Miracle-dz/UniFAR-FTUs huggingface dataset:
Systematic analysis of relative phase extraction in one-dimensional Bose gases interferometry

Spatially resolved relative phase measurement of two adjacent 1D Bose gases is enabled by matter-wave interference upon free expansion. However, longitudinal dynamics is typically ignored in the analysis of experimental data. We provide an analytical formula showing a correction to the readout of the relative phase due to longitudinal expansion and mixing with the common phase. We numerically assess the error propagation to the estimation of the gases' physical quantities such as correlation functions and temperature. Our work characterizes the reliability and robustness of interferometric measurements, directing us to the improvement of existing phase extraction methods necessary to observe new physical phenomena in cold-atomic quantum simulators.
MamMIL: Multiple Instance Learning for Whole Slide Images with State Space Models

Recently, pathological diagnosis, the gold standard for cancer diagnosis, has achieved superior performance by combining the Transformer with the multiple instance learning (MIL) framework using whole slide images (WSIs). However, the giga-pixel nature of WSIs poses a great challenge for the quadratic-complexity self-attention mechanism in Transformer to be applied in MIL. Existing studies usually use linear attention to improve computing efficiency but inevitably bring performance bottlenecks. To tackle this challenge, we propose a MamMIL framework for WSI classification by cooperating the selective structured state space model (i.e., Mamba) with MIL for the first time, enabling the modeling of instance dependencies while maintaining linear complexity. Specifically, to solve the problem that Mamba can only conduct unidirectional one-dimensional (1D) sequence modeling, we innovatively introduce a bidirectional state space model and a 2D context-aware block to enable MamMIL to learn the bidirectional instance dependencies with 2D spatial relationships. Experiments on two datasets show that MamMIL can achieve advanced classification performance with smaller memory footprints than the state-of-the-art MIL frameworks based on the Transformer. The code will be open-sourced if accepted.
On Representing Electronic Wave Functions with Sign Equivariant Neural Networks

Recent neural networks demonstrated impressively accurate approximations of electronic ground-state wave functions. Such neural networks typically consist of a permutation-equivariant neural network followed by a permutation-antisymmetric operation to enforce the electronic exchange symmetry. While accurate, such neural networks are computationally expensive. In this work, we explore the flipped approach, where we first compute antisymmetric quantities based on the electronic coordinates and then apply sign equivariant neural networks to preserve the antisymmetry. While this approach promises acceleration thanks to the lower-dimensional representation, we demonstrate that it reduces to a Jastrow factor, a commonly used permutation-invariant multiplicative factor in the wave function. Our empirical results support this further, finding little to no improvements over baselines. We conclude with neither theoretical nor empirical advantages of sign equivariant functions for representing electronic wave functions within the evaluation of this work.
Efficient Data Collection for Robotic Manipulation via Compositional Generalization

Data collection has become an increasingly important problem in robotic manipulation, yet there still lacks much understanding of how to effectively collect data to facilitate broad generalization. Recent works on large-scale robotic data collection typically vary a wide range of environmental factors during data collection, such as object types and table textures. While these works attempt to cover a diverse variety of scenarios, they do not explicitly account for the possible compositional abilities of policies trained on the data. If robot policies are able to compose different environmental factors of variation (e.g., object types, table heights) from their training data to succeed when encountering unseen factor combinations, then we can exploit this to avoid collecting data for situations that composition would address. To investigate this possibility, we conduct thorough empirical studies both in simulation and on a real robot that compare data collection strategies and assess whether visual imitation learning policies can compose environmental factors. We find that policies do exhibit composition, although leveraging prior robotic datasets is critical for this on a real robot. We use these insights to provide better practices for in-domain data collection by proposing data collection strategies that exploit composition, which can induce better generalization than naive approaches for the same amount of effort during data collection. We further demonstrate that a real robot policy trained on data from such a strategy achieves a success rate of 77.5% when transferred to entirely new environments that encompass unseen combinations of environmental factors, whereas policies trained using data collected without accounting for environmental variation fail to transfer effectively, with a success rate of only 2.5%. We provide videos at http://iliad.stanford. edu/robot-data-comp/.
Chains of model structures arising from modules of finite Gorenstein dimension

