Miracle-dz/UniFAR-FTUs huggingface dataset:
Flip Initial Features: Generalization of Neural Networks Under Sparse Features for Semi-supervised Node Classiﬁcation

Graph neural networks (GNNs) are commonly used in semi-supervised settings. Previous research has primarily focused on ﬁnding appropriate graph ﬁlters (e.g. aggregation methods) to perform well on both homophilic and heterophilic graphs. While these methods are effective, they can still suffer from the sparsity of node features, where the initial data contain few non-zero elements. This can lead to overﬁtting in certain dimensions in the ﬁrst projection matrix, as training samples may not cover the entire range of graph ﬁlters (hyperplanes). To address this, we propose a novel data augmentation strategy. Speciﬁcally, by ﬂipping both the initial features and hyperplane, we create additional space for training, which leads to more precise updates of the learnable parameters and improved robustness for unseen features during inference. To the best of our knowledge, this is the ﬁrst attempt to mitigate the overﬁtting caused by the initial features. Extensive experiments on real-world datasets show that our proposed technique increases node classiﬁcation accuracy by up to 40.2%.
Relational Reasoning Networks

—Neuro-symbolic methods integrate neural architec- tures, knowledge representation and reasoning. However, they have been struggling at both dealing with the intrinsic uncertainty of the observations and scaling to real-world applications. This paper presents Relational Reasoning Networks (R2N), a novel end-to-end model that performs relational reasoning in the latent space of a deep learner architecture, where the representations of constants, ground atoms and their manipulations are learned in an integrated fashion. Unlike ﬂat architectures like Knowledge Graph Embedders, which can only represent relations between entities, R2Ns deﬁne an additional computational structure, ac- counting for higher-level relations among the ground atoms. The considered relations can be explicitly known, like the ones deﬁned by logic formulas, or deﬁned as unconstrained correlations among groups of ground atoms. R2Ns can be applied to purely symbolic tasks or as a neuro-symbolic platform to integrate learning and reasoning in heterogeneous problems with both symbolic and feature-based represented entities. The proposed model overtakes the limitations of previous neuro-symbolic methods that have been either limited in terms of scalability or expressivity. The proposed methodology is shown to achieve state-of-the-art results in different experimental settings.
MOMENTS OF THE DISTRIBUTIONS OF POWERS AND PRODUCTS OF NORMAL VARIATES

1. Introductory . 226 2. Distribution of the cube of a normal variate . 228 3. Distribution of any power of a normal variate . 229 4. Distribution of the product of three independent normal variates 230 5. Distribution of the product of n independent normal variates . 231 6. Distribution of the product of two correlated normal variates . 232 7. Distribution of the product of three correlated normal variates . 235 8. The product of n correlated normal variates . 238 9. The Galton-Macalister distribution . 239 10. Biological applications . 240 11. Summary . 241 References . 242
A Prediction System Service

To better facilitate application performance programming we propose a software optimization strategy enabled by a novel low-latency Prediction System Service (PSS). Rather than relying on nuanced domain-specific knowledge or slapdash heuristics, a system service for prediction encourages programmers to spend their time uncovering new levers for optimization rather than worrying about the details of their control. The core idea is to write optimizations that improve performance in specific cases, or under specific tunings, and leave the decision of how and when exactly to apply those optimizations to the system to learn through feedback-directed learning. Such a prediction service can be implemented in any number of ways, including as a shared library that can be easily reused by software written in different programming languages, and opens the door to both new software optimization patterns and hardware design possibilities. As a demonstration of the utility of this approach, we show that three very different application-targeted optimization scenarios can each benefit from even a very straightforward perceptron-based implementation of the PSS as long as the service latency can be held low. First, we show that PSS can be used to more intelligently guide hardware lock elision with resulting speedups over a baseline implementation by 34% on average. Second, we show that a PSS can find good configuration parameters for PyPy’s Just-In-Time (JIT) compiler resulting in 15% speedup on average. Last, we show PSS can guide the page reclamation task within a kernel memory management subsystem to reduce the average memory latency by 33% on average. In all three cases, this new optimization pattern with service support is able to meet or beat the best-known hand-crafted methods with a fraction of the complexity.
Sigma: Compiling Einstein Summations to Locality-Aware Dataflow

