Miracle-dz/UniFAR-FTUs huggingface dataset:
Unsupervised Distance Metric Learning Using Predictability

Distance-based learning methods, like clustering and SVMs, are dependent on good distance metrics. This paper does unsupervised metric learning in the context of clustering. We seek transformations of data which give clean and well separated clusters where clean clusters are those for which membership can be accurately predicted. The transformation (hence distance metric) is obtained by minimizing the blur ratio, which is defined as the ratio of the within cluster variance divided by the total data variance in the transformed space. For minimization we propose an iterative procedure, Clustering Predictions of Cluster Membership (CPCM). CPCM alternately (a) predicts cluster memberships (e.g., using linear regression) and (b) clusters these predictions (e.g., using k-means). With linear regression and k-means, this algorithm is guaranteed to converge to a fixed point. The resulting clusters are invariant to linear transformations of original features, and tend to eliminate noise features by driving their weights to zero. Comments University of Pennsylvania Department of Computer and Information Science Technical Report No. MSCIS-08-23. This technical report is available at ScholarlyCommons: http://repository.upenn. edu/cis_reports/885 Unsupervised distance metric learning using predictability Abhishek A. Gupta Department of Statistics University of Pennsylvania abgupta@wharton.upenn. edu Dean P. Foster Department of Statistics University of Pennsylvania foster@wharton.upenn. edu Lyle H. Ungar Department of Computer and Information Science University of Pennsylvania ungar@cis.upenn. edu
Modeling DVFS and Power-Gating Actuators for Cycle-Accurate NoC-Based Simulators

Networks-on-chip (NoCs) are a widely recognized viable interconnection paradigm to support the multi-core revolution. One of the major design issues of multicore architectures is still the power, which can no longer be considered mainly due to the cores, since the NoC contribution to the overall energy budget is relevant. To face both static and dynamic power while balancing NoC performance, different actuators have been exploited in literature, mainly dynamic voltage frequency scaling (DVFS) and power gating. Typically, simulation-based tools are employed to explore the huge design space by adopting simplified models of the components. As a consequence, the majority of state-of-the-art on NoC power-performance optimization do not accurately consider timing and power overheads of actuators, or (even worse) do not consider them at all, with the risk of overestimating the benefits of the proposed methodologies. This article presents a simulation framework for power-performance analysis of multicore architectures with specific focus on the NoC. It integrates accurate power gating and DVFS models encompassing also their timing and power overheads. The value added of our proposal is manyfold: (i) DVFS and power gating actuators are modeled starting from SPICE-level simulations; (ii) such models have been integrated in the simulation environment; (iii) policy analysis support is plugged into the framework to enable assessment of different policies; (iv) a flexible GALS (globally asynchronous locally synchronous) support is provided, covering both handshake and FIFO re-synchronization schemas. To demonstrate both the flexibility and extensibility of our proposal, two simple policies exploiting the modeled actuators are discussed in the article.
Topicalization and Truth Conditions: A Categorial Account

In this paper we will consider how the choice between the topic marker wa and nominative marker for subjects affects truth conditions of sentences. We derive the proper interpretations for sentences with topics and nominative subjects in terms of syntaxsemantics interface. The categorial framework adopted here allows us to account for significant difference in meaning with respect to the choice of markers for subjects while maintaining the principle of strong compositionality.
Tunable protein synthesis by transcript isoforms in human cells

Eukaryotic genes generate multiple mRNA transcript isoforms though alternative transcription, splicing, and polyadenylation. However, the relationship between human transcript diversity and protein production is complex and not well understood at a global level. We fractionated a polysome profile and reconstructed transcript isoforms from each fraction, which we term Transcript Isoforms in Polysomes sequencing (TrIP-seq). Analysis of these data revealed regulatory features that control ribosome occupancy and the resulting translational output of each transcript isoform. We extracted a panel of 5′ and 3′ untranslated regions that control protein production from an unrelated gene in cells over a 100-fold range. Select 5′ untranslated regions exert robust translational control between cell lines, while 3′ untranslated regions can confer cell-type-specific expression. These results expose the large dynamic range of transcript-isoform-specific translational control, identify isoform-specific sequences that control protein output in human cells, and demonstrate that transcript isoform diversity must be considered when relating RNA and protein levels.
Compilation Based Approaches to Probabilistic Planning - Thesis Summary