For any integer $n\ge 0$ and any ring $R$, \ $(\mathcal {PGF}_n, \ \mathcal P_n^\perp \cap \mathcal {PGF}^{\perp})$ proves to be a complete hereditary cotorsion pair in $R$-Mod, where $\mathcal {PGF}$ is the class of PGF modules, introduced by J. \v{S}aroch and J. \v{S}\'{t}ov\'{i}\v{c}ek, and \ $\mathcal {PGF}_n$ is the class of $R$-modules of PGF dimension $\le n$. For any Artin algebra $R$, \ $(\mathcal {GP}_n, \ \mathcal P_n^\perp \cap \mathcal {GP}^{\perp})$ proves to be a complete and hereditary cotorsion pair in $R$-Mod, where $\mathcal {GP}_n$ is the class of modules of Gorenstein projective dimension $\le n$. These cotorsion pairs induce two chains of hereditary Hovey triples \ $(\mathcal {PGF}_n, \ \mathcal P_n^\perp, \ \mathcal {PGF}^{\perp})$ and \ $(\mathcal {GP}_n, \ \mathcal P_n^\perp, \ \mathcal {GP}^{\perp})$, and the corresponding homotopy categories in the same chain are the same. It is observed that some complete cotorsion pairs in $R$-Mod can induce complete cotorsion pairs in some special extension closed subcategories of $R$-Mod. Then corresponding results in exact categories $\mathcal {PGF}_n$, \ $\mathcal {GP}_n$, \ $\mathcal {GF}_n$, \ $\mathcal {PGF}^{<\infty}$, \ $\mathcal {GP}^{<\infty}$ and $\mathcal {GF}^{<\infty}$, are also obtained. As a byproduct, $\mathcal{PGF} = \mathcal {GP}$ for a ring $R$ if and only if $\mathcal{PGF}^\perp\cap\mathcal{GP}_n=\mathcal P_n$ for some $n$.
A spatiotemporal style transfer algorithm for dynamic visual stimulus generation

Understanding how visual information is encoded in biological and artificial systems often requires vision scientists to generate appropriate stimuli to test specific hypotheses. Although deep neural network models have revolutionized the field of image generation with methods such as image style transfer, available methods for video generation are scarce. Here, we introduce the Spatiotemporal Style Transfer (STST) algorithm, a dynamic visual stimulus generation framework that allows powerful manipulation and synthesis of video stimuli for vision research. It is based on a two-stream deep neural network model that factorizes spatial and temporal features to generate dynamic visual stimuli whose model layer activations are matched to those of input videos. As an example, we show that our algorithm enables the generation of model metamers, dynamic stimuli whose layer activations within our two-stream model are matched to those of natural videos. We show that these generated stimuli match the low-level spatiotemporal features of their natural counterparts but lack their high-level semantic features, making it a powerful paradigm to study object recognition. Late layer activations in deep vision models exhibited a lower similarity between natural and metameric stimuli compared to early layers, confirming the lack of high-level information in the generated stimuli. Finally, we use our generated stimuli to probe the representational capabilities of predictive coding deep networks. These results showcase potential applications of our algorithm as a versatile tool for dynamic stimulus generation in vision science.
Design and Analysis of Self-Protection:

