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‘We Don’t See A Clear Exit Strategy.’ The Travel Industry Remains To Be Getting Crushed

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There are indicators that folks want to travel. Are even keen to pay extra for their vacations. Tui (TUIFF), the world’s largest tour operator, stated final week that bookings for summer 2021 are monitoring ahead of last 12 months, with strong demand for dearer deals. Up next: A string of earnings out Thursday from major trade players, including Air France-KLM (AFLYY), Norwegian Air, Airbus (EADSF), Hyatt Hotels (H) and Marriott International (MAR), will offer some insight into the road forward. But whether or not these journeys are literally taken is far from sure. The pandemic has dramatically altered how individuals store and traders can be hunting for clues that point to how lasting the changes wrought by the previous 12 months shall be, experiences my colleague Nathaniel Meyersohn. While delivering a mega boost to online retailers such as Amazon (AMZN), the crisis also lifted massive field chains resembling Walmart (WMT), Target (CBDY) and Costco (Cost), which remained open throughout the pandemic. Walmart stories earnings for its holiday quarter on Thursday, promising to offer market contributors with early indications of the financial health of US shoppers. Many smaller rivals and mall-based mostly retailers were forced to shut and have since folded or not yet bounced again. Pandemic winner: Walmart’s stock has rallied practically 25% over the previous 12 months, highlighting the company’s enviable standing in retail. The corporate has been building out its dwelling supply. Investors will even be conserving a close eye on the performance of Walmart’s digital gross sales. Online sales jumped 79% between August and October, in contrast with the same quarter final year. Curbside pickup options as more procuring strikes online. Investors will want to understand how many customers have signed up so far. September, a membership program to take on Amazon Prime. Will probably be trying to gauge how huge the program would possibly turn out to be.
Passenger volumes at the airport, as soon as one of the world’s busiest, collapsed 89% in January compared to the same month final 12 months. Demand in 2020 was about a quarter of the earlier 12 months’s degree. If severe travel restrictions persist, international passenger demand could get well to simply 38% of 2019 ranges this year, in line with the International Air Transport Association (IATA). IATA CEO Alexandre de Juniac said in an announcement this month, adding that airlines will want continued financial assist from governments to remain viable. Why it matters: It’s not just airlines at stake. Tourism to earn an income. These firms employed 330 million folks globally in 2019, in keeping with the World Travel and Tourism Council (WTTC). Thousands of firms depend on travel. WTTC CEO Gloria Guevara instructed me. The trade physique estimates more than half of these employees have been laid off or are presently on furlough. With little or no steering on when restrictions shall be lifted – some UK officials are asking people to not e book any holidays simply but – companies on this vital trade face an more and more unsure future.
Mungkin ramai yang tak pernah dengar bahawa ada zakat barang kemas wanita yang mereka pakai. Tetapi kalau barang kemas itu berupa barang perhiasan dan digunakan senantiasa di lengan atau leher para wanita, itu dikecualikan. Selama ini kita hanya mendengar kewajipan membayar zakat emas simpanan termasuklah segala barang kemas wanita yang tidak digunakan. Dalam satu kursus mengerjakan haji di salah satu kampung di Seberang Perai Tengah sana, pengendali kursus haji telah menjemput tiga orang wakil daripada pusat zakat Pulau Pinang untuk memberikan penerangan berkenaan zakat barang kemas wanita. Cerita ini saya dengar ketika balik kampung ke Kubang Semang baru-baru ini. Dalam majlis penerangan itu semua kaum ibu yang terdiri daripada makcik-makcik yang yang menjadikan pemakaian gelang dan rantai emas penuh di badan sebagai hobi terkejut besar apabila diperkenalkan pada kewajipan mengeluarkan zakat barang kemas yang sedang dipakai mereka selama ini. Mereka bertambah terkejut apabila jumlah wang yang perlu dikeluarkan bukannya sedikit, ada yang kena bayar RM500, ada yang RM800 dan ada juga yang lebih banyak dari itu setelah dinilai dan dikira oleh pegawai kutip zakat.
