Miracle-dz/UniFAR-FTUs huggingface dataset:
SN 2015bq: A Luminous Type Ia Supernova with Early Flux Excess

We present optical and ultraviolet (UV) observations of a luminous type Ia supernova (SN Ia) SN 2015bq characterized by early flux excess. This SN reaches a B-band absolute magnitude at M B = −19.68 ± 0.41 mag and a peak bolometric luminosity at L = (1.75 ± 0.37) × 1043 erg s−1, with a relatively small post-maximum decline rate [Δm 15(B) = 0.82 ± 0.05 mag]. The flux excess observed in the light curves of SN 2015bq a few days after the explosion, especially seen in the UV bands, might be due to the radioactive decay of 56Ni mixed into the surface. The radiation from the decay of the surface 56Ni heats the outer layer of this SN. It produces blue U − B color followed by monotonically reddening in the early phase, dominated iron-group lines, and weak intermediate-mass element absorption features in the early spectra. The scenario of enhanced 56Ni in the surface is consistent with a large amount of 56Ni ( M56Ni=0.97±0.20 M ☉) synthesized during the explosion. The properties of SN 2015bq are found to locate between SN 1991T and SN 1999aa, suggesting the latter two subclasses of SNe Ia may have a common origin.
A Literature Review of 3D Face Reconstruction From a Single Image

In people’s daily life, face is not only a physiological feature of human beings, but also contains abundant information. 2D face only has a single pose and illumination. While 3D face contains a large number of semantic information. And 3D face shape is widely used in many areas, such as medical care, medical cosmetology, entertainment and security. And it contributes to face alignment, face recognition and face editing. Hence, 3D face reconstruction is an important task in the field of coumputer vision and graphics. 3D face reconstruction now has two main directions. One is single image reconstruction ans the other is multi-image reconstruction. Because of the limits of 2D structure, Single image reconstruction is more challenging than multi-image reconstruction. However, single image reconstruction has more application in our life. Therefore, more and more researchers pay attention to this domain and various approaches have been proposed to tackle this problem. This work is intended as a brief survey of the recent literature on 3D face reconstruction from a single image research works. Articles have been choosen among 2016 and 2020, in order to provide the most up-to-date view of the single image 3D face reconstruction.
MRI Recovery with a Self-Calibrated Denoiser

Plug-and-play (PnP) methods that employ application-specific denoisers have been proposed to solve inverse problems, including MRI reconstruction. However, training application-specific denoisers is not feasible for many applications due to the lack of training data. In this work, we propose a PnP-inspired recovery method that does not require data beyond the single, incomplete set of measurements. The proposed self-supervised method, called recovery with a self-calibrated denoiser (ReSiDe), trains the denoiser from the patches of the image being recovered. The denoiser training and a call to the denoising subroutine are performed in each iteration of a PnP algorithm, leading to a progressive refinement of the reconstructed image. For validation, we compare ReSiDe with a compressed sensing-based method and a PnP method with BM3D denoising using single-coil MRI brain data.
Twisted photovoltaics at terahertz frequencies from momentum shift current

The bulk photovoltaic effect (BPVE) converts light into a coherent dc current at zero bias, through what is commonly known as the shift current. This current has previously been attributed to the displacement of the electronic wave function center in real space, when the sample is excited by light. We reveal that materials like twisted bilayer graphene (TBG) with a flatband dispersion are uniquely suited to maximize the BPVE because they lead to an enhanced shift in the momentum space, unlike any previously known shift current mechanism. We identify properties of quantum geometry, which go beyond the quantum geometric tensor, and are unrelated to Berry charges, as the physical origin of the large BPVE we observe in TBG. Our calculations show that TBG with a band gap of several meV exhibits a giant BPVE in a range of 0.2–1 THz, which represents the strongest BPVE reported so far at this frequency in a two-dimensional material and partially persists even a room temperature. Our paper provides a design principle for shift current generation, which applies to a broad range of twisted heterostructures with the potential to overcome the so-called “terahertz gap” in THz sensing.
Thermoelectric response in two-dimensional Dirac systems: Role of particle-hole pairs

Clean two-dimensional Dirac systems have received a lot of attention for being a prime candidate to observe hydrodynamical transport behavior in interacting electronic systems. This is mostly due to recent advances in the preparation of ultrapure samples with sufficiently strong interactions. In this paper, we investigate the role of collective modes in the thermo-electric transport properties of those systems. We find that dynamical particle-hole pairs, plasmons, make a sizeable contribution to the thermal conductivity. While the increase at the Dirac point is moderate, it becomes large towards larger doping. We suspect, that this is a generic feature of ultraclean two-dimensional electronic systems, also applicable to degenerate systems.
Simulated Chats for Building Dialog Systems: Learning to Generate Conversations from Instructions