Most dataflow accelerator compilers achieve high performance by mapping each node in a dataflow program to a dedicated hardware element on a dataflow accelerator. However, this approach misses critical data reuse optimizations required to exploit the data bandwidth from fine-grained memory elements, e.g., FIFOs and pipeline registers. Moreover, writing performant dataflow programs requires users to have domain expertise in the underlying dataflow accelerators. To address these issues, we designed Sigma, a novel compiler that supports high-level programming constructs such as Einstein summations, index notations, and tensors, finds opportunities for data reuse from high-level dataflow graphs, and exploits on-chip data bandwidth from fine-grained memory elements. Sigma targeting a research dataflow accelerator demonstrates a 5.4x speedup and 44.6x area-normalized speedup over Nvidia's V100 accelerator, and a 7.1x speedup over hand-written dataflow programs.
A Survey on Measuring Cognitive Workload in Human-Computer Interaction

The ever-increasing number of computing devices around us results in more and more systems competing for our attention, making cognitive workload a crucial factor for the user experience of human-computer interfaces. Research in Human-Computer Interaction (HCI) has used various metrics to determine users’ mental demands. However, there needs to be a systematic way to choose an appropriate and effective measure for cognitive workload in experimental setups, posing a challenge to their reproducibility. We present a literature survey of past and current metrics for cognitive workload used throughout HCI literature to address this challenge. By initially exploring what cognitive workload resembles in the HCI context, we derive a categorization supporting researchers and practitioners in selecting cognitive workload metrics for system design and evaluation. We conclude with three following research gaps: (1) defining and interpreting cognitive workload in HCI, (2) the hidden cost of the NASA-TLX, and (3) HCI research as a catalyst for workload-aware systems, highlighting that HCI research has to deepen and conceptualize the understanding of cognitive workload in the context of interactive computing systems.
NUBA: Non-Uniform Bandwidth GPUs

The parallel execution model of GPUs enables scaling to hundreds of thousands of threads, which is a key capability that many modern high-performance applications exploit. GPU vendors are hence increasing the compute and memory resources with every GPU generation — resulting in the need to efficiently stitch together a plethora of Symmetric Multiprocessors (SMs), Last-Level Cache (LLC) slices and memory controllers while maximizing bandwidth and keeping power consumption and design complexity in check. Conventional GPUs are Uniform Bandwidth Architectures (UBAs) as they provide equal bandwidth between all SMs and all LLC slices. UBA GPUs require a uniform high-bandwidth Network-on-Chip (NoC), and our key observation is that provisioning a NoC to match the LLC slice bandwidth incurs a hefty power and complexity overhead. We propose the Non-Uniform Bandwidth Architecture (NUBA), a GPU system architecture aimed at fully utilizing LLC slice bandwidth. A NUBA GPU consists of partitions that each feature a few SMs and LLC slices as well as a memory controller — hence exposing the complete LLC bandwidth to the SMs within a partition since they can be connected with point-to-point links — and a NoC between partitions — to enable access to remote data.Exploiting the potential of NUBA GPUs however requires carefully co-designing system software, the compiler and architectural policies. The critical system software component is our Local-And-Balanced (LAB) page placement policy which enables the GPU driver to place data in local partitions while avoiding load imbalance. Moreover, we propose Model-Driven Replication (MDR) which identifies read-only shared data with data-flow analysis at compile time. At run time, MDR leverages an architectural mechanism that replicates read-only shared data across LLC slices when this can be done without pressuring cache capacity. With LAB and MDR, our NUBA GPU improves average performance by 23.1% and 22.2% (and up to 183.9% and 182.4%) compared to iso-resource memory-side and SM-side UBA GPUs, respectively. When the NUBA concept is leveraged to reduce overhead while maintaining similar performance, NUBA reduces NoC power consumption by 12.1× and 9.4×, respectively.
Siebzehntheilung des Lemniscatenumfangs durch alleinige Anwendung von Lineal und Cirkel.

