9607 research outputs found
Sort by
Performing and evaluating creditworthiness: Bank loans and the financial inclusion of micro/small enterprises in China
No full textDespite being key to China's economic development, micro and small enterprises have long faced difficulties in accessing bank financing. Consequently, the government has introduced digital lending platforms like “Credit Easy Loan” to enable “creditworthy” microentrepreneurs to obtain collateral-free loans. These platforms are expected to collect and integrate social credit data, thus easing the information asymmetry between banks and private enterprises. Based on 10 months of fieldwork in two Chinese cities, this paper investigates banks’ digital and nondigital credit assessment practices and the varied experiences of business loan applicants. It finds that banks and microentrepreneurs may work with underground intermediaries to manipulate application material for different purposes. Microentrepreneurs, especially those without local tangible assets or social networks, still face credit constrains, and discrimination in loan applications. This paper hopes to further our understanding of the social practice around digitalized credit and the dynamics between financial inclusion policies and local implementation
Resource-Efficient Machine Learning (Dagstuhl Seminar 24311)
No full textMachine learning (ML) enables forecasts, even in real-time, at ever lower cost and better accuracy. Today, data scientists are able to collect more data, access that data faster, and apply more complex data analysis than ever. As a result, ML impacts a variety of fields such as healthcare, finance, and entertainment.The advances in ML are mainly thanks to the exponential evolution of hardware, the availability of the large datasets, and the emergence of machine learning frameworks, which hide the complexities of the underlying hardware, boosting the productivity of data scientists. On the other hand, the computational need of the powerful ML models has increased several orders of magnitude in the past decade. A state-of-the-art large language processing model can cost of millions dollars to train in the cloud [The AI Index Report, 2024] without accounting for the electricity cost and carbon footprint [Dodge et al, 2022][Wu et al, 2024]. This makes the current rate of increase in model parameters, datasets, and compute budget unsustainable. To achieve a more sustainable progress in ML in the future, it is essential to invest in more resource-/energy-/cost-efficient solutions.In this Dagstuhl Seminar, our main goal was to reason critically about how we build software and hardware for end-to-end machine learning. The crowd was composed of experts from academia and industry across fields of data management, machine learning, compilers, systems, and computer architecture covering expertise of algorithmic optimizations in machine learning, job scheduling and resource management in distributed computing, parallel computing, and data management and processing. During the seminar, we explored how to improve ML resource efficiency through a holistic view of the ML landscape, which includes data preparation and loading, continual retraining of models in dynamic data environments, compiling ML on specialized hardware accelerators, hardware/software co-design for ML, and serving models for real-time applications with low-latency requirements and constrained resource environments. We hope that the discussions and the work planned during the seminar will lead to increased awareness for understanding the utilization of modern hardware and kickstart future developments to minimize hardware underutilization while still enabling emerging applications powered by ML
The Five-Minute Rule for the Cloud: Caching in Analytics Systems
No full textFor almost 40 years, Gray and Putzolu’s five-minute rule has helped quickly guide system architects to the break-even point between memory caching and direct local storage access. We believe similar rules of thumb are needed for object caches and storage in disaggregated cloud database system designs. However, it is not straightforward to adapt the established rules to the cloud as they presume fixed hardware, while, in the cloud, resources are dynamic and costs are determined by usage. This paper reviews requirements driving object caches, analyzes the design space, defines a cost model, and proposes new rules of thumb to help system designers determine when caches become cost-effective for analytical workloads in the cloud. While perhaps unsurprising, our analysis on AWS shows that caches are beneficial when a system makes (1) two requests per hour for latency-sensitive workloads, or (2) seven requests per second for non-latency-sensitive workloads. These results are consistent with and help explain the near ubiquity of object store caches in cloud analytics systems
IT Project Postmortem Theory: Identifying Root Causes by Process Tracing
Get PDFIT projects often fail. Postmortem analysis is a well-established method for investigating accidents and failures ex post facto to develop preventive actions against similar failures in the future. In IT project postmortems, the identification of root causes is often not explicitly based on well-developed theory for causal inference. This is significant, because strong theoretical foundations strengthen the legitimacy of recommendations based on postmortem analysis. This paper is a theoretical exploration of process tracing for causal inference in postmortem analysis. We (a) analyse how process tracing can provide theoretical foundations for causal inference in IT project postmortem analysis, and we (b) explain how the literature on IT project failure factors can be utilised for identifying root causes and eliminating rival explanations. The analysis of process tracing for IT project postmortems is new and original. The results of this study provide theoretical foundations, a theoretical framework, for postmortem analysis of failed IT projects. The results are applicable for policy makers and practitioners for the endorsement and performance of IT project postmortems
The Complexity of Counting Small Sub-Hypergraphs
Get PDFSubgraph counting is a fundamental and well-studied problem whose computational complexity is well understood. Quite surprisingly, the hypergraph version of subgraph counting has been almost ignored. In this work, we address this gap by investigating the most basic sub-hypergraph counting problem: given a (small) hypergraph and a (large) hypergraph , compute the number of sub-hypergraphs of isomorphic to . Formally, for a family of hypergraphs, let #Sub() be the restriction of the problem to ; the induced variant #IndSub() is defined analogously. Our main contribution is a complete classification of the complexity of these problems. Assuming the Exponential Time Hypothesis, we prove that #Sub() is fixed-parameter tractable if and only if has bounded fractional co-independent edge-cover number, a novel graph parameter we introduce. Moreover, #IndSub() is fixed-parameter tractable if and only if has bounded fractional edge-cover number. Both results subsume pre-existing results for graphs as special cases. We also show that the fixed-parameter tractable cases of #Sub() and #IndSub() are unlikely to be in polynomial time, unless respectively #P = P and Graph Isomorphism P. This shows a separation with the special case of graphs, where the fixed-parameter tractable cases are known to actually be in polynomial time
Chimera:State Space Models Beyond Sequences
No full textTransformer-based deep learning methods have become the standard approach for modeling diverse data such as sequences, images, and graphs. These methods rely on self-attention, which treats data as an unordered set of elements. This ignores the neighborhood structure or graph topology of the data and requires inductive biases--such as position embeddings in sequences and images, or random walks in graphs--to incorporate topology. However, designing such task-specific biases requires significant effort and can introduce side effects that hinder generalization. We introduce Chimera, a unified model that directly incorporates data topology in a principled way, removing the need for domain-specific biases. The key idea is that state space models--which naturally do not require position embeddings--can be generalized to capture any graph topology. Our experiments show that Chimera achieves strong performance across language, vision, and graph domains, outperforming BERT on GLUE by 0.7 points, ViT on ImageNet-1k by 2.6%, and all baselines on the Long Range Graph Benchmark. We further propose algorithmic optimizations to improve Chimera's efficiency: (1) for Directed Acyclic Graphs, Chimera can be implemented as a linear-time recurrence; (2) for general graphs, a simple mathematical relaxation achieves Transformer's quadratic complexity without domain-specific heuristics. These results validate Chimera's core contribution and support the idea that data topology is a powerful inductive bias across modalities