyst role of openness in the energy mix, especially the inclusion of clean energy both in the long run and short. The asymmetric evaluation revealed that positive negative shocks in openness lead to a positive association with energy consumption. Moreover, the asymmetric association was also exposed through the execution of a standard Wald test. The study findings show that FO, TO, and GCF are critical in energy sustainability in BRI nations. It implies that clean energy inclusion in the energy mix might be amplified, and energy sustainability may be ensured. The energy transition of Belt and Road Initiative (BRI) nations is significantly affected by financial, trade, and domestic capital adequacy. The success of sustainable energy policies is determined by several factors, which play a crucial role in countries participating in BRI projects; the findings provide insight into the complex interdependencies among the variables above and their effects on the energy dynamics within the BRI region. Furthermore, the research findings hold considerable significance for policymakers as they offer valuable insights into the possible synergies and trade-offs among these factors that can facilitate sustainable energy transitions in the BRI economies.

Letter from the Chair

Greetings SIGIR Members! SIGIR in Taipei was a success. A big thank you to the general chairs Hsin-Hsi Chen, Wei-Jou (Edward) Duh , and Hen-Hsen Huang , for putting on a great conference, in a beautiful venue. Thank you also to the Program Chairs Makoto Kato, Josiane Mothe and Barbara Poblete for the excellent technical programming! We would like to congratulate the Best Paper winners Maik Fröbe, Jan Heinrich Reimer, Sean MacAvaney, Niklas Deckers, Simon Reich, Janek Bevendorff, Benno Stein, Matthias Hagen , and Martin Potthast for their paper The Information Retrieval Experiment Platform. We would also like to congratulate Alireza Salemi, Juan Altmayer Pizzorno , and Hamed Zamani whose paper A Symmetric Dual Encoding Dense Retrieval Framework for Knowledge-Intensive Visual Question Answering won the Best Student Paper Award at SIGIR this year. Preparations for SIGIR 2024 in Washington DC are well underway, as preparations for SIGIR 2025 in Padua are beginning.

Syntheses, Structures and Reactivity of Metal Complexes of Trindane, Trindene, Truxene, Decacyclene and Related Ring Systems: Manifestations of Three-Fold Symmetry

The triple condensation of cyclopentanone or indanone to trindane (C15H18) or truxene (C27H18), respectively, provides convenient access to molecular skeletons on which major fragments of the prototypical fullerene C60 can be assembled. In particular, early approaches (both organic and organometallic) towards sumanene, as well as the final successful synthesis, are described. Organometallic derivatives of trindane have been prepared in which Cr(CO)3, Mo(CO)3, [Mn(CO)3]+ or [(C5H5)Fe(CO)2]+ are η6-bonded to the central arene ring. The debromination of hexabromotrindane yields trindene, which forms a tri-anion to which as many as three organometallic fragments, such as Mn(CO)3, W(CO)3Me, or Rh(CO)2, may be attached. Truxene forms complexes whereby three metal fragments can bind either to the peripheral arene rings, or to the five-membered rings, and these can be interconverted via η6 ↔ η5 haptotropic shifts. Truxene also forms a double-decker sandwich with Ag(I) bridges, and decacyclene, C36H18, forms triple-decker sandwiches bearing multiple cyclopentadienyl-nickel or -iron moieties. The organic chemistry of trindane has been investigated, especially with respect to its unexpectedly complex oxidation products, which were only identified unambiguously via X-ray crystallography. The three-fold symmetric trindane framework has also been used as a template upon which a potential artificial receptor has been constructed. Finally, the use of truxene and truxenone derivatives in a wide range of applications is highlighted.

A Secret Key Classification Framework of Symmetric Encryption Algorithm Based on Deep Transfer Learning

The leakage signals, including electromagnetic, energy, time, and temperature, generated during the operation of password devices contain highly correlated key information, which leads to security vulnerabilities. In traditional encryption algorithms, the length of the key greatly affects the upper limit of its security against cracking. Regarding side-channel attacks on long-key algorithms, traditional template attack methods characterize the energy traces using multivariate Gaussian distribution during the template construction phase. The exhaustive key-guessing process is expected to consume a significant amount of time and computational resources. Therefore, to analyze the effectiveness of obtaining key values from the side information of password devices, we propose an innovative attack method based on a divide-and-conquer logical structure, targeting semi-bytes. We construct a collection of key classification submodules with symmetric correlations. By integrating a differential network model for byte-block sets and an end-to-end direct attack method, we form a holistic symmetric decision framework and propose a key classification structure based on deep transfer learning. This structure consists of three main parts: side information data acquisition, analysis of key-value effectiveness, and determination of attack positions. It employs multiple parallel symmetric subnetworks, effectively improving attack efficiency and reducing the key enumeration range. Experimental results show that the optimal attack accuracy of the network model can reach 91%, with an average attack accuracy of 78%. It overcomes overfitting issues under small sample dataset conditions.

