Miracle-dz/UniFAR-FTUs huggingface dataset:
Sparse Matrix Multiplication in the Low-Bandwidth Model

We study matrix multiplication in the low-bandwidth model: There are n computers, and we need to compute the product of two n × n matrices. Initially computer i knows row i of each input matrix. In one communication round each computer can send and receive one O(logn)-bit message. Eventually computer i has to output row i of the product matrix. We seek to understand the complexity of this problem in the uniformly sparse case: each row and column of each input matrix has at most d non-zeros and in the product matrix we only need to know the values of at most d elements in each row or column. This is exactly the setting that we have, e.g., when we apply matrix multiplication for triangle detection in graphs of maximum degree d. We focus on the supported setting: the structure of the matrices is known in advance; only the numerical values of nonzero elements are unknown.
Chained Generalisation Bounds

This work discusses how to derive upper bounds for the expected generalisation error of supervised learning algorithms by means of the chaining technique. By developing a general theoretical framework, we establish a duality between generalisation bounds based on the regularity of the loss function, and their chained counterparts, which can be obtained by lifting the regularity assumption from the loss onto its gradient. This allows us to re-derive the chaining mutual information bound from the literature, and to obtain novel chained information-theoretic generalisation bounds, based on the Wasserstein distance and other probability metrics. We show on some toy examples that the chained generalisation bound can be signiﬁcantly tighter than its standard counterpart, particularly when the distribution of the hypotheses selected by the algorithm is very concentrated.
Parametric exploration of zero-energy modes in three-terminal InSb-Al nanowire devices

We systematically study three-terminal InSb-Al nanowire devices by using radio-frequency reﬂectometry. Tunneling spectroscopy measurements on both ends of the hybrid nanowires are performed while systematically varying the chemical potential, magnetic ﬁeld and junction transparencies. Identifying the lowest-energy state allows for the construction of lowest- and zero-energy state diagrams, which show how the states evolve as a function of the aforementioned parameters. Impor-tantly, comparing the diagrams taken for each end of the hybrids enables the identiﬁcation of states which do not coexist simultaneously, ruling out a signiﬁcant amount of the parameter space as candi-dates for a topological phase. Furthermore, altering junction transparencies ﬁlters out zero-energy states sensitive to a local gate potential. Such a measurement strategy signiﬁcantly reduces the time necessary to identify a potential topological phase and minimizes the risk of falsely recognizing trivial bound states as Majorana zero modes.
Improving Point Cloud Based Place Recognition with Ranking-based Loss and Large Batch Training

The paper presents a simple and effective learning-based method for computing a discriminative 3D point cloud descriptor for place recognition purposes. Recent state-of-the-art methods have relatively complex architectures such as multiscale pyramid of point Transformers combined with a pyramid of feature aggregation modules. Our method uses a simple and efficient 3D convolutional feature extraction, based on a sparse voxelized representation, enhanced with channel attention blocks. We employ recent advances in image retrieval and propose a modified version of a loss function based on a differentiable average precision approximation. Such loss function requires training with very large batches for the best results. This is enabled by using multistaged backpropagation. Experimental evaluation on the popular benchmarks proves the effectiveness of our approach, with a consistent improvement over the state of the art.
Top-N Recommendation Algorithms: A Quest for the State-of-the-Art

Research on recommender systems algorithms, like other areas of applied machine learning, is largely dominated by efforts to improve the state-of-the-art, typically in terms of accuracy measures. Several recent research works however indicate that the reported improvements over the years sometimes “don’t add up”, and that methods that were published several years ago often outperform the latest models when evaluated independently. Different factors contribute to this phenomenon, including that some researchers probably often only fine-tune their own models but not the baselines. In this paper, we report the outcomes of an in-depth, systematic, and reproducible comparison of ten collaborative filtering algorithms—covering both traditional and neural models—on several common performance measures on three datasets which are frequently used for evaluation in the recent literature. Our results show that there is no consistent winner across datasets and metrics for the examined top-n recommendation task. Moreover, we find that for none of the accuracy measurements any of the considered neural models led to the best performance. Regarding the performance ranking of algorithms across the measurements, we found that linear models, nearest-neighbor methods, and traditional matrix factorization consistently perform well for the evaluated modest-sized, but commonly-used datasets. Our work shall therefore serve as a guideline for researchers regarding existing baselines to consider in future performance comparisons. Moreover, by providing a set of fine-tuned baseline models for different datasets, we hope that our work helps to establish a common understanding of the state-of-the-art for top-n recommendation tasks.
Anisotropic superconductivity in ZrB$_{12}$ near critical Bogomolnyi point

