Lake Qinghai, China

Lake Qinghai lies on the northeast corner of the Tibet-Qinghai Plateau. It is a closed-basin lake and the largest water body in China with an area of about 4437 km2. Aragonite and calcite are precipitating from the brackish (TDS 12-14 g/l) and alkaline water (pH 9.1-9.4). Summer rainfall exerts an important control on changes in both lake level and water chemistry. As the lake today is situated at the outer margin of the Asian summer monsoon, past climate changes were sensitively documented in the sedimentary record of the lake. This thesis reports new results obtained after 1991 from two high quality cores, Q14B and Q16C, and on the 26 m drill core. It aims to provide key-site information for the international effort and database of the IGBP-PAGES community on regional paleoenvironmental and paleoclimate changes since the Marine Isotopic Stage 3. The study established a detailed carbonate mineral stratigraphy for the postglacial calcareous succession of the lake. The postglacial carbonate deposition underwent five main phases, each of them representing a distinguishable P-E balance period of the paleo-lake. The study provided new data showing that the isotopic ratios of some primary carbonates such as aragonite laminae do not fall into the d13C-d18O covariant trend. Primary dolomite showed most negative d13C values and their isotopic ratios dissent from the covariant trend. Nevertheless, neither the isotopic ratios of aragonite laminae nor those of primary dolomite represent a breakdown of the d13C-d18O covariant trend, the essential isotopic identity of the closed-basin lake. Results from the seismic Profile 1 and the 26 m drill core from the eastern basin reveal that Lake Qinghai expanded rapidly around 68.7 ka 14C BP and remained in wet but not in full glacial conditions until about 28.8 ka 14C BP. The largest extent of the paleo-lake size was much smaller than that of the Holocene. The sub-bottom sediment formed during this period of time show an offlap sequence, clearly indicating a general trend of shrinking in lake size towards the Last Glacial Maximum (LGM). The lake was in severely cold and arid conditions at the LGM (~28.8-18.3 ka 14C BP), as suggested by the windblown loess-like sandy deposits and the seismic data. The 26 m drill core at the central eastern basin did not reveal moraine deposits, suggesting an exclusion of any glacial advance down to the central areas of the lake during both MIS 3 and MIS 2. Climatic conditions during the MIS 3 were warmer than during the MIS 2 but definitely colder than during the Holocene. The MIS 3 wetter climate therefore was comparatively more favourable for regional glacier advance rather than the MIS 2 in the northern Tibet-Qinghai Plateau. The climate before 11.6 ka 14C BP was much colder and drier than during the Holocene, as evidenced by a shallow lake environment with lower carbonate production and lower organic productivity. The seasonal inflow of sediment-laden water increased abruptly from ~11.6 ka 14C BP, signaling an enhancement of precipitation in the large catchment. Between ~10.7 and 10 ka 14C BP, a negative water balance persisted, as indicated by a development of a carbonate playa lake

High-precision, large-domain three-dimensional manipulation of nano-materials for fabrication nanodevices

Nanoscaled materials are attractive building blocks for hierarchical assembly of functional nanodevices, which exhibit diverse performances and simultaneous functions. We innovatively fabricated semiconductor nano-probes of tapered ZnS nanowires through melting and solidifying by electro-thermal process; and then, as-prepared nano-probes can manipulate nanomaterials including semiconductor/metal nanowires and nanoparticles through sufficiently electrostatic force to the desired location without structurally and functionally damage. With some advantages of high precision and large domain, we can move and position and interconnect individual nanowires for contracting nanodevices. Interestingly, by the manipulating technique, the nanodevice made of three vertically interconnecting nanowires, i.e., diode, was realized and showed an excellent electrical property. This technique may be useful to fabricate electronic devices based on the nanowires' moving, positioning, and interconnecting and may overcome fundamental limitations of conventional mechanical fabrication

Aberrant Calcium Signaling in Astrocytes Inhibits Neuronal Excitability in a Human Down Syndrome Stem Cell Model.

Down syndrome (DS) is a genetic disorder that causes cognitive impairment. The staggering effects associated with an extra copy of human chromosome 21 (HSA21) complicates mechanistic understanding of DS pathophysiology. We examined the neuron-astrocyte interplay in a fully recapitulated HSA21 trisomy cellular model differentiated from DS-patient-derived induced pluripotent stem cells (iPSCs). By combining calcium imaging with genetic approaches, we discovered the functional defects of DS astroglia and their effects on neuronal excitability. Compared with control isogenic astroglia, DS astroglia exhibited more-frequent spontaneous calcium fluctuations, which reduced the excitability of co-cultured neurons. Furthermore, suppressed neuronal activity could be rescued by abolishing astrocytic spontaneous calcium activity either chemically by blocking adenosine-mediated signaling or genetically by knockdown of inositol triphosphate (IP3) receptors or S100B, a calcium binding protein coded on HSA21. Our results suggest a mechanism by which DS alters the function of astrocytes, which subsequently disturbs neuronal excitability