Kenapa mereka berusaha mengutip wang zakat barang kemas wanita sekeliling kampung pula secara tiba-tiba? Puas saya berfikir dan akhirnya mengambil sikap berbaik sangka. Mereka yang berurusan dengan zakat bukanlah orang-orang yang jahil, bahkan semuanya para ustaz yang dalil serta nas perpahat kemas dalam kepala masing-masing. Lagipun jika orang jahil macam saya menyuarakan sesuatu hukum itu tidak pernah didengari, bukan bermaksud hukum itu tak wujud, tapi lebih kepada disebabkan saya jahil. Lainlah jika seorang alim ulama menyuarakan tak pernah dengar sesuatu hukum atau fatwa, sudah tentu berkemungkinan besar hukum atau fatwa itu memang tak ada. Itulah bezanya antara si jahil dengan si alim. Selepas bertanyakan pada para ustaz dan kawan-kawan yang lebih alim, mereka juga berkata tak pernah dengar lagi hukum atau fatwa sedemikian. Maka sebagai si jahil saya diamkan diri dan mula bertanya mendapatkan maklumat. Lalu saya menuntut bantuan Google. Saya masih mencari dan tak puas hati. Walaupun ada ramai ustaz yang sering mengherdik pada orang ramai yang mendekati ilmu agama melalui pencarian maklumat di Google, saya rasa google merupakan salah satu sumber ilmu yang cukup common, mudah dan pantas di zaman ini.
Demi untuk menunaikan kewajipan dan takut berdosa, maka berebutlah para makcik menyegerakan pembayaran. Tetapi bukannya semua jenis gelabah sebegitu terus taat dan buat bayaran. Ada di kalangan makcik yang hadir bukannya jahil sangat, mereka dan bertahun-tahun mengikuti kuliah agama dari para ilmuan kampung seperti Ustaz Bakar dan juga Ustaz Mutalib. Berat sebab terlalu banyak gelang dan rantai emas. Tak pernah pula mereka dengar fatwa dari kedua-dua Ustaz tersebut bahawa mereka wajib bayar zakat gelang emas yang mereka pakai selama ini. Sah!!! UsTaz Bakar dan Ustaz Mutalib juga pasti tak ada zakat barang kemas yang dipakai. Untuk mendapatkan kepastian maka berpakatlah beberapa makcik yang berani ke depan menemui Ustaz rujukan mereka untuk mendapatkan penjelasan. Maka legalah para makcik yang cerdik itu terlepas dari kena keluarkan wang tambahan yang tak pernah mereka keluarkan selama ini dari muda sampai ke tua. Saya juga tak pernah dengar berkenaan fatwa tersebut dan merasa pelik dengan tindakatan para pegawai pusat zakat.
Nak kata ustaz googlepun katalah, saya perlukan maklumat pantas dan mudah untuk dicerna. Ternyata ada maklumat yang saya cari. Dari google saya dapat tahu ada dua kumpulan berbeza yang memberikan fatwa berkenaan zakat barang kemas wanita yang dipakai. Pendapat yang mengatakan tidak wajib zakat ke atas emas yang dipakai sebagai perhiasan terdiri daripada Abdullah bin Umar, Aisyah, Jabir bin Abdullah, Anas bin Malik, Asma binti Abu Bakr dan beberapa orang lagi. Mazhab Maliki, Hambali dan pendapat yang ketara daripada Syafie dan Ja’fariyyah juga mendokong pendapat kedua ni. Mazhab Hanafi, Zahiri dan Zaidiyah juga menyokong pendapat ini. Bersumberkan daripada Utusan yang memetik kenyataan Eksekutif Zakat PPZ-MAIWP, Ahmad Husni Abd. Ternyata andaian saya jahil yang tak tahu wujudnya sesuatu fatwa memang tepat sekali. Pandangan ini dikeluarkan oleh Umar bin al-Khattab, Abdullah bin Mas’ud, Abdullah bin Abbas, Abdullah bin Amr bin ‘Ash, Sa’id bin al-Musayyab, Sa’id bin Jabir, ‘Atha’, Mujahid dan lain-lain lagi. Dapatlah saya satu maklumat baru daripada apa yang berlaku ketika singgahan singkat ke kampung baru-baru ini. Ada caranya untuk mengira jumlah yang wajib dikeluarkan zakat pada barang kemas wanita yang dipakai. Semua ini menambahkan lagi sumber baru pada pegawai zakat bagi mengumpul timbunan dana zakat kerajaan. Entah apa pula zakat selepas ini akan berjaya dicungkil oleh para ustaz pengurus pusat zakat tak tahulah. Sama-samalah kita tunggu dan lihat.

