The Royalflush System for VoxCeleb Speaker Recognition Challenge 2022

In this technical report, we describe the Royalflush submissions for the VoxCeleb Speaker Recognition Challenge 2022 (VoxSRC-22). Our submissions contain track 1, which is for supervised speaker verification and track 3, which is for semi-supervised speaker verification. For track 1, we develop a powerful U-Net-based speaker embedding extractor with a symmetric architecture. The proposed system achieves 2.06% in EER and 0.1293 in MinDCF on the validation set. Compared with the state-of-the-art ECAPA-TDNN, it obtains a relative improvement of 20.7% in EER and 22.70% in MinDCF. For track 3, we employ the joint training of source domain supervision and target domain self-supervision to get a speaker embedding extractor. The subsequent clustering process can obtain target domain pseudo-speaker labels. We adapt the speaker embedding extractor using all source and target domain data in a supervised manner, where it can fully leverage both domain information. Moreover, clustering and supervised domain adaptation can be repeated until the performance converges on the validation set. Our final submission is a fusion of 10 models and achieves 7.75% EER and 0.3517 MinDCF on the validation set

Mining Temporal Sequential Patterns Based on Multi-granularities

Sequential pattern mining is an important data mining problem that can extract frequent subsequences from sequences. However, the times between successive items in a sequence is typically used as user-specified constraints to pre-process the input data or to prune the pattern search space. In either cases, the times cannot be used to identify item intervals of sequential patterns. In this paper, we introduce a form of multi-granularity sequence patterns, which is a sequential pattern where each transition time is annotated with multi-granularity boundary interval and average time derived from the source data rather than the user-predetermined time interval or only a typical time. Then we present a novel algorithm, MG-PrefixSpan, of multiple granularity sequential patterns based on PrefixSpan[, which discovers all such patterns. Empirical evaluation shows that MG-PrefixSpan scales up linearly as the size of database, and has a good scalability with respect to the length of sequence and the size of transaction

Gab2 deficiency suppresses high-fat diet-induced obesity by reducing adipose tissue inflammation and increasing brown adipose function in mice.

Obesity is caused by a long-term imbalance between energy intake and consumption and is regulated by multiple signals. This study investigated the effect of signaling scaffolding protein Gab2 on obesity and its relevant regulation mechanism. Gab2 knockout (KO) and wild-type (WT) mice were fed with a standard diet (SD) or high-fat diet (HFD) for 12 weeks. The results showed that the a high-fat diet-induced Gab2 expression in adipose tissues, but deletion of Gab2 attenuated weight gain and improved glucose tolerance in mice fed with a high-fat diet. White adipose tissue and systemic inflammations were reduced in HFD-fed Gab2 deficiency mice. Gab2 deficiency increased the expression of Ucp1 and other thermogenic genes in brown adipose tissue. Furthermore, the regulation of Gab2 on the mature differentiation and function of adipocytes was investigated in vitro using primary or immortalized brown preadipocytes. The expression of brown fat-selective genes was found to be elevated in differentiated adipocytes without Gab2. The mechanism of Gab2 regulating Ucp1 expression in brown adipocytes involved with its downstream PI3K (p85)-Akt-FoxO1 signaling pathway. Our research suggests that deletion of Gab2 suppresses diet-induced obesity by multiple pathways and Gab2 may be a novel therapeutic target for the treatment of obesity and associated complications

Towards additive manufacturing oriented geometric modeling using implicit functions

