Effective preparation and collisional decay of atomic condensate in excited bands of an optical lattice

We present a method for the effective preparation of a Bose-Einstein condensate (BEC) into the excited bands of an optical lattice via a standing-wave pulse sequence. With our method, the BEC can be prepared in either a single Bloch state in a excited-band, or a coherent superposition of states in different bands. Our scheme is experimentally demonstrated by preparing a $^{87}$Rb BEC into the $d$-band and the superposition of $s$- and $d$-band states of a one-dimensional optical lattice, within a few tens of microseconds. We further measure the decay of the BEC in the $d$-band state, and carry an analytical calculation for the collisional decay of atoms in the excited-band states. Our theoretical and experimental results consist well.Comment: 9 pages, 5 figures, Accepted by Phys. Rev.

A Resonance Model for Spontaneous Cortical Activity

How human brain function emerges from structure has intrigued researchers for decades and numerous models have been put forward, yet none of them yields a close structure-function relation. Here we present a resonance model based on neuronal spike timing dependent plasticity (STDP) principle to describe the spontaneous cortical activity by incorporating the dynamic interactions between neuronal populations into a wave equation, which is able to accurately predict the resting brain functional connectivity (FC), including the resting-state networks. Besides, the proposed model provides strong theoretical and experimental evidences that the spontaneous dynamic coupling between brain regions fluctuates with a low frequency. Crucially, it is able to account for how the negative functional correlations emerge during resonance. We test the model with a large cohort of subjects (1038) from the Human Connectome Project (HCP) S1200 release in both time and frequency domain, which exhibits superior performance to existing eigen-decomposition models

Understanding Differential Search Index for Text Retrieval

The Differentiable Search Index (DSI) is a novel information retrieval (IR) framework that utilizes a differentiable function to generate a sorted list of document identifiers in response to a given query. However, due to the black-box nature of the end-to-end neural architecture, it remains to be understood to what extent DSI possesses the basic indexing and retrieval abilities. To mitigate this gap, in this study, we define and examine three important abilities that a functioning IR framework should possess, namely, exclusivity, completeness, and relevance ordering. Our analytical experimentation shows that while DSI demonstrates proficiency in memorizing the unidirectional mapping from pseudo queries to document identifiers, it falls short in distinguishing relevant documents from random ones, thereby negatively impacting its retrieval effectiveness. To address this issue, we propose a multi-task distillation approach to enhance the retrieval quality without altering the structure of the model and successfully endow it with improved indexing abilities. Through experiments conducted on various datasets, we demonstrate that our proposed method outperforms previous DSI baselines.Comment: Accepted to Findings of ACL 202

CCA-secure unidirectional proxy re-encryption in the adaptive corruption model without random oracles

Proxy re-encryption (PRE), introduced by Blaze, Bleumer and Strauss in Eurocrypt\u2798, allows a semi-trusted proxy to convert a ciphertext originally intended for Alice into an encryption of the same message intended for Bob. PRE has recently drawn great interest, and many interesting PRE schemes have been proposed. However, up to now, it is still an important question to come up with a chosen-ciphertext secure unidirectional PRE in the adaptive corruption model. To address this problem, we propose a new unidirectional PRE scheme, and prove its chosen-ciphertext security in the adaptive corruption model without random oracles. Compared with the best known unidirectional PRE scheme proposed by Libert and Vergnaud in PKC\u2708, our schemes enjoys the advantages of both higher efficiency and stronger security

DoReMi: Grounding Language Model by Detecting and Recovering from Plan-Execution Misalignment

Large language models encode a vast amount of semantic knowledge and possess remarkable understanding and reasoning capabilities. Previous research has explored how to ground language models in robotic tasks to ensure that the sequences generated by the language model are both logically correct and practically executable. However, low-level execution may deviate from the high-level plan due to environmental perturbations or imperfect controller design. In this paper, we propose DoReMi, a novel language model grounding framework that enables immediate Detection and Recovery from Misalignments between plan and execution. Specifically, LLMs are leveraged for both planning and generating constraints for planned steps. These constraints can indicate plan-execution misalignments and we use a vision question answering (VQA) model to check constraints during low-level skill execution. If certain misalignment occurs, our method will call the language model to re-plan in order to recover from misalignments. Experiments on various complex tasks including robot arms and humanoid robots demonstrate that our method can lead to higher task success rates and shorter task completion times. Videos of DoReMi are available at https://sites.google.com/view/doremi-paper.Comment: 21 pages, 13 figure