Popular dialog datasets such as MultiWOZ are created by providing crowd workers an instruction, expressed in natural language, that describes the task to be accomplished. Crowd workers play the role of a user and an agent to generate dialogs to accomplish tasks involving booking restaurant tables, calling a taxi etc. In this paper, we present a data creation strategy that uses the pre-trained language model, GPT2, to simulate the interaction between crowd workers by creating a user bot and an agent bot. We train the simulators using a smaller percentage of actual crowd-generated conversations and their corresponding instructions. We demonstrate that by using the simulated data, we achieve significant improvements in low-resource settings on two publicly available datasets - the MultiWOZ dataset and the Persona chat dataset.
Mind the Gap: Domain Gap Control for Single Shot Domain Adaptation for Generative Adversarial Networks

We present a new method for one shot domain adaptation. The input to our method is trained GAN that can produce images in domain A and a single reference image I_B from domain B. The proposed algorithm can translate any output of the trained GAN from domain A to domain B. There are two main advantages of our method compared to the current state of the art: First, our solution achieves higher visual quality, e.g. by noticeably reducing overfitting. Second, our solution allows for more degrees of freedom to control the domain gap, i.e. what aspects of image I_B are used to define the domain B. Technically, we realize the new method by building on a pre-trained StyleGAN generator as GAN and a pre-trained CLIP model for representing the domain gap. We propose several new regularizers for controlling the domain gap to optimize the weights of the pre-trained StyleGAN generator to output images in domain B instead of domain A. The regularizers prevent the optimization from taking on too many attributes of the single reference image. Our results show significant visual improvements over the state of the art as well as multiple applications that highlight improved control.
KAM theory for active scalar equations

In this paper, we establish the existence of time quasi-periodic solutions to generalized surface quasi-geostrophic equation $({\rm gSQG})_\alpha$ in the patch form close to Rankine vortices. We show that invariant tori survive when the order $\alpha$ of the singular operator belongs to a Cantor set contained in $(0,\frac12)$ with almost full Lebesgue measure. The proof is based on several techniques from KAM theory, pseudo-differential calculus together with Nash-Moser scheme in the spirit of the recent works \cite{Baldi-Berti2018,Berti-Bolle15}. One key novelty here is a refined Egorov type theorem established through a new approach based on the kernel dynamics together with some hidden T\"opliz structures.
The Excluded Minors for Lattice Path Polymatroids

We find the excluded minors for the minor-closed class of lattice path polymatroids as a subclass of the minor-closed class of Boolean polymatroids. Like lattice path matroids and Boolean polymatroids, there are infinitely many excluded minors, but they fall into a small number of easily-described types.
Contextual Hate Speech Detection in Code Mixed Text using Transformer Based Approaches

In the recent past, social media platforms have helped people in connecting and communicating to a wider audience. But this has also led to a drastic increase in cyberbullying. It is essential to detect and curb hate speech to keep the sanity of social media platforms. Also, code mixed text containing more than one language is frequently used on these platforms. We, therefore, propose automated techniques for hate speech detection in code mixed text from scraped Twitter. We specifically focus on code mixed English-Hindi text and transformer-based approaches. While regular approaches analyze the text independently, we also make use of content text in the form of parent tweets. We try to evaluate the performances of multilingual BERT and Indic-BERT in single-encoder and dual-encoder settings. The first approach is to concatenate the target text and context text using a separator token and get a single representation from the BERT model. The second approach encodes the two texts independently using a dual BERT encoder and the corresponding representations are averaged. We show that the dual-encoder approach using independent representations yields better performance. We also employ simple ensemble methods to further improve the performance. Using these methods we report the best F1 score of 73.07% on the HASOC 2021 ICHCL code mixed data set.
Domain Generalisation for Apparent Emotional Facial Expression Recognition across Age-Groups

Apparent emotional facial expression recognition has attracted a lot of research attention recently. However, the majority of approaches ignore age differences and train a generic model for all ages. In this work, we study the effect of using different age-groups for training apparent emotional facial expression recognition models. To this end, we study Domain Generalisation in the context of apparent emotional facial expression recognition from facial imagery across different age groups. We first compare several domain generalisation algorithms on the basis of out-of-domain-generalisation, and observe that the Class-Conditional Domain-Adversarial Neural Networks (CDANN) algorithm has the best performance. We then study the effect of variety and number of age-groups used during training on generalisation to unseen age-groups and observe that an increase in the number of training age-groups tends to increase the apparent emotional facial expression recognition performance on unseen age-groups. We also show that exclusion of an age-group during training tends to affect more the performance of the neighbouring age groups.
SafeAccess+: An Intelligent System to make Smart Home Safer and Americans with Disability Act Compliant