Today’s software landscape features a high degree of complexity, frequent changes in requirements and stakeholder goals, and uncertainty. Uncertainty and high complexity imply a threat landscape where cybersecurity attacks are a common occurrence, while their consequences are often severe. Self-adaptive systems have been proposed to mitigate the complexity and frequent degree of change by adapting at run-time to deal with situations not known at design time. They, however, are not immune to attacks, as they themselves suﬀer from high degrees of complexity and uncertainty. Therefore, systems that can dynamically defend themselves from adversaries are required. Such systems are called self-protecting systems and aim to identify, analyse and mitigate threats autonomously. This thesis contributes two approaches towards the goal of providing systems with self-protection capabilities. The ﬁrst approach aims to enhance the security of architecture-based self-adaptive systems and equip them with (proactive) self-protection capabilities that reduce the exposed attack surface. We target systems where information about the system components and its adaptation decisions is available, and control over its adaptation is also possible. We formally model the security of the system and provide two methods to analyze its security that help us rank adaptations in terms of their security level: a method based on quantitative risk assessment and a method based on probabilistic veriﬁcation. The results indicate an improvement to the system security when either of our solutions is employed. However, only the second method can provide self-protecting capabilities. We have identiﬁed a direct relationship between security and performance overhead, i.e., higher security guarantees impose analogously higher performance overhead. The second approach targets open decentralized systems where we have limited information about and control over the system entities. Therefore, we attempt to employ decentralized information ﬂow control mechanisms to enforce security by controlling interactions among the system elements. We extend a classical decentralized information ﬂow control model by incorporating trust and adding adaptation capabilities that allow the system to identify security threats and self-organize to maximize the average trust between the system entities. We arrange entities of the system in trust hierarchies that enforce security policies among their elements and can mitigate security issues raised by the openness and uncertainty in the context and environment, without the need for a trusted central controller. The experiment results show that a reasonable level of trust can be achieved and at the same time conﬁdentiality and integrity can be enforced with a low impact on the throughput and latency of messages exchanged in the system.
Koszul complexes and spectra of projective hypersurfaces with isolated singularities

For a projective hypersurface $Z$ with isolated singularities, we generalize some well-known assertions in the nonsingular case due to Griffiths, Scherk, Steenbrink, Varchenko, and others about the relations between the Steenbrink spectrum, the Poincar\'e polynomial of the Jacobian ring, and the roots of Bernstein-Sato polynomial for a defining polynomial $f$ up to sign forgetting the multiplicities. We have to use the pole order spectrum and the alternating sum of the Poincar\'e series of certain subquotients of the Koszul cohomologies, and study the pole order spectral sequence. We show sufficient conditions for vanishing or non-vanishing of the differential $d_1$ of the spectral sequence, which are useful in many applications. We prove also symmetries of the dimensions of the subquotients of Koszul cohomologies, which are crucial for computing the roots of BS polynomials. We can deduce that the roots of BS polynomial whose absolute values are larger than $n-1-n/d$ are determined by the ``torsion part"of the Jacobian ring (modulo the roots of BS polynomial for $Z$) if all the singularities of $Z$ are weighted homogeneous. Here $d=\deg f$ and $n$ is the dimension of the ambient affine space.
Non-Standard Recursion Theory

This paper investigates a new approach to the recursive function theory, which is suitable for dealing with computational models based on term-rewriting. The set of natural numbers is completed into a countable algebraic $ \mathrm{c}\mathrm{p}o$ by adding partial numbers and $ \infty$ . Primitive recursion is interpreted as a way to deﬁne continuous functions on this “non-standard” structure of numerals. It is shown that recursive functions can be formalized as functions deﬁnable by the constant $ \infty$ and quasi-primitive recursion, a slightly generalized version of primitive recursion. Also, computability of those functions is discussed through an order-theoretic semantics of term rewriting systems. Finally, the relation between quasi-primitive and primitive recursion is investigated.
OneTip Is Enough: A Non-Rigid Input Device for Single-Fingertip Human-Computer Interaction With 6-DOF