Graphic statics and symmetry

Reciprocal diagrams are a geometric construction dating back to Maxwell and Cremona in which a self-stressed plane framework with a planar graph is paired with another self-stressed reciprocal framework on the dual graph. Either one of the reciprocal frameworks is the form diagram of a self-stressable structure and the other is the force diagram of the corresponding axial forces. This geometric technique offers insights into the self-stresses and infinitesimal motions (mechanisms) of both frameworks in the reciprocal pair. For a symmetric framework with a fully-symmetric self-stress, we obtain an equi-symmetric reciprocal pair of plane frameworks, as well as the associated symmetric discrete dual Airy stress function polyhedra. In this paper we exploit symmetry to refine the Maxwell–Cremona correspondence by considering the decomposition of the self-stress and motion spaces into invariant subspaces corresponding to the irreducible representations of the symmetry group. As such, the familiar s=m∗+1 relationship for the number of self-stresses of a framework, s, and the number of mechanisms of the reciprocal, m∗, is reworked into a symmetry adapted version which provides greater insights into the properties of the reciprocal framework pair. We also show how the quotient graph of a symmetric framework and its reciprocal can be used to efficiently detect infinitesimal motions, self-stresses and polyhedral liftings of different symmetry types. This allows for symmetry-adapted simplified structural analyses of symmetric structures.

Adaptive Symmetry Reference Trajectory Generation in Shared Autonomy for Active Knee Orthosis

Gait symmetry training plays an essential role in the rehabilitation of hemiplegic patients. Robotics-based gait training has been widely accepted by patients and clinicians. Reference trajectory generation for the affected side using the motion data of the unaffected side is an important way to achieve this. However, online generation of gait reference trajectory requires the algorithm to provide correct gait phase delay and could reduce the impact of measurement noise from sensors and input uncertainty from users. Based on an active knee orthosis (AKO) prototype, this work presents an adaptive symmetric gait trajectory generation framework for the gait rehabilitation of hemiplegic patients. Using the adaptive nonlinear frequency oscillators (ANFO) and movement primitives, we implement online gait pattern encoding and adaptive phase delay according to the real-time user input. A shared autonomy (SA) module with online input validation and arbitration has been designed to prevent undesired movements from being transmitted to the actuator on the affected side. The experimental results demonstrate the feasibility of the framework. Overall, this work suggests that the proposed method has the potential to perform gait symmetry rehabilitation in an unstructured environment and provide a kinematic reference for torque-assistance AKO.

Symmetry-extended mobility counting for plate-bar systems, with applications to rod-bar and rod-clamp frameworks

We obtain symmetry-extended counting rules for the mobility of general plate-bar frameworks in configurations with non-trivial point-group symmetry. Necessary conditions for isostaticity of a symmetric rod-bar framework in 3-space are derived. An example shows that establishing sufficient conditions will require significant further development. A symmetry-extended counting rule is established for rod-clamp frameworks: plate-bar frameworks are clamped in such a way as to remove relative translations within clamped pairs. Worked examples showing the utility of the symmetry approach in detecting mechanisms and states of self-stress include an application to linear pentapods where a singular configuration is detected by symmetry.

A search optimized blockchain‐based verifiable searchable symmetric encryption framework

SummaryOutsourcing storage and computation to cloud servers have become a trend. Although searchable symmetric encryption (SSE) had handled the data privacy issue caused by honest‐but‐curious servers, a semi‐honest server may return incomplete or incorrect results when users search for their encrypted data. To against such servers, scholars have recently used blockchain/Ethereum‐based SSE schemes which utilize the public, that is, active nodes, to verify the search process. However, the search operation in existing schemes is very expansive in terms of fee and time. In this paper, we propose a new blockchain‐based searchable encryption framework with search optimized, that is, free of charge, quicker, and more private, at the cost of some extra storage. Besides, we design a general and efficient verification algorithm for our framework, which makes the search verifiable. In addition, we deploy an instance of our framework on an official Ethereum test network, and the experimental results and evaluations demonstrate the advantage of our framework.

Common origin of θ13 and dark matter within the flavor symmetric scoto-seesaw framework

To understand the observed pattern of neutrino masses and mixing as well as to account for the dark matter we propose a hybrid scoto-seesaw model based on the A4 discrete flavor symmetry. In this setup, including at least two heavy right-handed neutrinos is essential to employ the discrete flavor symmetry that mimics once popular tribimaximal neutrino mixing at the leading order via type-I seesaw. The scotogenic contribution then acts as a critical deviation to reproduce the observed value of the reactor mixing angle θ13 (within the trimaximal mixing scheme) and to accommodate potential dark matter candidates, pointing towards a common origin of θ13 and dark matter. The model predicts the atmospheric angle to be in the upper octant, excludes some regions on the Dirac CP phase, and restricts the Majorana phases too. Further, normal and inverted mass hierarchies can be distinguished for specific values of the relative phases associated with the complex light neutrino mass matrix. Owing to the considered flavor symmetry, contributions coming from the scotogenic mechanism towards the lepton flavor violating decays such as μ → eγ, τ → eγ vanish, and a lower limit on the second right-handed neutrino mass can be obtained. Prediction for the effective mass parameter appearing in the neutrinoless double beta decay falls within the sensitivity of future experiments such as LEGEND-1k and nEXO.

