Model: shahafvl/reasonir-8b-scientific-parent-prompt
Author: shahafvl
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, llama, sentence-similarity, feature-extraction, dense, generated_from_trainer, dataset_size:161986, loss:MultipleNegativesRankingLoss, custom_code, arxiv:1908.10084, arxiv:1705.00652, base_model:reasonir/ReasonIR-8B, base_model:finetune:reasonir/ReasonIR-8B, model-index, endpoints_compatible, region:us
Base models (finetune): id: reasonir/ReasonIR-8B
Card metadata: tags: sentence-transformers, sentence-similarity, feature-extraction, dense, generated_from_trainer, dataset_size:161986, loss:MultipleNegativesRankingLoss
base_model: reasonir/ReasonIR-8B
widget: source_sentence: The 3-T system offers an improved SNR and thus can improve the precision of T 1 mapping over the 1.5-T system. However, the TESO-IRFSE technique proposed here applies RF pulses frequently and thus scan time efficiency may be limited in some cases by the higher RF power deposition at 3 T. Our TESO-IRFSE technique has successfully increased the scan time efficiency compared to the conventional IRSE and IRFSE T 1 mapping techniques, and it produces precise and highly reproducible full-brain T 1 maps.
sentences: In quantitative T1 imaging, there is a trade-off between maximizing precision through higher field strengths and advanced pulse designs and maintaining acceptable scan efficiency under RF power deposition limits. Methods that optimize inversion-recovery sampling schemes and refocusing strategies can substantially reduce acquisition time compared with traditional protocols, while preserving or even enhancing the reliability of full-brain T1 estimates., In integrative regulatory network models, adding biologically motivated sequence features via hierarchical or logit priors increases inferential accuracy but also makes posterior optimization challenging. This has prompted the development of streamlined model formulations and the adoption of Markov chain Monte Carlo techniques as substitutes or complements to variational EM approaches in order to reduce computation time while maintaining modeling flexibility., Models with strong representational power are not inherently constrained to recover the true data-generating process; instead, they can conform closely to any observed sample, including its random variation. This creates a tension between flexibility and generalization, emphasizing the need for appropriate regularization, validation, and model selection procedures., source_sentence: We then illustrate the autonomic management of multimodal interaction by considering a simple scenario. The DynaMo framework leverages recent advances in model-based engineering and in service-oriented components [6] . MULTIMODALITY IN SERVICE-BASED ENVIRONMENTS A major problem is that developing multimodal addedvalue services through the opportunistic and correct integration of volatile, heterogeneous elements is still a major challenge in software engineering.
sentences: In hyperspectral unmixing, physically based models can sometimes accommodate external perturbations such as illumination changes, yet they struggle to capture the intrinsic spectral variability of endmembers, which arises from complex, material-specific mechanisms and environmental influences. This has encouraged a shift toward probabilistic and statistical representations of endmember signatures, where variability is treated as a random process rather than explicitly derived from first principles., While prior approaches have typically optimized specific implementation blocks under dynamic power constraints, we propose a design methodology that relies on accurate architectural simulation to evaluate competing configurations before committing to a physical realization. This methodology is particularly relevant as technology scaling has shifted the power profile of digital systems, making leakage power a primary design concern and necessitating accurate early-stage estimation of all major power components., Model-based, service-oriented frameworks for multimodal interaction attempt to provide reusable building blocks and runtime adaptation mechanisms. Despite these advances, the opportunistic discovery, binding, and orchestration of heterogeneous and short-lived services for added-value multimodal applications is still difficult, especially when stringent requirements on timing, consistency across modalities, and fault tolerance must be satisfied., source_sentence: Second, any query rewriting strategy that leverages UDFs must handle a potentially explosive number of operators, because arbitrary UDFs can participate in the rewrite. Third, the abundance of materialized views in an opportunistic physical design leads to a correspondingly large search space of candidate views for rewriting, as these views are cheap to keep around when storage is available. Existing MapReduce reuse systems like ReStore [5] and MRShare [16] do not exploit semantic information about execution artifacts and are restricted to reusing cached outputs only when the underlying plans match syntactically.
