Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional Linear Regression of Cumulative Distribution Functions

The estimation of cumulative distribution functions (CDF) is an important learning task with a great variety of downstream applications, such as risk assessments in predictions and decision making. In this paper, we study functional regression of contextual CDFs where each data point is sampled from a linear combination of context dependent CDF basis functions. We propose functional ridge-regression-based estimation methods that estimate CDFs accurately everywhere. In particular, given $n$ samples with $d$ basis functions, we show estimation error upper bounds of $\widetilde O(\sqrt{d/n})$ for fixed design, random design, and adversarial context cases. We also derive matching information theoretic lower bounds, establishing minimax optimality for CDF functional regression. Furthermore, we remove the burn-in time in the random design setting using an alternative penalized estimator. Then, we consider agnostic settings where there is a mismatch in the data generation process. We characterize the error of the proposed estimators in terms of the mismatched error, and show that the estimators are well-behaved under model mismatch. Moreover, to complete our study, we formalize infinite dimensional models where the parameter space is an infinite dimensional Hilbert space, and establish a self-normalized estimation error upper bound for this setting. Notably, the upper bound reduces to the $\widetilde O(\sqrt{d/n})$ bound when the parameter space is constrained to be $d$-dimensional. Our comprehensive numerical experiments validate the efficacy of our estimation methods in both synthetic and practical settings.
On Characterizing the Trade-off in Invariant Representation Learning

Many applications of representation learning, such as privacy preservation, algorithmic fairness, and domain adaptation, desire explicit control over semantic information being discarded. This goal is formulated as satisfying two objectives: maximizing utility for predicting a target attribute while simultaneously being invariant (independent) to a known semantic attribute. Solutions to invariant representation learning (IRepL) problems lead to a trade-off between utility and invariance when they are competing. While existing works study bounds on this trade-off, two questions remain outstanding: 1) What is the exact trade-off between utility and invariance? and 2) What are the encoders (mapping the data to a representation) that achieve the trade-off, and how can we estimate it from training data? This paper addresses these questions for IRepLs in reproducing kernel Hilbert spaces (RKHS)s. Under the assumption that the distribution of a low-dimensional projection of high-dimensional data is approximately normal, we derive a closed-form solution for the global optima of the underlying optimization problem for encoders in RKHSs. This yields closed formulae for a near-optimal trade-off, corresponding optimal representation dimensionality, and the corresponding encoder(s). We also numerically quantify the trade-off on representative problems and compare them to those achieved by baseline IRepL algorithms.
Monte Carlo calculation of the surface tension for two- and three-dimensional lattice-gas models

It is suggested that the interface free energy between bulk phases with a macroscopically flat interface can be estimated from the variation of certain probability distribution functions of finite blocks with block size. For a liquid-gas system the probability distribution of the density would have to be used. The method is particularly suitable for the critical region where other methods are hard to apply. As a test case, the two-dimensional lattice-gas model is treated and it is shown that already, from rather small blocks, one obtains results consistent with the exact soluion of Onsager for the surface tension, by performing appropriate extrapolations. The surface tension of the three-dimensional lattice-gas model is also estimated and found to be reasonably consistent with the expected critical behavior. The universal amplitude of the surface tension of fluids near their critical point is estimated and shown to be in significantly better agreement with experimental data than the results of Fisk and Widom and the first-order 4-d renormalization-group expansion. Also the universal amplitude ratio used in nucleation theory near the critical point is estimated.
Generalized probability weighted moments: Application to the generalized Pareto Distribution

Probability weighted moments (PWMs) are widely used in hydrology for estimating parameters of flood distributions. The classical PWM approach considers moments of the type E[XFj] (or, alternatively, E[X(1 − F)k]), where j (or k) takes values 0, 1, or 2 depending on the number of parameters to be estimated. The classical approach is here compared with an extended class of PWMs that does not restrict j or k to be small nonnegative integers. Estimation based on the extended class of PWMs is named the generalized method of PWMs to distinguish it from the classical procedure. To illustrate the method, we consider estimation of quantiles in the generalized Pareto distribution and demonstrate that substantial gain in estimation accuracy can be obtained by using generalized PWMs.
Genetic status and conservation implications of endangered Formosan black bears