The main focus of our work is the use of classical planning algorithms in service of more complex problems of planning under uncertainty. In particular, we are exploring compilation techniques that allow us to reduce some probabilistic planning problems into variants of classical planning, such as metric planning,resource-bounded planning, and cost-bounded suboptimal planning. Currently, our initial work focuses on \emph{conformant probabilistic planning}. We intend toimprove our current methods by improving our compilation methods, but also by improving the ability of current planners to handle the special features ofour compiled problems. Then, we hope to extend these techniques to handle more complex probabilistic settings, such as problems with stochastic actions andpartial observability.
Intelligence, Human Capital and HIV/AIDS: Fresh Exploration

Abstract This study complements existing literature on the relationship between HIV/AIDS and human capital by introducing previously unexplored indicators and more robust empirical strategies. The overarching purpose is to assess whether previous findings on the relationship withstand empirical scrutiny when alternative indicators and methodologies are employed. Four main HIV/AIDS measurements are regressed on intelligence for a maximum of 195 cross-sectional averages over the past decade. The empirical evidence is based on OLS, IWLS and 2SLS. The following findings are established. First, human capital decreases HIV prevalence, with a higher magnitude on ‘Women's share of population aged 15 and above living with HIV’. This implies improving average human capital levels across communities may be more beneficial to girls above the age of 15 living with HIV. The relatively similar negative magnitudes across other dependent variables implies that increasing human capital decreases deaths from HIV/AIDS by almost the same rate at which it reduces infections to the disease. Moreover, the HIV infection rate in children between the ages of 0 and 14 does not significantly change with improvements in human capital. More policy implications are discussed.
Foor-Compliance Assessments of Projects Adhering to Enterprise Architecture

This article examines how to assess projects, which implement business processes and IT systems, on compliance with an Enterprise Architecture (EA) that provides them with constraints and high-level solutions. The authors begin by presenting the core elements of EA compliance testing. Next, the authors discuss the testing process and four types of compliance checks (i
Do Perfluoroalkyl Compounds Impair Human Semen Quality?

Background Perfluoroalkyl acids (PFAAs) are found globally in wildlife and humans and are suspected to act as endocrine disruptors. There are no previous reports of PFAA levels in adult men from Denmark or of a possible association between semen quality and PFAA exposure. Objectives We investigated possible associations between PFAAs and testicular function. We hypothesized that higher PFAA levels would be associated with lower semen quality and lower testosterone levels. Methods We analyzed serum samples for levels of 10 different PFAAs and reproductive hormones and assessed semen quality in 105 Danish men from the general population (median age, 19 years). Results Considerable levels of perfluorooctane sulfonic acid (PFOS), perfluorooctanoic acid (PFOA), and perfluorohexane sulfonic acid were found in all young men (medians of 24.5, 4.9, and 6.6 ng/mL, respectively). Men with high combined levels of PFOS and PFOA had a median of 6.2 million normal spermatozoa in their ejaculate in contrast to 15.5 million among men with low PFOS–PFOA (p = 0.030). In addition, we found nonsignificant trends with regard to lower sperm concentration, lower total sperm counts, and altered pituitary–gonadal hormones among men with high PFOS–PFOA levels. Conclusion High PFAA levels were associated with fewer normal sperm. Thus, high levels of PFAAs may contribute to the otherwise unexplained low semen quality often seen in young men. However, our findings need to be corroborated in larger studies.
Energy-efficient dual array capacitive DAC and switch control for SAR ADC