The superconductors with the Ginzburg-Landau (G-L) parameter (κ) ∼ 1/ √ 2 exist near a critical Bogolomonyi (B) point where they show inter-type domains between type-I and type-II superconductivity. While such physics is well understood for isotropic superconductors, the experimental investigation of the physics of anisotropic superconductors near a critical B-point remains an unattained goal mainly due to the unavailability of model material systems. Theoretically, such superconductors are expected to show type-I or type-II behaviour for definite directions of an applied magnetic field. Here, from directional point-contact Andreev reflection spectroscopy and field-angle dependent ac magnetic susceptibility measurements, we show that ZrB12 is an anisotropic superconductor, and it exhibits field-direction dependent type-I and type-II behavior. These observations match remarkably well with the theoretical expectations for an anisotropic superconductor near a critical B-point. Therefore, our results project ZrB12 as a model material system where the physics of inter-type anisotropic superconductivity can be explored experimentally.
Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications

This paper studies stochastic optimization for a sum of compositional functions, where the inner-level function of each summand is coupled with the corresponding summation index. We refer to this family of problems as finite-sum coupled compositional optimization (FCCO). It has broad applications in machine learning for optimizing non-convex or convex compositional measures/objectives such as average precision (AP), p-norm push, listwise ranking losses, neighborhood component analysis (NCA), deep survival analysis, deep latent variable models, etc., which deserves finer analysis. Yet, existing algorithms and analyses are restricted in one or other aspects. The contribution of this paper is to provide a comprehensive convergence analysis of a simple stochastic algorithm for both non-convex and convex objectives. Our key result is the improved oracle complexity with the parallel speed-up by using the moving-average based estimator with mini-batching. Our theoretical analysis also exhibits new insights for improving the practical implementation by sampling the batches of equal size for the outer and inner levels. Numerical experiments on AP maximization, NCA, and p-norm push corroborate some aspects of the theory.
IPC-GraspSim: Reducing the Sim2Real Gap for Parallel-Jaw Grasping with the Incremental Potential Contact Model

Accurately simulating whether an object will be lifted securely or dropped during grasping is a longstanding Sim2Real challenge. Soft compliant jaw tips are almost universally used with parallel-jaw robot grippers due to their ability to increase contact area and friction between the jaws and the object to be manipulated. However, interactions between the compliant surfaces and rigid objects are notoriously difficult to model. We introduce IPC-GraspSim, a novel grasp simulator that extends Incremental Potential Contact (IPC) - a highly accurate collision + deformation model developed in 2020 for computer graphics. IPC-GraspSim models both the dynamics and the deformation of compliant jaw tips to reduce Sim2Real gap for robot grasping. We evaluate IPC-GraspSim using a set of 2,000 physical grasps across 16 adversarial objects where analytic models perform poorly. In comparison to both analytic quasistatic contact models (soft point contact, REACH, 6DFC) and dynamic grasp simulators (Isaac Gym with FleX), results suggest IPC-GraspSim can predict robustness with higher precision and recall (F1 = 0.85). IPC-GraspSim increases F1 score by 0.03 to 0.20 over analytic baselines and 0.09 over Isaac Gym, at a cost of 8000x and 1.5x more compute time, respectively. All data, code, videos, and supplementary material are available at https://sites.google. com/berkeley.edu/ipcgraspsim.
Generalized hyper-Ramsey-Bordé matter-wave interferometry: Quantum engineering of robust atomic sensors with composite pulses