Abstract: The Formosan black bear (Ursus thibetanus formosanus, FBB) is an endangered subspecies of Asiatic black bear, geographically isolated in Taiwan from 6 other subspecies on the Asian continent and in Japan. Data on the genetic diversity and structure of wild FBB are lacking, though 2 potential hotspots for FBB occurrence have been identified; Yushan National Park (YNP) and Dasyueshan National Forest Recreation Area (DSY). Elucidating species' genetic structure can help assess the potential risks of population fragmentation and isolation that threaten FBB. Here, we employed 8 FBB-derived microsatellite loci to genotype 139 individuals from fecal samples collected in YNP, 20 captured individuals from both study areas, and 1 fecal sample from DSY, during 2009–2019. The marker set featured high polymorphic information content (mean > 0.699) and appropriate probability of identity (combined P(ID) < 0.0001) for individual identification. We detected high heterozygosity and no evidence of inbreeding in YNP, but the 2 subpopulations presented significant genetic differentiation. All DSY samples were assigned to one genetic cluster or phylogenetic clade, indicating that the DSY subpopulation is monophyletic. However, members of the YNP subpopulation were allocated to diverse lineages, and some YNP samples were partly assigned to the same cluster or clade as DSY individuals. We discuss potential evolutionary scenarios in which the observed population genetic structuring may have evolved. Based on our genetic results, the DSY and YNP subpopulations should be considered 2 separate management units and gene flow between both parts should be promoted.
How do seasonal changes in adult wolf defecation patterns affect scat detection probabilities?

Abstract. Wolves are currently recolonising their historic range in France. The collection of scats is a widely used a non-invasive survey method to monitor wolf population size. However, seasonal changes in wolf faecal deposition patterns might affect the results of surveys. We used a detection dog and camera trapping (CT) to compare wolf scat detectability during winter and the nursing season. We collected 113 scats deposited by adult wolves at 29 marking sites on forest roads in the Sainte-Baume Regional Park, Provence, France. After parturition, the mean number of adult wolf scats increased by 160% inside the nursing territory and decreased by 80% outside of it. Around the time the pups are born, changes in faecal deposition patterns of adults make it easier to find scats around the wolf den (87% probability per wolf marking site) and harder to find scats outside the nursing territory (11% probability). During winter, the chance to find scats is equal (38 to 40% probability per wolf marking site) inside vs. outside the nursing territory. The combined use of a detection dog and camera traps allowed us to gather data on wolf defecation patterns non-invasively. Detectability of adult wolf scats during the nursing season is highly variable compared to winter due to seasonal behavioural changes affecting scat location. We conclude that surveys to collect samples and estimate wolf population size should be conducted exclusively during winter to avoid sampling biases.
Transcriptomic Signatures of Human Immunodeficiency Virus Post-Treatment Control

The findings presented here provide new insights into how HIV and cellular gene expression change after stopping ART in both noncontrollers and posttreatment controllers. Posttreatment control is associated with an early ability to limit increases in multiply-spliced HIV RNA, a delayed (and presumably immune-mediated) ability to reverse an initial rise in processive/completed HIV transcripts, and multiple differences in cellular gene expression pathways. ABSTRACT Posttreatment controllers (PTCs) are rare HIV-infected individuals who can limit viral rebound after antiretroviral therapy interruption (ATI), but the mechanisms of this remain unclear. To investigate these mechanisms, we quantified various HIV RNA transcripts (via reverse transcription droplet digital PCR [RT-ddPCR]) and cellular transcriptomes (via RNA-seq) in blood cells from PTCs and noncontrollers (NCs) before and two time points after ATI. HIV transcription initiation did not significantly increase after ATI in PTCs or in NCs, whereas completed HIV transcripts increased at early ATI in both groups and multiply-spliced HIV transcripts increased only in NCs. Compared to NCs, PTCs showed lower levels of HIV DNA, more cell-associated HIV transcripts per total RNA at all times, no increase in multiply-spliced HIV RNA at early or late ATI, and a reduction in the ratio of completed/elongated HIV RNA after early ATI. NCs expressed higher levels of the IL-7 pathway before ATI and expressed higher levels of multiple cytokine, inflammation, HIV transcription, and cell death pathways after ATI. Compared to the baseline, the NCs upregulated interferon and cytokine (especially TNF) pathways during early and late ATI, whereas PTCs upregulated interferon and p53 pathways only at early ATI and downregulated gene translation during early and late ATI. In NCs, viral rebound after ATI is associated with increases in HIV transcriptional completion and splicing, rather than initiation. Differences in HIV and cellular transcription may contribute to posttreatment control, including an early limitation of spliced HIV RNA, a delayed reduction in completed HIV transcripts, and the differential expression of the IL-7, p53, and TNF pathways. IMPORTANCE The findings presented here provide new insights into how HIV and cellular gene expression change after stopping ART in both noncontrollers and posttreatment controllers. Posttreatment control is associated with an early ability to limit increases in multiply-spliced HIV RNA, a delayed (and presumably immune-mediated) ability to reverse an initial rise in processive/completed HIV transcripts, and multiple differences in cellular gene expression pathways. These differences may represent correlates or mechanisms of posttreatment control and may provide insight into the development and/or monitoring of therapeutic strategies that are aimed at a functional HIV cure.
Antigenic Characterization of Human Monoclonal Antibodies for Therapeutic Use against H7N9 Avian Influenza Virus