A new switch control method for a capacitive DAC architecture has been presented. This has been implemented to make a successive approximation register (SAR) ADC more energy efficient. By splitting the capacitor array into two equal halves and using a unity gain buffer, the proposed architecture reduces the switching energy by 97 percent compared to the conventional switching method. The proposed method is analyzed theoretically and then simulations are performed in a 0. 18µm CMOS process to verify the theoretical analysis.
Dueling Bandits: Beyond Condorcet Winners to General Tournament Solutions

Recent work on deriving $O(\log T)$ anytime regret bounds for stochastic dueling bandit problems has considered mostly Condorcet winners, which do not always exist, and more recently, winners defined by the Copeland set, which do always exist. In this work, we consider a broad notion of winners defined by tournament solutions in social choice theory, which include the Copeland set as a special case but also include several other notions of winners such as the top cycle, uncovered set, and Banks set, and which, like the Copeland set, always exist. We develop a family of UCB-style dueling bandit algorithms for such general tournament solutions, and show $O(\log T)$ anytime regret bounds for them. Experiments confirm the ability of our algorithms to achieve low regret relative to the target winning set of interest.
ATGC database and ATGC-COGs: an updated resource for micro- and macro-evolutionary studies of prokaryotic genomes and protein family annotation

The Alignable Tight Genomic Clusters (ATGCs) database is a collection of closely related bacterial and archaeal genomes that provides several tools to aid research into evolutionary processes in the microbial world. Each ATGC is a taxonomy-independent cluster of 2 or more completely sequenced genomes that meet the objective criteria of a high degree of local gene order (synteny) and a small number of synonymous substitutions in the protein-coding genes. As such, each ATGC is suited for analysis of microevolutionary variations within a cohesive group of organisms (e.g. species), whereas the entire collection of ATGCs is useful for macroevolutionary studies. The ATGC database includes many forms of pre-computed data, in particular ATGC-COGs (Clusters of Orthologous Genes), multiple sequence alignments, a set of ‘index’ orthologs representing the most well-conserved members of each ATGC-COG, the phylogenetic tree of the organisms within each ATGC, etc. Although the ATGC database contains several million proteins from thousands of genomes organized into hundreds of clusters (roughly a 4-fold increase since the last version of the ATGC database), it is now built with completely automated methods and will be regularly updated following new releases of the NCBI RefSeq database. The ATGC database is hosted jointly at the University of Iowa at dmk-brain.ecn. uiowa.edu/ATGC/ and the NCBI at ftp.ncbi. nlm.nih. gov/pub/kristensen/ATGC/atgc_home. html.
Ca2+ entry into neurons is facilitated by cooperative gating of clustered CaV1.3 channels

CaV1.3 channels regulate excitability in many neurons. As is the case for all voltage-gated channels, it is widely assumed that individual CaV1.3 channels behave independently with respect to voltage-activation, open probability, and facilitation. Here, we report the results of super-resolution imaging, optogenetic, and electrophysiological measurements that refute this long-held view. We found that the short channel isoform (CaV1.3S), but not the long (CaV1.3L), associates in functional clusters of two or more channels that open cooperatively, facilitating Ca2+ influx. CaV1.3S channels are coupled via a C-terminus-to-C-terminus interaction that requires binding of the incoming Ca2+ to calmodulin (CaM) and subsequent binding of CaM to the pre-IQ domain of the channels. Physically-coupled channels facilitate Ca2+ currents as a consequence of their higher open probabilities, leading to increased firing rates in rat hippocampal neurons. We propose that cooperative gating of CaV1.3S channels represents a mechanism for the regulation of Ca2+ signaling and electrical activity. DOI: http://dx.doi. org/10.7554/eLife.15744. 001
A constructive review of in-network caching: A core functionality of ICN