Surface-based geometric modeling has many advantages in terms of visualization and traditional subtractive manufacturing using computer-numerical-control cutting-machine tools. However, it is not an ideal solution for additive manufacturing because to digitally print a surface-represented geometric object using a certain additive manufacturing technology, the object has to be converted into a solid representation. However, converting a known surface-based geometric representation into a printable representation is essentially a redesign process, and this is especially the case, when its interior material structure needs to be considered. To specify a 3D geometric object that is ready to be digitally manufactured, its representation has to be in a certain volumetric form. In this research, we show how some of the difficulties experienced in additive manufacturing can be easily solved by using implicitly represented geometric objects. Like surface-based geometric representation is subtractive manufacturing-friendly, implicitly described geometric objects are additive manufacturing-friendly: implicit shapes are 3D printing ready. The implicit geometric representation allows to combine a geometric shape, material colors, an interior material structure, and other required attributes in one single description as a set of implicit functions, and no conversion is needed. In addition, as implicit objects are typically specified procedurally, very little data is used in their specifications, which makes them particularly useful for design and visualization with modern cloud-based mobile devices, which usually do not have very big storage spaces. Finally, implicit modeling is a design procedure that is parallel computing-friendly, as the design of a complex geometric object can be divided into a set of simple shape-designing tasks, owing to the availability of shape-preserving implicit blending operations

The application of custom 3D-printed prostheses with ultra-short stems in the reconstruction of bone defects: a single center analysis

Objective: Considering the advantages and widespread presence of 3D-printing technology in surgical treatments, 3D-printed porous structure prostheses have been applied in a wide range of the treatments of bone tumor. In this research, we aimed to assess the application values of the 3D-printed custom prostheses with ultra-short stems for restoring bone defects and maintaining arthrosis in malignant bone tumors of lower extremities in children.Methods: Seven cases of pediatric patients were included in this study. In all cases, the prostheses were porous titanium alloy with ultra-short stems. MSTS 93 (Musculoskeletal Tumor Society) scores were recorded for the functional recovery of the limbs. VAS (Visual analogue scale) scores were utilized to assess the degree of painfulness for the patients. X-ray and MRI (magnetic resonance imaging) were applied to evaluate the bone integration, prostheses aseptic loosening, prostheses fracture, wound healing, and tumor recurrence during follow-up.Results: During follow-up, none of the patients developed any postoperative complications, including prostheses aseptic loosening, prostheses fracture, or tumor recurrence. Radiological examinations during the follow-up showed that prostheses implanted into the residual bone were stably fitted and bone defects were effectively reconstructed. The MSTS 93 scores were 24.9 ± 2.9 (20–28). VAS scores were decreased to 5.8 ± 1.2 (4.0–7.0). No statistically significant differences in leg length discrepancy were observed at the time of the last follow-up.Conclusion: 3D-printing technology can be effectively applied throughout the entire surgical treatment procedures of malignant bone tumors, offering stable foundations for the initial stability of 3D-printed prostheses with ultra-short stems through preoperative design, intraoperative precision operation, and personalized prosthesis matching. With meticulous postoperative follow-up, close monitoring of postoperative complications was ensured. These favorable outcomes indicate that the utilization of 3D-printed custom prostheses with ultra-short stems is a viable alternative for reconstructing bone defects. However, further investigation is warranted to determine the long-term effectiveness of the 3D-printing technique

Efficient and bright warm-white electroluminescence from lead-free metal halides.

Solution-processed metal-halide perovskites are emerging as one of the most promising materials for displays, lighting and energy generation. Currently, the best-performing perovskite optoelectronic devices are based on lead halides and the lead toxicity severely restricts their practical applications. Moreover, efficient white electroluminescence from broadband-emission metal halides remains a challenge. Here we demonstrate efficient and bright lead-free LEDs based on cesium copper halides enabled by introducing an organic additive (Tween, polyethylene glycol sorbitan monooleate) into the precursor solutions. We find the additive can reduce the trap states, enhancing the photoluminescence quantum efficiency of the metal halide films, and increase the surface potential, facilitating the hole injection and transport in the LEDs. Consequently, we achieve warm-white LEDs reaching an external quantum efficiency of 3.1% and a luminance of 1570 cd m-2 at a low voltage of 5.4 V, showing great promise of lead-free metal halides for solution-processed white LED applications