Obgleich bereits Abel in seinen „Recherches sur les fonctions elliptiques (Oeuvres completes, tome I., p. 141—252) nachgewiesen hat, dass der Lemniscatenumfang durch alleinige Anwendung von Lineal und Cirkel in n gleiche Theile getheilt werden kann, wenn n eine Primzahl von der Form 2"+ 1 ist, wenn also n gleich 5, 17, 257, . , so ist dennoch meines Wissens die Theilung in 17 gleiche Theile geometrisch bis jetzt noch nicht ausgef hrt worden. Indem ich daher die L sung dieser Aufgabe versuche, lege ich die Betrachtungen und zum Theil auch die Bezeichnungen zu Grunde, die Abel selbst in der oben erw hnten Abhandlung benutzt hat.
Evaluating rural tourism competitiveness: Application of PROMETHEE-GAIA method

Abstract This study aims to analyze the factors that determine rural tourism competitiveness in Indonesia, measure the tourism competitiveness in each rural tourism, and discover the gap between rural tourism in four provinces. This study adopts the Travel and Tourism Competitiveness Report (TTCR) Model from the World Economic Forum, and we develop the combination between the PROMETHEE method and GAIA plane. This study finds that Giri Emas is rural tourism with the highest score of competitiveness index compared to others. Generally, the tourism sector potential in Indonesia is great and must be developed with additional sense of policy to have a better performance. Stakeholders need to give attention to the Health and Hygiene sub-pillar to be ready during pandemic and post-pandemic. This study encourages policymakers to develop an appropriate strategy that can make the performance of the tourism sector better. Managerial implications are that rural tourism needs to approach various aspects other than the tourist attraction factors. Policymakers should give priority to health facilities and capacities as part of the pandemic response. This study measures the tourism competitiveness of villages with semi-structured interviews and questionnaires. Therefore, it will portray a more detail tourism in Indonesia.
How fast is sea level rising?

STATE OF KNOWLEDGE. Sea level has been rising since the end of the last ice age (~20,000 years ago). During periods of rapid ice sheet loss, sea level rose in excess of 40 mm/year. During the twentieth century, the rate averaged 1.5 (1.1–1.9) mm/year, due primarily to warming of the upper ocean (thermal expansion) but with a contribution from glacier loss. More recently, the rate of sea level rise has increased to a present value of about 3.7 ± 0.5 mm/yr (2006–2018), mostly due to increasing losses from glaciers and the Greenland Ice Sheet (Figure 1). The globally averaged rate of sea level rise is projected to continue to increase as the oceans continue to warm and glaciers and ice sheets shrink faster. Estimates of globally averaged sea level by 2100 are 0.44 to 1.01 m higher than the average during 1986 to 2005, depending on the actual emissions of greenhouse gases by continued global economic development. Sea level increases in this range will cause extensive damage in the United States, particularly along the Atlantic, Gulf, and northern Alaskan coasts. Moreover, a significant possibility exists that actual sea level rise will exceed these estimates due to ice– climate interactions not fully included in models. Local changes of sea level can differ markedly from these globally averaged rates (Figure 2). Three major contributors to sea level underlie these local variations, each of which have unique geographic variability. Warming of the ocean is causing the water to expand and, thus, sea level to rise; however, warming rates and ocean layer thicknesses vary. Changes in surface winds and the exchange of heat and freshwater between the air and ocean change ocean circulation and therefore regional sea level. As warmer temperatures both melt more ice and increase ice flow into the ocean, loss of land ice (e.g., glaciers and the Greenland and Antarctic Ice Sheets) increases the mass of the oceans. These large changes in the distribution of water change the Earth’s gravitational field and the surface load on the Earth’s surface. The resulting movement of the
Building generalized inverses of matrices using only row and column operations

