Evolution of superhalogen properties in PtCln clusters

We have systematically calculated the ground state geometries, relative stability, electronic structure, and spectroscopic properties of PtCl n (n = 1–7) clusters. The bonding in these clusters is dominated by covalent interaction. In neutral clusters, chlorine atoms are chemically bound to Pt up to n = 5. However, in neutral PtCl6 and PtCl7 clusters, two of the chlorine atoms bind molecularly while the remaining bind as individual atoms. In the negative ions, this happens only in the case of PtCl7 cluster. The geometries of both neutral and anionic clusters can be considered as fragments of an octahedron and are attributed to the stabilization associated with splitting of partially filled d orbitals under the chloride ligand field. The electron affinity of PtCl n clusters rises steadily with n, reaching a maximum value of 5.81 eV in PtCl5. PtCl n clusters with n ≥ 3 are all superhalogens with electron affinities larger than that of chlorine. The accuracy of our results has been verified by carrying out photoelectron spectroscopy experiments on PtCl n − anion clusters

Electronic structure and properties of isoelectronic magic clusters: Al13X (X=H,Au,Li,Na,K,Rb,Cs)

The equilibrium structure, stability, and electronic properties of the Al13X (X=H,Au,Li,Na,K,Rb,Cs) clusters have been studied using a combination of photoelectron spectroscopy experiment and density functional theory. All these clusters constitute 40 electron systems with 39 electrons contributed by the 13 Al atoms and 1 electron contributed by each of the X (X=H,Au,Li,Na,K,Rb,Cs) atom. A systematic study allows us to investigate whether all electrons contributed by the X atoms are alike and whether the structure, stability, and properties of all the magic clusters are similar. Furthermore, quantitative agreement between the calculated and the measured electron affinities and vertical detachment energies enable us to identify the ground state geometries of these clusters both in neutral and anionic configurations

Aluminum Zintl anion moieties within sodium aluminum clusters

Through a synergetic combination of anion photoelectron spectroscopy and density functional theory based calculations, we have established that aluminum moieties within selected sodium-aluminum clusters are Zintl anions. Sodium–aluminum cluster anions, Na m Al n −, were generated in a pulsed arc discharge source. After mass selection, their photoelectron spectrawere measured by a magnetic bottle, electron energy analyzer. Calculations on a select sub-set of stoichiometries provided geometric structures and full charge analyses for both cluster anions and their neutral cluster counterparts, as well as photodetachment transition energies (stickspectra), and fragment molecular orbital based correlation diagrams

Absolute sets of rigid local systems

The absolute sets of local systems on a smooth complex algebraic variety are the subject of a conjecture of N. Budur and B. Wang based on an analogy with special subvarieties of Shimura varieties. An absolute set should be the higher-dimensional generalization of a local system of geometric origin. We show that the conjecture for absolute sets of simple cohomologically rigid local systems reduces to the zero-dimensional case, that is, to Simpson's conjecture that every such local system with quasi-unipotent monodromy at infinity and determinant is of geometric origin. In particular, the conjecture holds for this type of absolute sets if the variety is a curve or if the rank is two.Comment: 33 pages, v2: references adde

Tile-Weighted Rate-Distortion Optimized Packet Scheduling for 360∘^\circ VR Video Streaming

A key challenge of 360$^\circ$ VR video streaming is ensuring high quality with limited network bandwidth. Currently, most studies focus on tile-based adaptive bitrate streaming to reduce bandwidth consumption, where resources in network nodes are not fully utilized. This article proposes a tile-weighted rate-distortion (TWRD) packet scheduling optimization system to reduce data volume and improve video quality. A multimodal spatial-temporal attention transformer is proposed to predict viewpoint with probability that is used to dynamically weight tiles and corresponding packets. The packet scheduling problem of determining which packets should be dropped is formulated as an optimization problem solved by a dynamic programming solution. Experiment results demonstrate the proposed method outperforms the existing methods under various conditions.Comment: Accepted by IEEE Intelligent System

A Logical Pattern Memory Pre-trained Model for Entailment Tree Generation

Generating coherent and credible explanations remains a significant challenge in the field of AI. In recent years, researchers have delved into the utilization of entailment trees to depict explanations, which exhibit a reasoning process of how a hypothesis is deduced from the supporting facts. However, existing models often overlook the importance of generating intermediate conclusions with logical consistency from the given facts, leading to inaccurate conclusions and undermining the overall credibility of entailment trees. To address this limitation, we propose the logical pattern memory pre-trained model (LMPM). LMPM incorporates an external memory structure to learn and store the latent representations of logical patterns, which aids in generating logically consistent conclusions. Furthermore, to mitigate the influence of logically irrelevant domain knowledge in the Wikipedia-based data, we introduce an entity abstraction approach to construct the dataset for pre-training LMPM. The experimental results highlight the effectiveness of our approach in improving the quality of entailment tree generation. By leveraging logical entailment patterns, our model produces more coherent and reasonable conclusions that closely align with the underlying premises. Code and Data are released at https://github.com/YuanLi95/T5-LMPMComment: Accepted By Coling 202