Establishment and functional testing of a novel ex vivo extraskeletal osteosarcoma cell model (USZ20-ESOS1)

Extraskeletal osteosarcoma (ESOS) is a rare malignant mesenchymal tumor that originates in the soft tissue. ESOS accounts for less than 1% of all soft tissue sarcomas and exhibits an aggressive behavior with a high propensity for local recurrence and distant metastasis. Despite advances in treatment, the prognosis for ESOS remains poor, with a five-year survival rate of less than 50% and 27% for metastatic patients. Ex vivo models derived from patient samples are critical tools for studying rare diseases with poor prognoses, such as ESOS, and identifying potential new treatment strategies. In this work, we established a novel ESOS ex vivo sarco-sphere model from a metastatic lesion to the dermis for research and functional testing purposes. The ex vivo cell model accurately recapitulated the native tumor, as evidenced by histomorphology and molecular profiles. Through a functional screening approach, we were able to identify novel individual anti-cancer drug sensitivities for different drugs such as romidepsin, miverbresib and to multiple kinase inhibitors. Overall, our new ESOS ex vivo cell model represents a valuable tool for investigating disease mechanisms and answering basic and translational research questions

Ramp hyper-invertible matrices and their applications to MPC protocols

Beerliová-Trubíniová and Hirt introduced hyper-invertible matrix technique to construct the first perfectly secure MPC protocol in the presence of maximal malicious corruptions $\lfloor \frac{n-1}{3} \rfloor$ with linear communication complexity per multiplication gate [5]. This matrix allows MPC protocol to generate correct shares of uniformly random secrets in the presence of malicious adversary. Moreover, the amortized communication complexity of generating each sharing is linear. Due to this prominent feature, the hyper-invertible matrix plays an important role in the construction of MPC protocol and zero-knowledge proof protocol where the randomness needs to be jointly generated. However, the downside of this matrix is that the size of its base field is linear in the size of its matrix. This means if we construct an $n$-party MPC protocol over $\mathbb{F}_q$ via hyper-invertible matrix, $q$ is at least $2n$. In this paper, we propose the ramp hyper-invertible matrix which can be seen as the generalization of hyper-invertible matrix. Our ramp hyper-invertible matrix can be defined over constant-size field regardless of the size of this matrix. Similar to the arithmetic secret sharing scheme, to apply our ramp hyper-invertible matrix to perfectly secure MPC protocol, the maximum number of corruptions has to be compromised to $\frac{(1-\epsilon)n}{3}$. As a consequence, we present the first perfectly secure MPC protocol in the presence of $\frac{(1-\epsilon)n}{3}$ malicious corruptions with constant communication complexity. Besides presenting the variant of hyper-invertible matrix, we overcome several obstacles in the construction of this MPC protocol. Our arithmetic secret sharing scheme over constant-size field is compatible with the player elimination technique, i.e., it supports the dynamic changes of party number and corrupted party number. Moreover, we rewrite the public reconstruction protocol to support the sharings over constant-size field. Putting these together leads to the constant-size field variant of celebrated MPC protocol in [5]. We note that although it was widely acknowledged that there exists an MPC protocol with constant communication complexity by replacing Shamir secret sharing scheme with arithmetic secret sharing scheme, there is no reference seriously describing such protocol in detail. Our work fills the missing detail by providing MPC primitive for any applications relying on MPC protocol of constant communication complexity. As an application of our perfectly secure MPC protocol which implies perfect robustness in the MPC-in-the-Head framework, we present the constant-rate zero-knowledge proof with $3$ communication rounds. The previous work achieves constant-rate with $5$ communication rounds [32] due to the statistical robustness of their MPC protocol. Another application of our ramp hyper-invertible matrix is the information-theoretic multi-verifier zero-knowledge for circuit satisfiability[43]. We manage to remove the dependence of the size of circuit and security parameter from the share size

Establishment, characterization and functional testing of two novel ex vivo extraskeletal myxoid chondrosarcoma (EMC) cell models