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Detecting Climate, COVID, And Military Multimodal Misinformation

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In the primary stage we collected roughly 100,000 tweets each for COVID-19 and Climate Change matters. For example, a Twitter person may submit a tweet about working from home through the pandemic and tag the tweet with a COVID-associated hashtag. While one of these content is considerably related to COVID-19, we wanted to deal with data the place misinformation/disinformation may be more related, equivalent to more topical/newsworthy tweets (e.g. unhealthy actors might unfold propaganda associated to the COVID-19 pandemic by making false or deceptive claims). Inspection of the stage 1111 outcomes revealed a lot of off-topic tweets. This corresponds to what the SemaFor knowledge crew used to collect data for the analysis set. To that end, in stage 2222 we filtered by combining every subject phrase with one of the 19555The complete keyword record is included in Table sixteen within the Appendix. ”. The resulting knowledge appeared far more related than the preliminary collection effort.
From our hyperparameter sweeps we discover this setting to be essentially the most acceptable, as CLIP is pretrained while the classifier is randomly initialized. We multiply CLIP picture and text embeddings earlier than passing that as an input to the classifier. This is different from Luo et al. 2021), who used an easy feature concatenation. In the following, we go over the results from our approach and several ablations: completely different high quality-tuning schemes, multimodal fusion methods, share of arduous detrimental samples, skilled vs. For most ablations we optimize on a 500k subset of the coaching knowledge (until in any other case famous) for quicker improvement. Our proposed fusion method is simpler as demonstrated by a later ablation study. We report the next metrics. Most tables report binary classification accuracy with the threshold 0.5. That is complemented with ROC curves, which provide a more full view of performance throughout a number of thresholds. Since a few of our evaluation units (Eval 1,2) have an unequal variety of pristine and falsified samples, for these we report the balanced binary classification accuracy, the place we common the true positive and false constructive rates.
In this work we face a selected real-world problem: flag picture-text pairs as misinformative with no corresponding coaching data. To strategy this problem, we first acquire Twitter-COMMs, a large-scale topical dataset with multimodal tweets, and construct random and hard negatives on top of it. We present that with this strategy we are able to considerably enhance over a strong baseline, an off-the-shelf CLIP model, and achieve the top end result on a challenge with in-the-wild (unseen) textual content-picture inconsistencies. We wish to thank PAR Tech, Syracuse University, and the University of Canada, for creating the evaluation information. We thank the SRI team, including John Cadigan and Martin Graciarena, for providing the WikiData-sourced news group Twitter handles. We then design our approach based mostly on the recent CLIP mannequin, making a number of necessary design selections, resembling multiplying the picture and text embeddings for multimodal fusion and growing the percentage of hard negatives in our coaching information. We’d also wish to thank Dong Huk (Seth) Park, Sanjay Subramanian, and Reuben Tan for helpful discussions on finetuning CLIP. This work was supported partly by DoD including DARPA’s LwLL, and/or SemaFor packages, and Berkeley Artificial Intelligence Research (BAIR) industrial alliance applications.
We note that amassing “real” out-of-context misinformation at scale is very challenging. All three evaluation units contain a mixture of samples related to the subjects of COVID-19, Climate Change and Military Vehicles. Table three provides the variety of samples in every set. 2021), a big pretrained multimodal mannequin that maps photographs and text into a joint embedding area through contrastive studying. We use the RN50x16 spine. We discover that this spine constantly yields a 2-3% improvement compared to other released backbones, akin to ViT/B-32. For our strategy we superb-tune CLIP Radford et al. We tune the upper layers and keep CLIP’s lower layers frozen888We positive-tune the layers “visual.layer4”, “visual.attnpool”, “transformer.resblocks. ”.. We find that this scheme is extra memory efficient. We discover that this scheme is extra reminiscence efficient. Yields more stable convergence than tuning all of the layers.. Yields more stable convergence than tuning all of the layers. We use a studying price of 5e-08 for CLIP and 5e-05 for the classifier.
We create random negatives (denoted as “Random”) by retrieving a picture for a given caption at random. We also create arduous negatives (denoted as “Hard”) following the strategy from Luo et al. 2021). Specifically, we use the matching technique from their “Semantics / CLIP Text-Text” cut up where given a question caption, we retrieve the image of the pattern with the greatest textual similarity. We mainly generate mismatches within every subject (COVID-19, Climate Change, Military Vehicles), aside from a small set of random mismatches throughout matters (denoted as “Cross Topic”). Our dataset is balanced with respect to labels, the place half of the samples are pristine and half are falsified, i.e., each falsified sample has an associated pristine pattern. We element our improvement set. Table 2 presents summary statistics for the falsified coaching samples. Other information used for evaluation in the subsequent part. In this section we talk about the information used for analysis, current our strategy and supply an ablation research for our varied design selections, and eventually, report the results on the Image-Text Inconsistency Detection problem of the DARPA Semantic Forensics (SemaFor) Program.

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