Avian influenza virus (AIV) H7N9 continues to circulate and evolve in birds, posing a credible threat to humans. Antiviral drugs have proven useful for the treatment of severe influenza infections in humans; however, concerns have been raised as antiviral-resistant mutants have emerged. ABSTRACT Since 2013, H7N9 avian influenza viruses (AIVs) have caused more than 1,500 human infections and the culling of millions of poultry. Despite large-scale poultry vaccination, H7N9 AIVs continue to circulate among poultry in China and pose a threat to human health. Previously, we isolated and generated four monoclonal antibodies (mAbs) derived from humans naturally infected with H7N9 AIV. Here, we investigated the hemagglutinin (HA) epitopes of H7N9 AIV targeted by these mAbs (L3A-44, K9B-122, L4A-14, and L4B-18) using immune escape studies. Our results revealed four key antigenic epitopes at HA amino acid positions 125, 133, 149, and 217. The mutant H7N9 viruses representing escape mutations containing an alanine-to-threonine substitution at residue 125 (A125T), a glycine-to-glutamic acid substitution at residue 133 (G133E), an asparagine-to-aspartic acid substitution at residue 149 (N149D), or a leucine-to-glutamine substitution at residue 217 (L217Q) showed reduced or completely abolished cross-reactivity with the mAbs, as measured by a hemagglutination inhibition (HI) assay. We further assessed the potential risk of these mutants to humans should they emerge following mAb treatment by measuring the impact of these HA mutations on virus fitness and evasion of host adaptive immunity. Here, we showed that the L4A-14 mAb had broad neutralizing capabilities, and its escape mutant N149D had reduced viral stability and human receptor binding and could be neutralized by both postinfection and antigen-induced sera. Therefore, the L4A-14 mAb could be a therapeutic candidate for H7N9 AIV infection in humans and warrants further investigation for therapeutic applications. IMPORTANCE Avian influenza virus (AIV) H7N9 continues to circulate and evolve in birds, posing a credible threat to humans. Antiviral drugs have proven useful for the treatment of severe influenza infections in humans; however, concerns have been raised as antiviral-resistant mutants have emerged. Monoclonal antibodies (mAbs) have been studied for both prophylactic and therapeutic applications in infectious disease control and have demonstrated great potential. For example, mAb treatment has significantly reduced the risk of people developing severe disease with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. In addition to the protection efficiency, we should also consider the potential risk of the escape mutants generated by mAb treatment to public health by assessing their viral fitness and potential to compromise host adaptive immunity. Considering these parameters, we assessed four human mAbs derived from humans naturally infected with H7N9 AIV and showed that the mAb L4A-14 displayed potential as a therapeutic candidate.
Enabling cumulative learning in user-oriented research for root, tuber and banana crop breeding

Summary User-oriented research is important in breeding improved genotypes, for developing and validating product concepts (mostly involving trait prioritisation), as well as evaluating breeding products in use situations (mostly involving participatory variety evaluation). This paper examines key aspects that enable cumulative learning in user-oriented research for root, tuber and banana (RTB) crop breeding. We reviewed empirical user-oriented studies on RTB crops published between 1996 and 2020. We examined the ability of user-oriented research to foster cumulative learning by examining four key aspects: spatial and temporal coverage; gender aspects; the range of traits considered and publishing practices as evident in reports and datasets. We conclude that user-oriented studies have received attention in RTB breeding but fall short of enabling cumulative learning. Substantial investments in methodology development and capacity are needed to bring greater coherence to this field and enable cumulative learning about user perspectives to iteratively increase the fit between improved genotypes and user preferences.
“Am I not human?”: Reasserting humanness in response to group-based dehumanization

Research on group dehumanization has focused largely on the perpetrators of dehumanization or on its negative emotional and cognitive effects on targets. We theorized that people would also reassert their humanness in response to dehumanizing portrayals of their group. Experiment 1 showed that Black individuals responded to a dehumanizing representation of their racial group by emphasizing their experience of more complex, uniquely human emotions versus emotions more associated with other animals. Experiment 2 and a supplemental experiment showed that Black, but not White, individuals responded to group-based dehumanization by depicting more complex self-portrayals. Taken together, these studies begin to illustrate that targets of group-based dehumanization are not simply passive victims but respond actively, resisting negative representations of their group by reasserting their humanness.
The tethered humanity hypothesis among victims of interpersonal harm: The role of apologies, forgiveness, and the relation between self-, other-, and meta-perceptions of humanity

