Metal-organic frameworks (MOFs) have attracted great attention because of their intriguing molecular topologies and potential applications in chemical separation, [1] gas storage, [2] drug delivery, [3] catalysis [4] and chemical sensor technology. [5] Particularly, MOFs could also be potential energetic materials because of their high densities and high heats of detonation. For example, Hope-Weeks and co-workers recently reported two hydrazine-perchlorate 1D MOFs [(Ni(NH 2 NH 2 ) 5 (ClO 4 ) 2 ) n (NHP), and (Co(NH 2 NH 2 ) 5 (ClO 4 ) 2 ) n (CHP)] with linear polymeric structures, [6] which were regarded as possibly the most powerful metal-based energetic materials known to date, with heats of detonation comparable with that of hexanitrohexaazaisowutzitane (CL-20; about 1.5 kcal g À1 ).Unfortunately, these coordination polymers were highly sensitive to impact deriving from their low rigidity characteristic of such linear polymeric structures, which makes practical use infeasible. In order to decrease the sensitivities, the same authors also used a hydrazine derivative (hydrazine-carboxylate) as the ligand to construct MOFs with 2D sheet structures [((

Non-negative Matrix Factorization (NMF) is a part-based image representation method which adds a non-negativity constraint to matrix factorization. NMF is compatible with the intuitive notion of combining parts to form a whole face. In this paper, we propose a framework of face recognition by adding NMF constraint and classifier constraints to matrix factorization to get both intuitive features and good recognition results. Based on the framework, we present two novel subspace methods: Fisher Non-negative Matrix Factorization (FNMF) and PCA Non-negative Matrix Factorization (PNMF). FNMF adds both the non-negative constraint and the Fisher constraint to matrix factorization. The Fisher constraint maximizes the between-class scatter and minimizes the within-class scatter of face samples. Subsequently, FNMF improves the capability of face recognition. PNMF adds the non-negative constraint and characteristics of PCA, such as maximizing the variance of output coordinates, orthogonal bases, etc. to matrix factorization. Therefore, we can get intuitive features and desirable PCA characteristics. Our experiments show that FNMF and PNMF achieve better face recognition performance than NMF and Local NMF.

Metal-organic frameworks (MOFs) have attracted great attention because of their intriguing molecular topologies and potential applications in chemical separation, [1] gas storage, [2] drug delivery, [3] catalysis [4] and chemical sensor technology. [5] Particularly, MOFs could also be potential energetic materials because of their high densities and high heats of detonation. For example, Hope-Weeks and co-workers recently reported two hydrazine-perchlorate 1D MOFs [(Ni(NH 2 NH 2 ) 5 (ClO 4 ) 2 ) n (NHP), and (Co(NH 2 NH 2 ) 5 (ClO 4 ) 2 ) n (CHP)] with linear polymeric structures, [6] which were regarded as possibly the most powerful metal-based energetic materials known to date, with heats of detonation comparable with that of hexanitrohexaazaisowutzitane (CL-20; about 1.5 kcal g À1 ).Unfortunately, these coordination polymers were highly sensitive to impact deriving from their low rigidity characteristic of such linear polymeric structures, which makes practical use infeasible. In order to decrease the sensitivities, the same authors also used a hydrazine derivative (hydrazine-carboxylate) as the ligand to construct MOFs with 2D sheet structures [((

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A series of trinitromethyl/trinitroethyl substituted derivatives of 2,4,6,8,10,12-hexanitro-2,4,6,8,10,12-hexaazatetracyclo[5,5,0, 0(3.11),0(5.9)] dodecane (CL-20) were designed and investigated by theoretical methods. Intramolecular interactions between the trinitromethyl/trinitroethyl and the cage were investigated. The effects of trinitromethyl/trinitroethyl groups on stability of the parent compound are discussed. The results reveal a mutual influence of bond length and dihedral angle between the trinitromethyl and the cage. Compared to CL-20, the sensitivity of derivatives is barely affected. Properties such as density, heat of formation and detonation performance of these novel compounds were also predicted. The introduction of the trinitromethyl group can significantly enhance the oxygen balance, density and detonation properties of the parent compound. The remarkable energy properties make these novel cage compounds competitive high energy density materials.

Recently, Rayleigh scattering-based distributed fiber sensors have been widely used for measurement of static and dynamic phenomena such as temperature change, dynamic strain, and sound waves. In this review paper, several sensing systems including traditional Rayleigh optical time domain reflectometry (OTDR), Φ-OTDR, chirped pulse Φ-OTDR, and optical frequency domain reflectometry (OFDR) are introduced for their working principles and recent progress with different instrumentations for various applications. Beyond the sensing technology and instrumentation, we also discuss new types of fiber sensors, such as ultraweak fiber Bragg gratings and random fiber gratings for distributed sensing and their interrogators. Ultimately, the limitations of Rayleigh-based distributed sensing systems are discussed.

In this paper, we introduce an improved approach of speculative decoding aimed at enhancing the efficiency of serving large language models. Our method capitalizes on the strengths of two established techniques: the classic two-model speculative decoding approach, and the more recent single-model approach, Medusa. Drawing inspiration from Medusa, our approach adopts a single-model strategy for speculative decoding. However, our method distinguishes itself by employing a single, lightweight draft head with a recurrent dependency design, akin in essence to the small, draft model uses in classic speculative decoding, but without the complexities of the full transformer architecture. And because of the recurrent dependency, we can use beam search to swiftly filter out undesired candidates with the draft head. The outcome is a method that combines the simplicity of single-model design and avoids the need to create a data-dependent tree attention structure only for inference in Medusa. We empirically demonstrate the effectiveness of the proposed method on several popular open source language models, along with a comprehensive analysis of the trade-offs involved in adopting this approach.

Nitro-NNO-azoxy group: the unique structure could improve the density, heat of formation, detonation velocity and detonation pressure of an explosive. Compared with the nitro group, the nitro-NNO-azoxy group has a stronger energetic and electron-attracting property.