—With the development of novel display technologies such as 3-D graphical applications, virtual and augmented reality, there are new demands for more efficient and natural multi-degree-of-freedom (DOF) interaction devices. However, traditional rigid touchscreens provide only the 2-D position of the touch point as input information, which cannot meet the needs of high DOF interactions, and many of the existing interaction technologies suffer from the problems of low resolution and lack of information. At the same time, non-rigid interaction interfaces provide deformability for input and have been shown to extend the richness of input vocabulary. This article proposes a new non-rigid input device, OneTip, for single-fingertip human-computer interaction with 6-DOF. In terms of design and manufacture, OneTip employs the bio-inspired design of Skin-On interfaces to mimic the sensitivity of human skin and provide 6-DOF interaction capabilities. In terms of sensing, OneTip uses the visuotactile sensing technique based on the marker displacement method to achieve high-resolution and multi-modal measurements. We propose a novel fingertip pose estimation method based on incipient slip detection, a non-learning algorithm that does not require registration and priori information. Experiments show that OneTip had good 6-D pose estimation accuracy, with RMSEs of translation and rotation not exceeding 0.1mm and 2.6°, respectively, within the linear interval. Extensive experiments were also conducted to explore the application of OneTip in typical virtual manipulation tasks and the possibility of combining it with other interaction devices. This work is intended to serve as a reference for other researchers exploring innovative interaction techniques.
Conference on ‘ Dietary strategies for the management of cardiovascular risk ’ ‘ The way to a man ’ s heart is through his gut microbiota ’ – dietary pro-and prebiotics for the management of cardiovascular risk

The human gut microbiota has been identified as a possible novel CVD risk factor. This review aims to summarise recent insights connecting human gut microbiome activities with CVD and how such activities may be modulated by diet. Aberrant gut microbiota profiles have been associated with obesity, type 1 and type 2 diabetes and non-alcoholic fatty liver disease. Transfer of microbiota from obese animals induces metabolic disease and obesity in germ-free animals. Conversely, transfer of pathogen-free microbiota from lean healthy human donors to patients with metabolic disease can increase insulin sensitivity. Not only are aberrant microbiota profiles associated with metabolic disease, but the flux of metabolites derived from gut microbial metabolism of choline, phosphatidylcholine and L-carnitine has been shown to contribute directly to CVD pathology, providing one explanation for increased disease risk of eating too much red meat. Diet, especially high intake of fermentable fibres and plant polyphenols, appears to regulate microbial activities within the gut, supporting regulatory guidelines encouraging increased consumption of whole-plant foods (fruit, vegetables and whole-grain cereals), and providing the scientific rationale for the design of efficacious prebiotics. Similarly, recent human studies with carefully selected probiotic strains show that ingestion of viable microorganisms with the ability to hydrolyse bile salts can lower blood cholesterol, a recognised risk factor in CVD. Taken together such observations raise the intriguing possibility that gut microbiome modulation by whole-plant foods, probiotics and prebiotics may be at the base of healthy eating pyramids advised by regulatory agencies across the globe. In conclusion, dietary strategies which modulate the gut microbiota or their metabolic activities are emerging as efficacious tools for reducing CVD risk and indicate that indeed, the way to a healthy heart may be through a healthy gut microbiota.
Digitalization and Employee Performance With Mediating Role of Resilience – A Systematic Review By Bibliometric Analysis

Abstract. One must consider not only the technical, but also the social components, when using new technologies. Therefore, it is important that companies prepare their employees for change and the challenges that come with it. It is difficult for the change process to work, without the commitment and motivation of the workforce to do their duties effectively. Start with the right attitude of employees and encourage their openness to change. This can be achieved by developing skills like resilience on the one hand and performance in another hand. The aim of this study is to find out the number of countries doing research in mediating role of employee resilience and its effect on employee performance in digitization and interact in a way that could help an employee cope with a challenging task in a difficult time. For achieving this objective we have used PRISMA frame work as a extracting and screening strategy for data to be analyzed using systematic review using bibliometric analysis by Vos Viewer. It was founded in the study that 33 countries are doing research in this area and out of that United Kingdom, India and United States of America are contributing to a larger extent and the citation score for more in this area after 2020. Thus, digital transformation is a process as it affects the entire business, affecting strategy, operations, innovation and governance structures, but employee resilience will play a mediating role and lead to employee performance and organizational success.
STGraph : A Framework for Temporal Graph Neural Networks