When interpersonal harm is inflicted, victims stop seeing themselves as fully human. The tethered humanity hypothesis proposes that victims restore a full human status when perpetrators undertake attempts at reconciliation and victims manage to reestablish the humanness of their perpetrators. In two studies, we tested this hypothesis and manipulated the perpetrators attempts at apologizing for their misconduct. Participants were either included or socially excluded and received a full or self-exonerating apology or a hostile message when they were excluded. Results indicated that victims dehumanized themselves and their ostracizers when they were socially excluded and managed to regain a full human status and rehumanized their perpetrators when a full apology was uttered. Moreover, regression analyses indicated that different humanness judgments (self, other, and meta-humanness) become tethered only when perpetrators apologized, while forgiving the perpetrator always correlated with the rehumanization of the self regardless of the perpetrator’s apology.
Disparate Impacts of Performance Funding Research Incentives on Research Expenditures and State Appropriations

Performance-based funding (PBF) policies with research incentives have grown in popularity over the years despite little understanding regarding whether they actually work. This study leverages a novel national data set to examine the impact of PBF research incentives on the research expenditures and total state appropriations among public 4-year institutions, with a particular focus on minority-serving institutions. We find that PBF research incentives had no impact on the total amount or relative share of total research expenditures or the total amount of state appropriations allocated to treated institutions, regardless of minority-serving institution status. PBF policies that allowed institutions to self-select or opt into including research incentives as part of their PBF formula had a positive impact on the relative share of expenditures allocated to research and total state appropriations.
Why are Graph Neural Networks Effective for EDA Problems?

In this paper, we discuss the source of effectiveness of Graph Neural Networks (GNNs) in EDA, particularly in the VLSI design automation domain. We argue that the effectiveness comes from the fact that GNNs implicitly embed the prior knowledge and inductive biases associated with given VLSI tasks, which is one of the three approaches to make a learning algorithm physics-informed. These inductive biases are different to those common used in GNNs designed for other structured data, such as social networks and citation networks. We will illustrate this principle with several recent GNN examples in the VLSI domain, including predictive tasks such as switching activity prediction, timing prediction, parasitics prediction, layout symmetry prediction, as well as optimization tasks such as gate sizing and macro and cell transistor placement. We will also discuss the challenges of applications of GNN and the opportunity of applying self-supervised learning techniques with GNN for VLSI optimization.
2022 ICCAD CAD Contest Problem B: 3D Placement with D2D Vertical Connections

In the chiplet era, the benefits from multiple factors can be observed by splitting a large single die into multiple small dies. By having the multiple small dies with die-to-die (D2D) vertical connections, the benefits including: 1) better yield, 2) better timing/performance, and 3) better cost. How to do the netlist partitioning, cell placement in each of the small dies, and also how to determine the location of the D2D inter-connection terminals becomes a new topic.To address this chiplet era physical implementation problem, ICCAD-2022 contest encourages the research in the techniques of multi-die netlist partitioning and placement with D2D vertical connections. We provided (i) a set of benchmarks and (ii) an evaluation metric that facilitate contestants to develop, test, and evaluate their new algorithms.
Language Equation Solving via Boolean Automata Manipulation

Language equations are a powerful tool for compositional synthesis, modeled as the unknown component problem. Given a (sequential) system specification S and a fixed component F, we are asked to synthesize an unknown component X such that whose composition with F fulfills S. The synthesis of X can be formulated with language equation solving. Although prior work exploits partitioned representation for effective finite automata manipulation, it remains challenging to solve language equations involving a large number of states. In this work, we propose variants of Boolean automata as the underlying succinct representation for regular languages. They admit logic circuit manipulation and extend the scalability for solving language equations. Experimental results demonstrate the superiority of our method to the state-of-the-art in solving nine more cases out of the 36 studied benchmarks and achieving an average of 740× speedup.
Glign: Taming Misaligned Graph Traversals in Concurrent Graph Processing

In concurrent graph processing, different queries are evaluated on the same graph simultaneously, sharing the graph accesses via the memory hierarchy. However, different queries may traverse the graph differently, especially for those starting from different source vertices. When these graph traversals are ”misaligned”, the benefits of graph access sharing can be seriously compromised. As more concurrent queries are added to the evaluation batch, the issue tends to become even worse. To address the above issue, this work introduces Glign, a runtime system that automatically aligns the graph traversals for concurrent queries. Glign introduces three levels of graph traversal alignment for iterative evaluation of concurrent queries. First, it synchronizes the accesses of different queries to the active parts of the graph within each iteration of the evaluation—intra-iteration alignment. On top of that, Glign leverages a key insight regarding the “heavy iterations” in query evaluation to achieve inter-iteration alignment and alignment-aware batching. The former aligns the iterations of different queries to increase the graph access sharing, while the latter tries to group queries of better graph access sharing into the same evaluation batch. Together, these alignment techniques can substantially boost the data locality of concurrent query evaluation. Based on our experiments, Glign outperforms the state-of-the-art concurrent graph processing systems Krill and GraphM by 3.6× and 4.7× on average, respectively.