sentences: Many learning-based shape description methods require extensive training data and may be sensitive to certain geometric transformations, such as non-uniform scaling or shearing. While some prior work has incorporated perceptual findings to derive more robust, transformation-invariant symbol descriptors, achieving consistent performance across varied drawing styles and application domains remains an open challenge that some studies explicitly set aside., The integration of user-defined functions into cost-based query rewriting significantly enlarges the space of logical and physical operators that need to be considered, complicating both search and pruning. When this is combined with an opportunistic materialization strategy that maintains many intermediate results as views, the optimizer is confronted with a very large and potentially expensive search over alternative rewrites. Existing mechanisms for reusing distributed data processing jobs typically treat intermediate results as opaque and rely on syntactic plan equivalence, which limits their ability to exploit more general semantic redundancies among computations., In standards-compliant broadcast systems, the choice of codec profile can severely limit the set of error-resilient or switching tools available to system designers. For example, picture types designed explicitly for drift-free switching between streams are sometimes confined to non-baseline profiles, which are excluded by certain delivery standards, forcing implementers to rely on less elegant or more bandwidth-intensive workarounds., source_sentence: More recently, evidence has emerged that dynamic demand-side management (DSM) schemes do not automatically yield lower overall energy consumption, but rather reshape load profiles. As outlined in Table 5, multiple approaches can be incorporated into DSM design and deployment. However, despite its novelty and technological diversity, ongoing DSM adoption is hampered by several barriers, such as insufficient ICT infrastructure, misaligned market structures and incentives, and limited stakeholder awareness of DSM benefits.
sentences: Emerging smart grid strategies, particularly those relying on flexible and automated DSM, reveal that altering load profiles is often more feasible than achieving net demand reductions. Although numerous technical solutions and planning methodologies have been proposed to support DSM, their real-world deployment is constrained by persistent challenges such as inadequate ICT backbones, poorly designed market incentives, and limited recognition of the long-term benefits to utilities and end-users., Empirical studies of sparse measurement designs suggest that there exists a non-trivial range of matrix densities for which compressed sensing algorithms, including generic convex optimization methods, achieve high-quality reconstruction, whereas overly sparse constructions trigger a sudden and severe loss of performance., Empirical analyses of demand-side management programmes have shown that advanced, dynamic control strategies may primarily reallocate electricity use over time rather than unequivocally decreasing total consumption. As various planning tools and control mechanisms are integrated into DSM schemes, implementation continues to face structural barriers, including infrastructure deficits, misaligned regulatory and market environments, and insufficient stakeholder knowledge about the economic and operational advantages of these measures., source_sentence: Running document analytics pipelines can be highly time-consuming, particularly as the underlying corpora expand at a rapid pace. In addition, these workloads typically demand substantial storage capacity and main memory. A widely adopted strategy to alleviate the storage pressure is to apply data compression.
sentences: Data-intensive analytics workloads are often both computationally expensive and demanding in terms of storage and memory resources, with costs escalating as datasets grow. One prevalent method for alleviating storage and memory pressure is to store data in compressed form., Endmember variability in spectral unmixing encompasses both extrinsic factors, such as sensor geometry and illumination, and intrinsic factors related to the physical and chemical properties of the materials. While the former can often be approximated with analytical radiative transfer models, the latter is generally too complex to describe explicitly, motivating the adoption of statistical distributions, mixture models, or stochastic processes to model endmember spectra in high-dimensional spaces., Studies on virus–host interactions increasingly highlight that co-infected individuals can display unique transcriptional signatures compared to those infected by a single virus, underscoring the importance of examining multiple infection states. At the same time, global change research has emphasized that coastal areas, where human populations and infrastructure are heavily concentrated, are disproportionately exposed to emerging environmental hazards such as sea-level rise and coastal flooding.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics: cosine_accuracy@1, cosine_accuracy@3, cosine_accuracy@5, cosine_accuracy@10, cosine_precision@1, cosine_precision@3, cosine_precision@5, cosine_precision@10, cosine_recall@1, cosine_recall@3, cosine_recall@5, cosine_recall@10, cosine_ndcg@10, cosine_mrr@10, cosine_map@100, cosine_accuracy
model-index: name: SentenceTransformer based on reasonir/ReasonIR-8B
results: task: type: information-retrieval
name: Information Retrieval
dataset: name: ir parent grandparent combined
type: ir_parent_grandparent_combined
metrics: type: cosine_accuracy@1
value: 1
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 1
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 1
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 1
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 1
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 1
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.987
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.5949499999999999
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.16668571428571424
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.5000571428571429
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.8225285714285716
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.9915642857142857
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.9924375690374935
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 1
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.9885627515931619
name: Cosine Map@100
verified: no, task: type: triplet
name: Triplet
dataset: name: orig vs parent
type: orig_vs_parent
metrics: type: cosine_accuracy
value: 0.989995539188385
name: Cosine Accuracy
verified: no, task: type: triplet
name: Triplet
dataset: name: orig vs grandparent
type: orig_vs_grandparent
metrics: type: cosine_accuracy
value: 0.010004446841776371
name: Cosine Accuracy
verified: no, task: type: triplet
name: Triplet
dataset: name: sib vs parent
type: sib_vs_parent
metrics: type: cosine_accuracy
value: 0.9895508885383606
name: Cosine Accuracy
verified: no, task: type: triplet
name: Triplet
dataset: name: sib vs grandparent
type: sib_vs_grandparent
metrics: type: cosine_accuracy
value: 0.010449088178575039
name: Cosine Accuracy
verified: no
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Created: 2025-12-18 15:03:19