A new class of atomic interferences using ultra-narrow optical transitions are pushing quantum engineering control to a very high level of precision for a next generation of sensors and quantum gate operations. In such context, we propose a new quantum engineering approach to Ramsey-Bord\'e interferometry introducing multiple composite laser pulses with tailored pulse duration, Rabi field amplitude, frequency detuning and laser phase-step. We explore quantum metrology with hyper-Ramsey and hyper-Hahn-Ramsey clocks below the $10^-18$ level of fractional accuracy by a fine tuning control of light excitation parameters leading to spinor interferences protected against light-shift coupled to laser-probe field variation. We review cooperative composite pulse protocols to generate robust Ramsey-Bord\'e, Mach-Zehnder and double-loop atomic sensors shielded against measurement distortion related to Doppler-shifts and light-shifts coupled to pulse area errors. Fault-tolerant auto-balanced hyper-interferometers are introduced eliminating several technical laser pulse defects that can occur during the entire probing interrogation protocol. Quantum sensors with composite pulses and ultra-cold atomic sources should offer a new level of high accuracy in detection of acceleration and rotation inducing phase-shifts, a strong improvement in tests of fundamental physics with hyper-clocks while paving the way to a new conception of atomic interferometers tracking space-time gravitational waves with a very high sensitivity.
Identification and functional characterization of multiple inositol polyphosphate phosphatase1 (Minpp1) isoform-2 in exosomes with potential to modulate tumor microenvironment

Inositol polyphosphates (InsPs) play key signaling roles in diverse cellular functions, including calcium homeostasis, cell survival and death. Multiple inositol polyphosphate phosphatase 1 (Minpp1) affects the cellular levels of InsPs and cell functions. The Minpp1 is an endoplasmic reticulum (ER) resident but localizes away from its cytosolic InsPs substrates. The current study examines the heterogeneity of Minpp1 and the potential physiologic impact of Minpp1 isoforms, distinct motifs, subcellular distribution, and enzymatic potential. The NCBI database was used to analyze the proteome diversity of Minpp1 using bioinformatics tools. The analysis revealed that translation of three different Minpp1 variants resulted in three isoforms of Minpp1 of varying molecular weights. A link between the minpp1 variant-2 gene and ER-stress, using real-time PCR, suggests a functional similarity between minpp1 variant-1 and variant-2. A detailed study on motifs revealed Minpp1 isoform-2 is the only other isoform, besides isoform-1, that carries a phosphatase motif for InsPs hydrolysis but no ER-retention signal. The confocal microscopy revealed that the Minpp1 isoform-1 predominantly localized near the nucleus with a GRP-78 ER marker, while Minpp1 isoform-2 was scattered more towards the cell periphery where it co-localizes with the plasma membrane-destined multivesicular bodies biomarker CD63. MCF-7 cells were used to establish that Minpp1 isoform-2 is secreted into exosomes. Brefeldin A treatment resulted in overexpression of the exosome-associated Minpp1 isoform-2, suggesting its secretion via an unconventional route involving endocytic-generated vesicles and a link to ER stress. Results further demonstrated that the exosome-associated Minpp1 isoform-2 was enzymatically active. Overall, the data support the possibility that an extracellular form of enzymatically active Minpp1 isoform-2 mitigates any anti-proliferative actions of extracellular InsPs, thereby also impacting the makeup of the tumor microenvironment.
Symptom clusters in chronic kidney disease and their association with people’s ability to perform usual activities