Real-life graphs from various application domains like social networks, transportation networks, and citation networks evolve over time. Temporal Graph Neural Networks (TGNNs) are used for analyzing spatial and temporal properties of graphs from these application domains. We propose STGraph , a framework to program TGNNs. The proposed framework extends Seastar , a vertex-centric programming model for training static GNNs on GPUs. STGraph supports TGNNs for static graphs with temporal signals and discrete-time dynamic graphs (DTDGs). Existing TGNN frameworks store DTDGs as separate snapshots, incurring high memory overhead. As an improvement, STGraph constructs each snapshot on demand during training. This is achieved by integrating the system with dynamic graph data structures capable of building graph snapshots from temporal updates. STGraph is benchmarked against PyTorch Geometric Temporal ( PyG-T ) on an NVIDIA GPU. For static graphs with temporal signals, STGraph shows up to 1.22 ⇥ speedup and up to 2.14 ⇥ memory improvement over PyG-T. For DTDGs, STGraph exhibits up to 1.70 ⇥ speedup and 1.52 ⇥ memory improvement over PyG-T.
Anatomical evaluation of the nasolacrimal groove in 150 dry bones in the Anatolian population

Pupose: In this study, we aimed to evaluate the anatomical features of the nasolacrimal groove in detail by providing a morphological classi�cation based on morphometric evaluations of the nasolacrimal groove. Methods: A total of 150 sagittal dry bones in the Department of Anatomy, Faculty of Medicine, Istanbul University were evaluated. The length and the width at different points of the nasolacrimal canal were calculated. According to the widths of the nasolacrimal canal 10 different morphological types were revealed. Results: The length of the canal was found as mean 13.62 ± 2.42 mm on the right and 12.44 ± 2.68 mm on the left side. The entrance, the base, the upper and the lower thirds of nasolacrimal canal were 6.22 ± 1.19 mm, 7.95 ± 1.85 mm, 5.85 ± 1.06 mm, 6.60 ± 1.54 mm, on the right and 6.08 ± 1.16 mm, 7.24 ± 1.64 mm, 5.45 ± 1.29 mm, 6.23 ± 1.48 mm, on the left side, respectively. The width of the entrance of the nasolacrimal canal was the narrowest width compared to the base, upper and lower thirds in 7/10 types of 71/150 cranial bones. Conclusion: This comprehensive morphological classi�cation of the nasolacrimal groove sheds new light on its complex variations. We support that the �nding of this study has the potential to improve the precision of diagnostic assessments and guide speci�c therapeutic interventions for patients with lacrimal drainage disorders.
The Case of Ice-Breaking along the Coastline of Norrland, Sweden, 1940–1975

Due to the cold climate, navigation along the coast lines of the northern regions in Sweden, Finland, Canada, Russia and the United States must negotiate winter conditions which cause ports to freeze over. In order to avoid the negative economic effects of such interruptions, ice-breaking and other measures to facilitate winter navigation have been introduced. This article deals with the introduction of ice-breaking along the coast line of the five northern-most counties in Sweden, the Norrland region, from a perspective that examines and analyzes the underlying decision-making processes. It is concluded that the ability of regional interest groups to link their demands for an improved ice-breaker service to important aims within macro policy such as trade policy, growth policy and regional development policy contributed to the outcome of the decision-making processes. The international competitiveness of the export industries in Norrland was therefore regarded as a national concern during the decision-making processes. Another factor that contributed to the outcome of the decision-making processes was the sectoral organization within the government maritime bodies. Large-scale planning and operational experimentation was allowed to take place within the ice-breaker service, which convinced the government that ice-breaking and winter navigation were a feasible transport alternative.