Smart homes are becoming ubiquitous, but they are not Americans with Disability Act (ADA) compliant. Smart homes equipped with ADA compliant appliances and services are critical for people with disabilities (i.e., visual impairments and limited mobility) to improve independence, safety, and quality of life. Despite all advancements in smart home technologies, some fundamental design and implementation issues remain. For example, people with disabilities often feel insecure to respond when someone knocks on the door or rings the doorbell. In this paper, we present an intelligent system called"SafeAccess+"to build safer and ADA compliant premises (e.g. smart homes, offices). The key functionalities of the SafeAccess+ are: 1) Monitoring the inside/outside of premises and identifying incoming people; 2) Providing users relevant information to assess incoming threats (e.g., burglary, robbery) and ongoing crimes 3) Allowing users to grant safe access to homes for friends/family members. We have addressed several technical and research challenges: - developing models to detect and recognize person/activity, generating image descriptions, designing ADA compliant end-end system. In addition, we have designed a prototype smart door showcasing the proof-of-concept. The premises are expected to be equipped with cameras placed in strategic locations that facilitate monitoring the premise 24/7 to identify incoming persons and to generate image descriptions. The system generates a pre-structured message from the image description to assess incoming threats and immediately notify the users. The completeness and generalization of models have been ensured through a rigorous quantitative evaluation. The users' satisfaction and reliability of the system has been measured using PYTHEIA scale and was rated excellent (Internal Consistency-Cronbach's alpha is 0.784, Test-retest reliability is 0.939 )
Trajectory Optimization for Thermally-Actuated Soft Planar Robot Limbs

Practical use of robotic manipulators made from soft materials requires generating and executing complex motions. We present the first approach for generating trajectories of a thermally-actuated soft robotic manipulator. Based on simplified approximations of the soft arm and its antagonistic shape-memory alloy actuator coils, we justify a dynamics model of a discretized rigid manipulator with joint torques proportional to wire temperature. Then, we propose a method to calibrate this model from experimental data and demonstrate that the simulation aligns well with a hardware test. Finally, we use a direct collocation optimization with the robot's nonlinear dynamics to generate feasible state-input trajectories from a desired reference. Three experiments validate our approach for a single-segment robot in hardware: first using a hand-derived reference trajectory, then with two teach-and-repeat tests. The results show promise for both open-loop motion generation as well as for future applications with feedback.
Demographic Biases of Crowd Workers in Key Opinion Leaders Finding

Key Opinion Leaders (KOLs) are people that have a strong influence and their opinions are listened to by people when making important decisions. Crowdsourcing provides an efficient and cost-effective means to gather data for the KOL finding task. However, data collected through crowdsourcing is affected by the inherent demographic biases of crowd workers. To avoid such demographic biases, we need to measure how biased each crowd worker is. In this paper, we propose a simple yet effective approach based on demographic information of candidate KOLs and their counterfactual value. We argue that it is effectiveness because of the extra information that we can consider together with labeled data to curate a less biased dataset.
Training Dynamics for Text Summarization Models

Pre-trained language models (e.g. BART) have shown impressive results when fine-tuned on large summarization datasets. However, little is understood about this fine-tuning process, including what knowledge is retained from pre-training time or how content selection and generation strategies are learnt across iterations. In this work, we analyze the training dynamics for generation models, focusing on summarization. Across different datasets (CNN/DM, XSum, MediaSum) and summary properties, such as abstractiveness and hallucination, we study what the model learns at different stages of its fine-tuning process. We find that a propensity to copy the input is learned early in the training process consistently across all datasets studied. On the other hand, factual errors, such as hallucination of unsupported facts, are learnt in the later stages, though this behavior is more varied across domains. Based on these observations, we explore complementary approaches for modifying training: first, disregarding high-loss tokens that are challenging to learn and second, disregarding low-loss tokens that are learnt very quickly in the latter stages of the training process. We show that these simple training modifications allow us to configure our model to achieve different goals, such as improving factuality or improving abstractiveness.
Tackling Multi-Answer Open-Domain Questions via a Recall-then-Verify Framework

Open domain questions are likely to be open-ended and ambiguous, leading to multiple valid answers. Existing approaches typically adopt the rerank-then-read framework, where a reader reads top-ranking evidence to predict answers. According to our empirical analyses, this framework is faced with three problems: to leverage the power of a large reader, the reranker is forced to select only a few relevant passages that cover diverse answers, which is non-trivial due to unknown effect on the reader’s performance; the small reading bud-get also prevents the reader from making use of valuable retrieved evidence ﬁltered out by the reranker; besides, as the reader generates predictions all at once based on all selected evidence, it may learn pathological dependencies among answers, i.e., whether to predict an answer may also depend on evidence of the other answers. To avoid these problems, we propose to tackle multi-answer open-domain questions with a recall-then-verify framework, which separates the reasoning process of each answer so that we can make better use of retrieved evidence while also leveraging the power of large models under the same memory constraint. Our framework achieves new state-of-the-art results on two multi-answer datasets, and predicts signiﬁcantly more gold answers than a rerank-then-read system with an oracle reranker.
Inscribed Tverberg‐type partitions for orbit polytopes