Dynamic Feature Pruning and Consolidation for Occluded Person Re-Identification

Occluded person re-identification (ReID) is a challenging problem due to contamination from occluders, and existing approaches address the issue with prior knowledge cues, eg human body key points, semantic segmentations and etc, which easily fails in the presents of heavy occlusion and other humans as occluders. In this paper, we propose a feature pruning and consolidation (FPC) framework to circumvent explicit human structure parse, which mainly consists of a sparse encoder, a global and local feature ranking module, and a feature consolidation decoder. Specifically, the sparse encoder drops less important image tokens (mostly related to background noise and occluders) solely according to correlation within the class token attention instead of relying on prior human shape information. Subsequently, the ranking stage relies on the preserved tokens produced by the sparse encoder to identify k-nearest neighbors from a pre-trained gallery memory by measuring the image and patch-level combined similarity. Finally, we use the feature consolidation module to compensate pruned features using identified neighbors for recovering essential information while disregarding disturbance from noise and occlusion. Experimental results demonstrate the effectiveness of our proposed framework on occluded, partial and holistic Re-ID datasets. In particular, our method outperforms state-of-the-art results by at least 8.6% mAP and 6.0% Rank-1 accuracy on the challenging Occluded-Duke dataset.Comment: 12 pages, 9 figure

Progressive Text-to-Image Diffusion with Soft Latent Direction

In spite of the rapidly evolving landscape of text-to-image generation, the synthesis and manipulation of multiple entities while adhering to specific relational constraints pose enduring challenges. This paper introduces an innovative progressive synthesis and editing operation that systematically incorporates entities into the target image, ensuring their adherence to spatial and relational constraints at each sequential step. Our key insight stems from the observation that while a pre-trained text-to-image diffusion model adeptly handles one or two entities, it often falters when dealing with a greater number. To address this limitation, we propose harnessing the capabilities of a Large Language Model (LLM) to decompose intricate and protracted text descriptions into coherent directives adhering to stringent formats. To facilitate the execution of directives involving distinct semantic operations-namely insertion, editing, and erasing-we formulate the Stimulus, Response, and Fusion (SRF) framework. Within this framework, latent regions are gently stimulated in alignment with each operation, followed by the fusion of the responsive latent components to achieve cohesive entity manipulation. Our proposed framework yields notable advancements in object synthesis, particularly when confronted with intricate and lengthy textual inputs. Consequently, it establishes a new benchmark for text-to-image generation tasks, further elevating the field's performance standards.Comment: 14 pages, 15 figure

Fine-grained Appearance Transfer with Diffusion Models

Image-to-image translation (I2I), and particularly its subfield of appearance transfer, which seeks to alter the visual appearance between images while maintaining structural coherence, presents formidable challenges. Despite significant advancements brought by diffusion models, achieving fine-grained transfer remains complex, particularly in terms of retaining detailed structural elements and ensuring information fidelity. This paper proposes an innovative framework designed to surmount these challenges by integrating various aspects of semantic matching, appearance transfer, and latent deviation. A pivotal aspect of our approach is the strategic use of the predicted $x_0$ space by diffusion models within the latent space of diffusion processes. This is identified as a crucial element for the precise and natural transfer of fine-grained details. Our framework exploits this space to accomplish semantic alignment between source and target images, facilitating mask-wise appearance transfer for improved feature acquisition. A significant advancement of our method is the seamless integration of these features into the latent space, enabling more nuanced latent deviations without necessitating extensive model retraining or fine-tuning. The effectiveness of our approach is demonstrated through extensive experiments, which showcase its ability to adeptly handle fine-grained appearance transfers across a wide range of categories and domains. We provide our code at https://github.com/babahui/Fine-grained-Appearance-TransferComment: 14 pages, 15 figure

Design of a Robust Radio-Frequency Fingerprint Identification Scheme for Multimode LFM Radar

International audienceRadar is an indispensable part of the Internet of Things (IoT). Specific emitter identification is essential to identify the legitimate radars and, more importantly, to reject the malicious radars. Conventional methods rely on pulse parameters that are not capable to identify the specific emitter as two radars may have the same configuration or a malicious radar can perform spoofing attacks. Radio frequency fingerprint (RFF) is the unique and intrinsic hardware characteristic of devices resulted from hardware imperfection, which can be used as the device identity. This paper proposes a robust and reliable radar identification scheme based on the RFF, taking linear frequency modulation (LFM) radar as a case study. This scheme first classifies the operation mode of the pulses, then eliminates the noise effect, and finally identifies the radar emitters based on the transient and modulation-based RFF features. Experimental results verify the effectiveness of our radar identification scheme among three real LFM radars (same model) operating at four modes, each mode with 2,000 pulses from each radar. The identification rates of the four modes are all higher than 90% when the signal-tonoise ratio (SNR) is about 5 dB. In addition, mode 3 achieves almost 100% identification accuracy even when the SNR is as low as-10 dB