Over the years, caching has been leveraged (and established) as an add-on functionality to enhance network performance. However, Information Centric Networking (ICN) conceives caching at network layer (i.e. beyond the premise of end-to-end principle) thereby making it one of the core functionalities. Further, ICN advocates named-content (another core functionality) that allows content-consciousness within networks. Together the two functionalities result in content-aware ubiquitous in-network caching that has received significant attention of researcher all-around. The aim of this paper is to probe in depth and review some of the work done pertaining to in-network caching in ICN, in order to understand their aims, assumptions, approaches, simulation set-ups used, network topologies exploited, traffic pattern fed-in, performance metrics used, parameter(s) tuned-in to optimize the performance and the significance of the results obtained by the researchers. The paper also lists out advantages of in-network caching, related issues, factors that affect in-network caching and relevant performance metrics. The paper intends to assist researchers who are searching ways to put-forward (an acceptable) proof of their ideas.
Up-Regulation of Adiponectin Expression in Antigravitational Soleus Muscle in Response to Unloading Followed by Reloading, and Functional Overloading in Mice

The purpose of this study was to investigate the expression level of adiponectin and its related molecules in hypertrophied and atrophied skeletal muscle in mice. The expression was also evaluated in C2C12 myoblasts and myotubes. Both mRNA and protein expression of adiponectin, mRNA expression of adiponectin receptor (AdipoR) 1 and AdipoR2, and protein expression of adaptor protein containing pleckstrin homology domain, phosphotyrosine binding domain, and leucine zipper motif 1 (APPL1) were observed in C2C12 myoblasts. The expression levels of these molecules in myotubes were higher than those in myoblasts. The expression of adiponectin-related molecules in soleus muscle was observed at mRNA (adiponectin, AdipoR1, AdipoR2) and protein (adiponectin, APPL1) levels. The protein expression levels of adiponectin and APPL1 were up-regulated by 3 weeks of functional overloading. Down-regulation of AdipoR1 mRNA, but not AdipoR2 mRNA, was observed in atrophied soleus muscle. The expression of adiponectin protein, AdipoR1 mRNA, and APPL1 protein was up-regulated during regrowth of unloading-associated atrophied soleus muscle. Mechanical loading, which could increase skeletal muscle mass, might be a useful stimulus for the up-regulations of adiponectin and its related molecules in skeletal muscle.
Orbits of families of vector fields on subcartesian spaces

Nous demontrons que les orbites d'un ensemble complet de champs de vecteurs sur des espaces sous-cartesiens sont des varietes differentielles. Ce resultat permet de decrire la structure de l'espace de phase reduite d'un systeme hamiltonien a l'aide de l'algebre de Poisson reduite. De plus, nous pouvons donner une description globale des structures geometriques de classe C∞sur une famille de varietes formant un feuilletage singulier d'un espace sous-cartesien, en fonction d'objets definis par l'ensemble des champs de vecteurs correspondants.
Analysing directed covert attention in Brain Computer Interfaces

One way of measuring brain activity is a method called EEG (electroencephalogram). This is a non-invasive procedure that depends on the electric currents generated inside the brain. These minute currents and in particular the differences in currents throughout the brain at a particular moment in time are good predictors of brain activity. A BCI experiment performed at the university of Berlin involved 8 healthy subjects, they were given the task to direct their attention to one of six specific locations. This particular location was indicated with a cue, after that moment the test subjects covertly directed their attention to the place where the cue had occurred. Important was that they had to keep their eyes fixed in the center of the screen. So even though a cue appeared, only their covert attention was focused to that location and not their eye-gaze[10]. This way, motoric actions (moving eyes) did not disrupt the brain signal related to covert attention.
Antigenic Fingerprinting following Primary RSV Infection in Young Children Identifies Novel Antigenic Sites and Reveals Unlinked Evolution of Human Antibody Repertoires to Fusion and Attachment Glycoproteins