Tverberg's theorem states that any set of t(r,d)=(r−1)(d+1)+1$t(r,d)=(r-1)(d+1)+1$ points in Rd$\mathbb {R}^d$ can be partitioned into r subsets whose convex hulls have non‐empty r‐fold intersection. Moreover, generic collections of fewer points cannot be so divided. Extending earlier work of the first author, we show that one can nonetheless guarantee inscribed “polytopal partitions” with specified symmetry conditions in many such circumstances. Namely, for any faithful and full‐dimensional orthogonal representation ρ:G→O(d)$\rho \colon G\rightarrow O(d)$ of any order r group G, we show that a generic set of t(r,d)−d$t(r,d)-d$ points in Rd$\mathbb {R}^d$ can be partitioned into r subsets so that there are r points, one from each of the resulting convex hulls, which are the vertices of a convex d‐polytope whose isometry group contains G via the regular action afforded by the representation. As with Tverberg's theorem, the number of points is optimal for this. At one extreme, this gives polytopal partitions for all regular r‐gons in the plane, as well as for three of the six regular 4‐polytopes in R4$\mathbb {R}^4$ . At the other extreme, one has polytopal partitions for d‐polytopes on r vertices with isometry group equal to G whenever G is the isometry group of a vertex‐transitive d‐polytope.
S-Cyc: A Learning Rate Schedule for Iterative Pruning of ReLU-based Networks

We explore a new perspective on adapting the learning rate (LR) schedule to improve the performance of the ReLU-based network as it is iteratively pruned. Our work and contribution consist of four parts: (i) We find that, as the ReLU-based network is iteratively pruned, the distribution of weight gradients tends to become narrower. This leads to the finding that as the network becomes more sparse, a larger value of LR should be used to train the pruned network. (ii) Motivated by this finding, we propose a novel LR schedule, called S-Cyclical (S-Cyc) which adapts the conventional cyclical LR schedule by gradually increasing the LR upper bound (max_lr) in an S-shape as the network is iteratively pruned.We highlight that S-Cyc is a method agnostic LR schedule that applies to many iterative pruning methods. (iii) We evaluate the performance of the proposed S-Cyc and compare it to four LR schedule benchmarks. Our experimental results on three state-of-the-art networks (e.g., VGG-19, ResNet-20, ResNet-50) and two popular datasets (e.g., CIFAR-10, ImageNet-200) demonstrate that S-Cyc consistently outperforms the best performing benchmark with an improvement of 2.1% - 3.4%, without substantial increase in complexity. (iv) We evaluate S-Cyc against an oracle and show that S-Cyc achieves comparable performance to the oracle, which carefully tunes max_lr via grid search.
Unsupervised Finetuning

This paper studies"unsupervised finetuning", the symmetrical problem of the well-known"supervised finetuning". Given a pretrained model and small-scale unlabeled target data, unsupervised finetuning is to adapt the representation pretrained from the source domain to the target domain so that better transfer performance can be obtained. This problem is more challenging than the supervised counterpart, as the low data density in the small-scale target data is not friendly for unsupervised learning, leading to the damage of the pretrained representation and poor representation in the target domain. In this paper, we find the source data is crucial when shifting the finetuning paradigm from supervise to unsupervise, and propose two simple and effective strategies to combine source and target data into unsupervised finetuning:"sparse source data replaying", and"data mixing". The motivation of the former strategy is to add a small portion of source data back to occupy their pretrained representation space and help push the target data to reside in a smaller compact space; and the motivation of the latter strategy is to increase the data density and help learn more compact representation. To demonstrate the effectiveness of our proposed ``unsupervised finetuning'' strategy, we conduct extensive experiments on multiple different target datasets, which show better transfer performance than the naive strategy.
Deep Transfer Learning & Beyond: Transformer Language Models in Information Systems Research

AI is widely thought to be poised to transform business, yet current perceptions of the scope of this transformation may be myopic. Recent progress in natural language processing involving transformer language models (TLMs) offers a potential avenue for AI-driven business and societal transformation that is beyond the scope of what most currently foresee. We review this recent progress as well as recent literature utilizing text mining in top IS journals to develop an outline for how future IS research can benefit from these new techniques. Our review of existing IS literature reveals that suboptimal text mining techniques are prevalent and that the more advanced TLMs could be applied to enhance and increase IS research involving text data, and to enable new IS research topics, thus creating more value for the research community. This is possible because these techniques make it easier to develop very powerful custom systems and their performance is superior to existing methods for a wide range of tasks and applications. Further, multilingual language models make possible higher quality text analytics for research in multiple languages. We also identify new avenues for IS research, like language user interfaces, that may offer even greater potential for future IS research.