Most students complete their first and only course in linear algebra with the understanding that a real, square matrix A has an inverse if and only if rref(A), the reduced row echelon form of A, is the identity matrix I n . That is, if they apply elementary row operations via the Gauss-Jordan algorithm to the partitioned matrix [A | I n ] to obtain [rref(A) | P], then the matrix A is invertible exactly when rref(A) = I n , in which case, P = A −1. Many students must wonder what happens when A is not invertible, and what information P conveys in that case. That question is, however, seldom answered in a first course. We show that investigating that question emphasizes the close relationships between matrix multiplication, elementary row operations, linear systems, and the four fundamental spaces associated with a matrix. More important, answering that question provides an opportunity to show students how mathematicians extend results by relaxing hypotheses and then exploring the strengths and limitations of the resulting generalization, and how the first relaxation found is often not the best relaxation to be found. Along the way, we introduce students to the basic properties of generalized inverses. Finally, our approach should fit within the time and topic constraints of a first course in linear algebra.
scMultiome analysis identifies embryonic hindbrain progenitors with mixed rhombomere identities

Rhombomeres serve to position neural progenitors in the embryonic hindbrain, thereby ensuring appropriate neural circuit formation, but the molecular identities of individual rhombomeres and the mechanism whereby they form have not been fully established. Here we apply scMultiome analysis in zebrafish to molecularly resolve all rhombomeres for the first time. We find that rhombomeres become molecularly distinct between 10hpf (end of gastrulation) and 13hpf (early segmentation). While the mature hindbrain consists of alternating odd- versus even-type rhombomeres, our scMultiome analyses do not detect extensive odd versus even characteristics in the early hindbrain. Instead, we find that each rhombomere displays a unique gene expression and chromatin profile. Prior to the appearance of distinct rhombomeres, we detect three hindbrain progenitor clusters (PHPDs) that correlate with the earliest visually observed segments in the hindbrain primordium and that represent prospective rhombomere r2/r3 (possibly including r1), r4 and r5/r6, respectively. We further find that the PHPDs form in response to Fgf and RA morphogens and that individual PHPD cells co-express markers of multiple mature rhombomeres. We propose that the PHPDs contain mixed-identity progenitors and that their subdivision into individual mature rhombomeres requires resolution of mixed transcription and chromatin states.
Autoinhibited kinesin-1 adopts a hierarchical folding pattern

Conventional kinesin-1 is the primary anterograde motor in cells for transporting cellular cargo. While there is a consensus that the C-terminal tail of kinesin-1 inhibits motility, the molecular architecture of a full-length autoinhibited kinesin-1 remains unknown. Here, we combine cross-linking mass spectrometry (XL-MS), electron microscopy (EM), and AlphaFold structure prediction to determine the architecture of the full-length autoinhibited kinesin-1 homodimer [kinesin-1 heavy chain (KHC)] and kinesin-1 heterotetramer [KHC bound to kinesin light chain 1 (KLC1)]. Our integrative analysis shows that kinesin-1 forms a compact, bent conformation through a break in coiled coil 3. Moreover, our XL-MS analysis demonstrates that kinesin light chains stabilize the folded inhibited state rather than inducing a new structural state. Using our structural model, we show that disruption of multiple interactions between the motor, stalk, and tail domains is required to activate the full-length kinesin-1. Our work offers a conceptual framework for understanding how cargo adaptors and microtubule-associated proteins relieve autoinhibition to promote activation.
Nance-Horan-Syndrome-like 1b controls mesodermal cell migration by regulating protrusion and actin dynamics during zebrafish gastrulation

Cell migrations are crucial for embryonic development, wound healing, the immune response, as well as for cancer progression. During mesenchymal cell migration, the Rac1-WAVE-Arp2/3 signalling pathway induces branched actin polymerisation, which protrudes the membrane and allows migration. Fine-tuning the activity of the Rac1-WAVE-Arp2/3 pathway modulates protrusion lifetime and migration persistence. Recently, NHSL1, a novel interactor of the Scar/WAVE complex has been identified as a negative regulator of cell migration in vitro. We here analysed its function in vivo, during zebrafish gastrulation, when nhsl1b is specifically expressed in migrating mesodermal cells. Loss and gain of function experiments revealed that nhsl1b is required for the proper migration of the mesoderm, controlling cell speed and migration persistence. Nhsl1b localises to the tip of actin-rich protrusions where it controls protrusion dynamics, its loss of function reducing the length and lifetime of protrusions, whereas overexpression has the opposite effect. Within the protrusion, Nhsl1b knockdown increases F-actin assembly rate and retrograde flow. These results identify Nhsl1b as a cell type specific regulator of cell migration and highlight the importance of analysing the function of regulators of actin dynamics in physiological contexts.
Phylogenetics and phylogeography of Euphorbia canariensis reveal an extreme Canarian-Asian disjunction and limited inter-island colonization