Confronting dark fermion with a doubly charged Higgs in the left–right symmetric model

We consider a fermionic dark matter (DM) in the left–right symmetric framework by introducing a pair of vector-like (VL) doublets in the particle spectrum. The stability of the DM is ensured through an unbroken mathcal {Z}_2 symmetry. We explore the parameter space of the model compatible with the observed relic density and direct and indirect detection cross sections. The presence of charged dark fermions opens up an interesting possibility for the doubly charged Higgs signal at LHC and ILC. The signal for the doubly charged scalar decaying into the dark sector is analyzed in multilepton final states for a few representative parameter choices consistent with DM observations.

Are green resource productivity and environmental technologies the face of environmental sustainability in the Nordic region?

AbstractThis study examines whether the raw material productivity, export intensification, and environmental‐related technologies in the Nordic region (i.e., Denmark, Finland, Iceland, Norway, and Sweden) drives the region's carbon neutrality target. By adopting both symmetric and asymmetric empirical approaches over the period 1990–2019, the study found that positive and negative shifts in environmental‐related technologies mitigates greenhouse gas (GHG) emissions in the region with the former causing a larger impact. Furthermore, the findings reveal that a positive shift in raw material productivity mitigates GHG emissions while a negative shift in raw material productivity causes a surge in GHG emissions especially in the long‐run. Moreover, a positive (negative) shift in export intensity yields a decline (upsurge) in GHG emissions in the long‐run. In the symmetric framework, in both long‐ and short‐run, the result reveals that economic growth upsurges GHG emissions while raw material productivity for green growth and environmental‐related technologies mitigates GHG emissions. This demonstrates the efficient raw material productivity profile of the Nordic countries. Alongside the Granger causality inference, the result further informs that energy intensity is crucial to curbing GHG emissions in the region. Thus, the result from the study offers relevant policy instructions.

A dynamic threshold method for wind turbine fault detection based on spatial-temporal neural network

Strong coupling of wind turbines (WTs) makes the supervisory control and data acquisition (SCADA) data spatially relevant and time-dependent. Aiming at identifying the fault state of wind turbines accurately by comprehensively using the change information in the spatial and temporal scale of SCADA data, a novel fault detection method of WTs based on spatial-temporal features' fusion of SCADA data by a deep autoencoder (DAE) and a gated recurrent unit (GRU) is proposed in this paper. The spatial feature extraction capability of the encoder layer and the temporal feature extraction capability of GRU are combined with the symmetric framework of DAE to achieve the spatial-temporal feature extraction of the whole model. Mahalanobis distance (MD) is adopted to convert the multidimensional output into a one-dimensional performance index. To avoid the influence on the fault detection results due to the changeable operational state of WTs, a dynamic threshold regression method based on the gray wolf optimization algorithm and support vector regression model is developed to identify fault data instances. Experiments with SCADA data from real wind farms verify the effectiveness of the proposed method.

Polygonal boundary gaps in multiple diffusion source precipitation systems in gel media.

We investigate multiple reaction-diffusion processes that engender the formation of distinct precipitation zones. In this paper, we carry out various original precipitation reactions in a gel medium, wherein the interdiffusion of the co-precipitates occurs from various sources arranged in a symmetric framework in 2D Petri dishes. The distinct precipitation zones are separated by clear polygonal boundaries, in congruence with the spatial distribution of the diffusion holes hosting the outer electrolyte. We use scanning electron microscopy, energy dispersive x-ray diffraction spectrometry, and notably powder x-ray diffraction for the characterization of the differentiated precipitate patterning zones for each system studied. The obtained patterns find their application niche in the chemical analogs of Voronoi diagrams and the rift scenery in geological landscapes.

A transfer framework based on co-matrix decomposition for undirected, high-dimensional and sparse networks

Undirected, high-dimensional and sparse network is often encountered in industrial engineering and described by symmetric, high-dimensional and sparse (SHiDS) matrix for academic investigation. Latent factor (LF) models have shown an arguably promising yet whelming capability of extracting knowledge from so little known information in the SHiDS matrix. However, most of the LF models in the form of matrix factorization (a.k.a. matrix decomposition) focus exclusively on a single domain and struggle to meet the requirements of symmetry and non-negativity of the SHiDS matrix. To address these issues, a symmetric and non-negative collective factorization (SNCF) transfer framework in the form of collective-matrix (co-matrix) decomposition is proposed. SNCF transfers a great deal of knowledge from the auxiliary domain to mitigate the sparsity of the target domain. To solve the SNCF efficiently and guarantee the symmetry and non-negativity of the target matrix, a collective batch-based non-negative optimization approach (SNCF-CBN) is put forward. Experimental results demonstrate the effectiveness of SNCF-CBN by comparing it with several state-of-the-art models on public datasets, including Protein Networks, Sonar Mines and Sonar Rocks datasets with different sparsities in cross-domain.