Background People living with a long-term condition, such as chronic kidney disease (CKD), often suffer from multiple symptoms simultaneously, making symptom management challenging. This study aimed to identify symptom clusters in adults with CKD across treatment groups and investigate their association with people’s ability to perform their usual activities. Methods We conducted a secondary analysis of both cross-sectional and longitudinal data collected as part of a national service improvement programme in 14 kidney centres in England, UK. This data included symptom severity (17 items, POS-S Renal) and the extent to which people had problems performing their usual activities (single item, EQ-5D-5L). We categorised data by treatment group: haemodialysis (n = 1,462), transplantation (n = 866), peritoneal dialysis (n = 127), or CKD without kidney replacement therapy (CKD non-KRT; n = 684). We used principal component analysis to identify symptom clusters per treatment group, and proportional odds models to assess the association between clusters and usual activities. Results Overall, clusters related to: lack of energy and mobility; gastrointestinal; skin; and mental health. Across groups, the ‘lack of energy and mobility’ clusters were associated with having problems with usual activities, with odds ratios (OR) ranging between 1.24 (95% confidence interval [CI], 1.21–1.57) for haemodialysis and 1.56 for peritoneal dialysis (95% CI, 1.28–1.90). This association was confirmed longitudinally in haemodialysis (n = 399) and transplant (n = 249) subgroups. Implications Our findings suggest that healthcare professionals should consider routinely assessing symptoms in the ‘lack of energy & mobility’ cluster in all people with CKD, regardless of whether they volunteer this information; not addressing these symptoms is likely to be related to them having problems with performing usual activities. Future studies should explore why symptoms within clusters commonly co-occur and how they interrelate. This will inform the development of cluster-level symptom management interventions with enhanced potential to improve outcomes for people with CKD.
A time-series meta-transcriptomic analysis reveals the seasonal, host, and gender structure of mosquito viromes

Abstract Although metagenomic sequencing has revealed high numbers of viruses in mosquitoes sampled globally, our understanding of how their diversity and abundance varies in time and space as well as by host species and gender remains unclear. To address this, we collected 23,109 mosquitoes over the course of 12 months from a bat-dwelling cave and a nearby village in Yunnan province, China. These samples were organized by mosquito species, mosquito gender, and sampling time for meta-transcriptomic sequencing. A total of 162 eukaryotic virus species were identified, of which 101 were novel, including representatives of seventeen RNA virus multi-family supergroups and four species of DNA virus from the families Parvoviridae, Circoviridae, and Nudiviridae. In addition, two known vector-borne viruses—Japanese encephalitis virus and Banna virus—were found. Analyses of the entire virome revealed strikingly different viral compositions and abundance levels in warmer compared to colder months, a strong host structure at the level of mosquito species, and no substantial differences between those viruses harbored by male and female mosquitoes. At the scale of individual viruses, some were found to be ubiquitous throughout the year and across four mosquito species, while most of the other viruses were season and/or host specific. Collectively, this study reveals the diversity, dynamics, and evolution of the mosquito virome at a single location and sheds new lights on the ecology of these important vector animals.
The estimated benefits of increasing cigarette prices through taxation on the burden of disease and economic burden of smoking in Nigeria: A modeling study

Background Globally, tobacco consumption continues to cause a considerable burden of preventable diseases. Although the smoking prevalence in Nigeria may be declining over the last years, the absolute number of active smokers remains one of the highest in Africa. Little is known about the disease burden and economic costs of cigarette smoking in Nigeria. Consequently, there is an evidence gap to inform the design and implementation of an effective policy for tobacco control. Methods We applied a microsimulation model to estimate the burden attributable to smoking in terms of morbidity, mortality, disability-adjusted life-years (DALYs), and direct medical costs and indirect costs (e.g., productivity loss costs, informal caregivers’ costs). We also modeled the health and economic impact of different scenarios of tobacco price increases through taxes. Results We estimated that smoking is responsible for approximately 29,000 annual deaths in Nigeria. This burden corresponds to 816,230 DALYs per year. In 2019, the total economic burden attributable to tobacco was estimated at ₦ 634 billion annually (approximately U$D 2.07 billion). If tobacco cigarettes’ prices were to be raised by 50% through taxes, more than 30,000 deaths from smoking-attributable diseases would be averted in 10 years, with subsequent savings on direct and indirect costs of ₦597 billion and increased tax revenue collection of ₦369 billion. Conclusion In Nigeria, tobacco is responsible for substantial health and economic burden. Increasing tobacco taxes could reduce this burden and produce net economic benefits.
Anchoring in the past, tweeting from the present: Cognitive bias in journalists’ word choices