Euphorbia canariensis is an iconic endemic species of the Canary Islands and one of the most characteristic species of lowland xerophytic communities known, in Spanish, as ‘cardonal-tabaibal’. This species is widely distributed in the archipelago, which contrasts with the theoretically low dispersal abilities suggested by its unspecialized diasporas. Furthermore, the phylogenetic relationships of this species are unclear, although it is thought to be related to the Indian E. epiphylloides and not to other cactus-like Euphorbia of the Canary Islands (E. handiensis) and Africa. Here we aimed to reconstruct the evolutionary history of E. canariensis at two levels: (i) a phylogenetic approach aimed at unravelling relationships of this species and large-scale biogeographic patterns, and (ii) a phylogeographic approach focused on the history of colonization between islands of the Canarian archipelago in relation to habitat availability for this species through time. Based on previous phylogenetic studies of Euphorbia, we sequenced the ITS region for E. canariensis and several potentially related species to build a phylogenetic framework. We also sequenced two cpDNA regions for 92 individuals from 29 populations of E. canariensis representing its distribution range. We estimated the number of inter-island colonization events using PAICE, a recently developed method that includes a sampling effort correction. Additionally, we used species distribution modelling (SDM) to project current habitat availability for E. canariensis to past periods. Phylogenetic results supported the Canarian E. canariensis as closely related to the Southeast Asian E. epiphylloides and E. sessiliflora. In the Canarian archipelago, E. canariensis displayed a surprising west-to-east colonization pattern. The estimated number of inter-island colonization events was c. 20 – 50, and SDM suggested an increase in habitat availability in recent times. In summary, in this study we confirmed an extreme biogeographic disjunction between Macaronesia and Southeast Asia, described only for a small number of plant species, and estimated relatively low dispersal capabilities for E. canariensis, with a west-to-east colonization pattern in the Canary Islands. Highlights The Canarian Euphorbia canariensis is sister to Southeast Asian species. This lineage shows one of the widest disjunctions affecting Macaronesian plants. The lack of intermediate relatives could be the result of extinction events. E. canariensis displays low colonization ability in the Canarian archipelago.
Improving Gene Regulatory Network Inference using Dropout Augmentation

Motivation Many methods have been proposed to infer gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNA-seq) data. One major challenge when working with single-cell data is the prevalence of dropout events, when some expression values are not captured. Here we propose to improve GRN inference using Dropout Augmentation (DA), which helps models stay robust against stochastic dropout events by introducing more dropout noise during training. Results Benchmarking experiments illustrate the improved performance and increased stability of the proposed DAZZLE model over existing approaches. Exploration of the source of gain shows that DA plays a significant role in improving performance, while restricting the sparsity of the adjacency matrix at a later stage stabilizes the model. The practical application of the DAZZLE model on a longitudinal mouse microglia dataset, containing over 15,000 genes, illustrates its ability to handle real-world single cell data with minimal gene filtration. Conclusions The improved robustness and stability of DAZZLE make it a practical and valuable addition to the toolkit for GRN inference from single-cell data. Dropout Augmentation may have wider applications beyond the GRN-inference problem. Availability and implementation Project website: https://bcb.cs. tufts.edu/DAZZLE; Visualization of inferred mouse microglia network: https://bcb.cs. tufts.edu/DAZZLE/hammond.html; Code available at https://github.com/TuftsBCB/dazzle and on PyPI under the grn-dazzle package. Contact hao.zhu@tufts.edu; donna.slonim@tufts.edu