Extraskeletal myxoid chondrosarcoma (EMC) is a malignant mesenchymal neoplasm of uncertain differentiation as classified by the WHO Classification of Tumours 2020. Although often associated with pronlonged survival, EMC has high rates of distant recurrences and disease-associated death. EMCs are translocation sarcomas and harbor in > 90% of the cases an NR4A3 rearrangement. The molecular consequences of the NR4A3 gene fusions are not yet fully elucidated as well-characterized ex vivo cell models for EMC are lacking. Patient-derived ex vivo models are important and essential tools for investigating disease mechanisms associated with diseases that are rare, that exhibit poor prognosis and for the identification of potential novel treatment options. We established two novel EMC ex vivo models (USZ20-EMC1 and USZ22-EMC2) for functional testing and research purposes. USZ20-EMC1 and USZ22-EMC2 were established and maintained as sarco-sphere cell models for several months in culture. The cells were molecularly characterized using DNA sequencing and methylation profiling. Both cell models represent their native tumor tissue as confirmed by histomorphology and their molecular profiles, suggesting that native tumor cell function can be recapitulated in the ex vivo models. Using a functional screening approach, novel anti-cancer drug sensitivities including potential synergistic combinations were identified. In conclusion, two novel EMC ex vivo cell models (USZ20-EMC1 and USZ22-EMC2) were successfully established and characterized from native tumor tissues. Both cell models will be useful tools for further investigating disease mechanisms and for answering basic and translational research questions. Keywords: Ex vivo cell model; Extraskeletal myxoid chondrosarcoma; Functional testing; Molecular profiling; Sarco-spher

Addressing Modern Diagnostic Pathology for Patient-Derived Soft Tissue Sarcosphere Models in the Era of Functional Precision Oncology

Responses to therapy often cannot be exclusively predicted by molecular markers, thus evidencing a critical need to develop tools for better patient selection based on relations between tumor phenotype and genotype. Patient-derived cell models could help to better refine patient stratification procedures and lead to improved clinical management. So far, such ex vivo cell models have been used for addressing basic research questions and in preclinical studies. As they now enter the era of functional precision oncology, it is of utmost importance that they meet quality standards to fully represent the molecular and phenotypical architecture of patients' tumors. Well-characterized ex vivo models are imperative for rare cancer types with high patient heterogeneity and unknown driver mutations. Soft tissue sarcomas account for a very rare, heterogeneous group of malignancies that are challenging from a diagnostic standpoint and difficult to treat in a metastatic setting because of chemotherapy resistance and a lack of targeted treatment options. Functional drug screening in patient-derived cancer cell models is a more recent approach for discovering novel therapeutic candidate drugs. However, because of the rarity and heterogeneity of soft tissue sarcomas, the number of well-established and characterized sarcoma cell models is extremely limited. Within our hospital-based platform we establish high-fidelity patient-derived ex vivo cancer models from solid tumors for enabling functional precision oncology and addressing research questions to overcome this problem. We here present 5 novel, well-characterized, complex-karyotype ex vivo soft tissue sarcosphere models, which are effective tools to study molecular pathogenesis and identify the novel drug sensitivities of these genetically complex diseases. We addressed the quality standards that should be generally considered for the characterization of such ex vivo models. More broadly, we suggest a scalable platform to provide high-fidelity ex vivo models to the scientific community and enable functional precision oncology

Connectivity within regions characterizes epilepsy duration and treatment outcome

Finding clear connectome biomarkers for temporal lobe epilepsy (TLE) patients, in particular at early disease stages, remains a challenge. Currently, the whole-brain structural connectomes are analyzed based on coarse parcellations (up to 1,000 nodes). However, such global parcellation-based connectomes may be unsuitable for detecting more localized changes in patients. Here, we use a high-resolution network (~50,000-nodes overall) to identify changes at the local level (within brain regions) and test its relation with duration and surgical outcome. Patients with TLE (n = 33) and age-, sex-matched healthy subjects (n = 36) underwent high-resolution (~50,000 nodes) structural network construction based on deterministic tracking of diffusion tensor imaging. Nodes were allocated to 68 cortical regions according to the Desikan–Killany atlas. The connectivity within regions was then used to predict surgical outcome. MRI processing, network reconstruction, and visualization of network changes were integrated into the NICARA (https://nicara.eu). Lower clustering coefficient and higher edge density were found for local connectivity within regions in patients, but were absent for the global network between regions (68 cortical regions). Local connectivity changes, in terms of the number of changed regions and the magnitude of changes, increased with disease duration. Local connectivity yielded a better surgical outcome prediction (Mean value: 95.39% accuracy, 92.76% sensitivity, and 100% specificity) than global connectivity. Connectivity within regions, compared to structural connectivity between brain regions, can be a more efficient biomarker for epilepsy assessment and surgery outcome prediction of medically intractable TLE