This study examines journalists’ language in their reporting and what their word choices reveal about their cognitive mindsets. Reporters on the campaign trail often cannot afford to engage in systematic information processing as they distill complex political situations under deadline pressures. Twitter’s emphasis on speed and informal cultural milieu can further lead journalists to rely on heuristics and emotions. Drawing upon insights from theories of the mind, memory, and language, this study explores how cognitive biases are embodied in journalistic work across different media. We built a large-scale dataset of text corpora that consisted of more than 220,000 news articles, broadcast transcripts, and tweets generated over a year by 73 campaign reporters in the 2016 U.S. presidential election. Leveraging this unique dataset of journalistic outputs from a campaign season, we conducted automated text analyses. Results suggest that heuristics and intuitive thinking played a significant role in the generation of content on Twitter. Journalists infused their tweets with more emotion, compared to when they appeared in traditional media such as newspapers and broadcasts. Journalists’ tweets contained fewer words related to analytical and long-term thinking than their writing. Journalists also used informal language in their tweets to connect with their audiences in more personal and casual manners. Across all media examined in the study, journalists described the current race by drawing upon their experience of covering prior presidential elections, a form of anchoring heuristic. This study extends the use of cognitive biases in politics to a new realm, reporting, and shows how journalists’ use of language on the campaign trail reflects cognitive biases that arise when individuals make decisions under time pressure and uncertainty.
Paediatric intracranial dural arteriovenous shunts: types, clinical presentation and therapeutic management

Abstract Paediatric intracranial dural arteriovenous shunts have clinical presentations and evolutions, with angiographic characteristics that differ from those described in adults. We report our experience concerning their therapeutic management, emphasizing the relevance of early diagnosis and appropriate treatment for satisfactory neurocognitive development. Using a prospective database, we reviewed the clinical and radiological data of all children with dural arteriovenous shunts managed between 2002 and 2020. Dural shunts were categorized into three types: dural sinus malformations with arteriovenous shunts; infantile dural arteriovenous shunts; and adult-type dural arteriovenous shunts. Therapeutic strategies and outcomes were analysed depending on lesional subtypes. Modified Rankin Scale for the paediatric population was assessed pre-treatment and at last follow-up. Twenty-eight patients [16 girls (57.1%); 12 boys (42.9%)] were included: 17 dural sinus malformation [10 boys (58.8%); seven girls (41.2%)], three infantile shunts [three girls (100%)], eight adult-type shunts [four girls (50%)]; four boys (50%)], with a mean age of 19.2 ± 36.6 months at presentation. Twelve (42.9%) had a modified Rankin Scale score of 0–2, four (14.3%) had a score of 3, three (10.7%) had a score of 4 and eight (28.6%) had a score of 5. Embolization was performed in 22 children [78.6%; 12 girls (54.5%); 10 boys (45.5%)]. Fifteen patients could be cured (68.2%): 11 dural sinus malformations (73.3%), four adult-type lesions (100%) but no infantile shunt. Mean post-treatment follow-up was 39.5 months (max. 139 months): 14 patients (63.6%) presented a modified Rankin Scale score of 0–2 and eight (36.4%) had a score ≥3. In the dural sinus malformation group, the modified Rankin Scale score was improved in 11 patients (73.3%) and unchanged in three (20%). Only one patient with infantile subtype (33.3%) improved clinically. In the adult-subtype group, all children (100%) improved. Of six untreated patients [four girls (66.7%); two boys (33.3%)], four with adult-subtype shunts showed uneventful evolutions, one with dural sinus malformation died, and therapeutic abortion was conducted in an antenatally diagnosed dural sinus malformation. Paediatric dural fistulas comprise different subtypes with variable clinical courses. Proper diagnosis is mandatory for optimal therapeutic strategies within appropriate therapeutic windows.