MADRL-Based Rate Adaptation for 360{\deg} Video Streaming with Multi-Viewpoint Prediction

Over the last few years, 360{\deg} video traffic on the network has grown significantly. A key challenge of 360{\deg} video playback is ensuring a high quality of experience (QoE) with limited network bandwidth. Currently, most studies focus on tile-based adaptive bitrate (ABR) streaming based on single viewport prediction to reduce bandwidth consumption. However, the performance of models for single-viewpoint prediction is severely limited by the inherent uncertainty in head movement, which can not cope with the sudden movement of users very well. This paper first presents a multimodal spatial-temporal attention transformer to generate multiple viewpoint trajectories with their probabilities given a historical trajectory. The proposed method models viewpoint prediction as a classification problem and uses attention mechanisms to capture the spatial and temporal characteristics of input video frames and viewpoint trajectories for multi-viewpoint prediction. After that, a multi-agent deep reinforcement learning (MADRL)-based ABR algorithm utilizing multi-viewpoint prediction for 360{\deg} video streaming is proposed for maximizing different QoE objectives under various network conditions. We formulate the ABR problem as a decentralized partially observable Markov decision process (Dec-POMDP) problem and present a MAPPO algorithm based on centralized training and decentralized execution (CTDE) framework to solve the problem. The experimental results show that our proposed method improves the defined QoE metric by up to 85.5% compared to existing ABR methods.Comment: Accepted by IEEE Internet of Things Journa

Application of bioabsorbable screw fixation for anterior cervical decompression and bone grafting

OBJECTIVES: To examine the application of bioabsorbable screws for anterior cervical decompression and bone grafting fixation and to study their clinical effects in the treatment of cervical spondylosis. METHODS: From March 2007 to September 2012, 56 patients, 36 males and 20 females (38-79 years old, average 58.3±9.47 years), underwent a novel operation. Grafts were fixed by bioabsorbable screws (PLLA, 2.7 mm in diameter) after anterior decompression. The bioabsorbable screws were inserted from the midline of the graft bone to the bone surface of the upper and lower vertebrae at 45 degree angles. Patients were evaluated post-operatively to observe the improvement of symptoms and evaluate the fusion of the bone. The Japanese Orthopaedic Association (JOA) score was used to evaluate the recovery of neurological functions. RESULTS: All screws were successfully inserted, with no broken screws. The rate of symptom improvement was 87.5%. All of the grafts fused well with no extrusion. The average time for graft fusion was 3.8±0.55 months (range 3-5 months). Three-dimensional reconstruction of CT scans demonstrated that the grafts fused with adjacent vertebrae well and that the screws were absorbed as predicted. The MRI findings showed that the cerebrospinal fluid was unobstructed. No obvious complications appeared in any of the follow-up evaluations. CONCLUSIONS: Cervical spondylosis with one- or two-level involvement can be effectively treated by anterior decompression and bone grafting with bioabsorbable screw fixation. This operative method is safe and can avoid the complications induced by metal implants

Faster Diffusion Action Segmentation

Temporal Action Segmentation (TAS) is an essential task in video analysis, aiming to segment and classify continuous frames into distinct action segments. However, the ambiguous boundaries between actions pose a significant challenge for high-precision segmentation. Recent advances in diffusion models have demonstrated substantial success in TAS tasks due to their stable training process and high-quality generation capabilities. However, the heavy sampling steps required by diffusion models pose a substantial computational burden, limiting their practicality in real-time applications. Additionally, most related works utilize Transformer-based encoder architectures. Although these architectures excel at capturing long-range dependencies, they incur high computational costs and face feature-smoothing issues when processing long video sequences. To address these challenges, we propose EffiDiffAct, an efficient and high-performance TAS algorithm. Specifically, we develop a lightweight temporal feature encoder that reduces computational overhead and mitigates the rank collapse phenomenon associated with traditional self-attention mechanisms. Furthermore, we introduce an adaptive skip strategy that allows for dynamic adjustment of timestep lengths based on computed similarity metrics during inference, thereby further enhancing computational efficiency. Comprehensive experiments on the 50Salads, Breakfast, and GTEA datasets demonstrated the effectiveness of the proposed algorithm.Comment: 25 pages, 6 figure

Unsupervised Multi-document Summarization with Holistic Inference

Multi-document summarization aims to obtain core information from a collection of documents written on the same topic. This paper proposes a new holistic framework for unsupervised multi-document extractive summarization. Our method incorporates the holistic beam search inference method associated with the holistic measurements, named Subset Representative Index (SRI). SRI balances the importance and diversity of a subset of sentences from the source documents and can be calculated in unsupervised and adaptive manners. To demonstrate the effectiveness of our method, we conduct extensive experiments on both small and large-scale multi-document summarization datasets under both unsupervised and adaptive settings. The proposed method outperforms strong baselines by a significant margin, as indicated by the resulting ROUGE scores and diversity measures. Our findings also suggest that diversity is essential for improving multi-document summary performance.Comment: Findings of IJCNLP-AACL 202