Respiratory Syncytial Virus (RSV) is the major cause of pneumonia among infants. Here we elucidated the antibody repertoire following primary RSV infection and traced its evolution through adolescence and adulthood. Whole genome-fragment phage display libraries (GFPDL) expressing linear and conformational epitopes in the RSV fusion protein (F) and attachment protein (G) were used for unbiased epitope profiling of infant sera prior to and following RSV infection. F-GFPDL analyses demonstrated modest changes in the anti-F epitope repertoires post-RSV infection, while G-GFPDL analyses revealed 100-fold increase in number of bound phages. The G-reactive epitopes spanned the N- and C-terminus of the G ectodomain, along with increased reactivity to the central conserved domain (CCD). Panels of F and G antigenic sites were synthesized to evaluate sera from young children (<2 yr), adolescents (14–18 yr) and adults (30–45 yr) in SPR real-time kinetics assays. A steady increase in RSV-F epitope repertoires from young children to adults was observed using peptides and F proteins. Importantly, several novel epitopes were identified in pre-fusion F and an immunodominant epitope in the F-p27. In all age groups, antibody binding to pre-fusion F was 2–3 folds higher than to post-fusion form. For RSV-G, antibody responses were high following early RSV infection in children, but declined significantly in adults, using either G proteins or peptides. This study identified unlinked evolution of anti-F and anti G responses and supportive evidence for immune pressure driven evolution of RSV-G. These findings could help development of effective countermeasures including vaccines.
Study of Bin Migration in the Z Boson Rapidity Measurement at CMS

In the Z boson rapidity measurement [2], bin migration eects are currently added to the total measurement error. Unfolding is an alternative approach in which bin migration eects are reduced in order to lower the measurement uncertainties. Since our knowledge of the PDFs at LHC is limited, it is important for the unfolding process to be insensitive to variations in the assumed PDFs. We demonstrate that unfolding exhibits minimal PDF-dependence, and is a viable tool for reducing measurement uncertainties in the analysis [2].
Natural Language Generation as Incremental Planning Under Uncertainty: Adaptive Information Presentation for Statistical Dialogue Systems

We present and evaluate a novel approach to natural language generation (NLG) in statistical spoken dialogue systems (SDS) using a data-driven statistical optimization framework for incremental information presentation (IP), where there is a trade-off to be solved between presenting “enough" information to the user while keeping the utterances short and understandable. The trained IP model is adaptive to variation from the current generation context (e.g. a user and a non-deterministic sentence planner), and it incrementally adapts the IP policy at the turn level. Reinforcement learning is used to automatically optimize the IP policy with respect to a data-driven objective function. In a case study on presenting restaurant information, we show that an optimized IP strategy trained on Wizard-of-Oz data outperforms a baseline mimicking the wizard behavior in terms of total reward gained. The policy is then also tested with real users, and improves on a conventional hand-coded IP strategy used in a deployed SDS in terms of overall task success. The evaluation found that the trained IP strategy significantly improves dialogue task completion for real users, with up to a 8.2% increase in task success. This methodology also provides new insights into the nature of the IP problem, which has previously been treated as a module following dialogue management with no access to lower-level context features (e.g. from a surface realizer and/or speech synthesizer).
Model-Based Reflection for Agent Evolution

Adaptability is a key characteristic of intelligence. My research explores techniques for enabling software agents to adapt themselves as their functional requirements change incrementally. In the domain of manufacturing, for example, a software agent designed to assemble physical artifacts may be given a new goal of disassembling artifacts. As another example, in the internet domain, a software agent designed to browse some types of documents may be called upon to browse a document of another
Unusual Scene Detection Using Distributed Behaviour Model and Sparse Representation

The ability to detect unusual events in surviellance footage as they happen is a highly desireable feature for a surveillance system. However, this problem remains challenging in crowded scenes due to occlusions and the clustering of people. In this paper, we propose using the Distributed Behavior Model (DBM), which has been widely used in computer graphics, for video event detection. Our approach does not rely on object tracking, and is robust to camera movements. We use sparse coding for classification, and test our approach on various datasets. Our proposed approach outperforms a state-of-the-art work which uses the social force model and Latent Dirichlet Allocation.